The importance of mutations in disease phenotype has been studied, with information available in databases such as OMIM. However, it remains a research challenge for the possibility of clustering amino acid residues based on an underlying interaction, such as co-evolution, to understand how mutations in these related sites can lead to different disease phenotypes.This paper presents an integrative approach to identify groups of co-evolving residues, known as protein sectors. By studying a protein family using multiple sequence alignments and statistical coupling analysis, we attempted to determine if it is possible that these groups of residues could be related to disease phenotypes. After the protein sectors were identified, disease-associated residues within these groups of amino acids were mapped to a structure representing the protein family. In this study, we used the proposed pipeline to analyze two test cases of spermine synthase and Rab GDP dissociation inhibitor.The results suggest that there is a possible link between certain groups of co-evolving residues and different disease phenotypes. The pipeline described in this work could also be used to study other protein families associated with human diseases. The role of mutations in disease phenotypes is an important focus for human genetics -3. The iThe techniques such as statistical coupling analysis (SCA) and direct coupling analysis (DCA) amongst others -7,10 havThe statistical coupling analysis requires the use of a large number of sequences as well as structural models for a representative member of the protein family to be analyzed ,7. AlthoPrevious studies have focused on identifying co-evolving residues, and therefore understanding how these correlated amino acid units, and the mutations within these regions, could affect the protein. The most common approaches consist of biochemical assays that provide experimental support to the link between correlated residue units and measureable characteristics such as stability or catalytic activity ,11. HoweAlthough the study of specific regions provides valuable information to further understand how changes in amino acids can affect the overall function of the protein, the exclusion of residues outside the defined region could however lead to a possible loss of valuable information regarding residue distribution in functional sectors. This is of special importance in human genetic disorders, where diseases have multiple phenotypes ranging in severity, which can be associated with the location of a mutation within the protein.We propose an integrative approach that consists of analyzing full-length sequence alignments from proteins and the subsequent identification of protein sectors using statistical coupling analysis . The ideAs shown in Figure The first step was to obtain a reference protein sequence. Selection of the sequence and its organism is determined based on the type of dataset to be analyzed. The retrieval of homologous sequences belonging to the protein family to be analyzed was done using one iteration of PSI-BLAST with a maximum number of target sequences set to 1000. This allowed us to obtain multiple sequences representing diverse evolutionary lineages. The motivation behind this was to enrich sequence diversity, thus allowing for the possible identification of groups of conserved co-evolving residues. Another reason to attempt to identify various sequences was to diminish the possible bias towards an organism that has overrepresentation of entries in the database. The approach of protein sector analysis may not be applied to disease genes with few homologous sequences in the database. However, this is not supposed to be a common situation with more and more genomes being sequenced.BlastClust  was subsProtein structure files for the reference sequences used in the test cases were obtained from the Protein Data Bank . The entAn alternative for proteins without a 3D structure is the use of homology modeling. We generated such homology structure files for our test sets by using the freely available software such as CPHmodels 3.2  or the SEach of the working datasets was subsequently aligned using ClustalW  incorporThe statistical coupling analysis was undertaken to identify the groups of coevolving residues. The SCA toolbox for MATLAB was obtained from the Rama Ranganathan laboratory at the Green Center for Systems Biology, UT Southwestern Medical Center. The MATLAB script was modified and adapted to analyze the test cases.The identification of protein sectors was accomplished using the methodology previously described ,11. ThisThe information associated with mutations in a protein family can be obtained from databases such as OMIM as well as publications related to the protein family, their clinical importance and reported phenotypes known to be associated with the mutations. The clustering or grouping of mutations can be based on their location, within one of the major regions: N-terminal, central/other, and C-terminal. This allows for the preliminary classification of the mutations as well as the latter characterization of the residues identified within the protein sectors.The labeling and numbering of residues and their corresponding sectors was done using a PDB file obtained by BLAST search using the reference sequence for each family as a query. The full-length sequence was used to maximize the number of possible residues belonging to protein sectors. By increasing the length and coverage of the alignment, it is possible to identify co-evolving residues outside the main protein domain, which could be associated with a disease phenotype.http://jmol.sourceforge.net/ was used to visualize the 3D structures in PDB files. Protein domains were subsequently identified and colored using the information available from PDB. Each domain was assigned an arbitrary color, based on visualization purposes . For the visualization of protein sectors, the residues identified were assigned the color that corresponded to the group of residues to which they belonged based on the SCA analysis.The program Jmol 3C6K) made SMS a good candidate as a test case.The selection of spermine synthase (SMS) and the Rab GDP dissociation inhibitor 1 (GDI1) as test cases was based on the available information for both proteins. SMS has been widely studied, with multiple mutations and the associated phenotypes comprehensively described as well as a vast amount of information on the importance of this molecule in brain development -25. ThesGDI1 has been associated with non-specific X-linked intellectual disability, with different mutations being reported ,27. Thisab initio modeling. Since the analysis of residue co-evolution is based on the multiple sequence alignment, the accuracy of homology modeling does not represent an issue to the approach. The structure model is only used to map the positions of the multiple sequence alignment to the primary structure of the protein. Thus, the modeled structure file can be used in the pipeline in the same manner as the files obtained from the Protein Data Bank.The test cases of SMS and GDI1 were analyzed using full-length sequences and their corresponding structures. The results reported in this paper were obtained using a personal computer  installed with the MATLAB 2012a and SCA packages. The analysis covered 90% of the residues represented in the crystal structures of SMS  and GDI1The protein sector analysis of both SMS and GDI1 revealed the distribution of groups of co-evolving residues Figure  and 2b tFor GDI1, our analysis identified three distinct sectors, red, blue and green. The FAD/NAD(P) binding domain, located in the N-terminal region of GDI1, had amino acid residues classified within the blue and green sector Figure , with onOur results indicate that although protein sectors may be distributed across the proteins, there is a concentration of these co-evolving residues in certain areas of the proteins Figure  and 2b, The protein sector analysis of our test cases revealed a possible link between different disease phenotypes and the locations of the mutations within groups of co-evolving residues. We were able to identify multiple residues, in which disease-causing mutations have been identified Table . As statThe results suggest that protein sectors, when compared to domains, allow for the clustering of mutations based on the underlying process of residue co-evolution instead of grouping them by sequential or spatial locations. The protein sector analysis of SMS has shown that the known Snyder-Robinson syndrome phenotypes, including expanded phenotypes , are maiThe analysis of GDI1 identified two known mutations located in the green and blue sectors Table . It is sOur results from the protein sector analysis of SMS suggest a possible link between groups of co-evolving residues and disease severity. However, to fully understand the role of protein sectors, the use of mutagenic analysis can provide further information. Experimental validation of our results would present further evidence to support the approach of clustering disease-associated residues with their corresponding clinical manifestation. In addition, although we have presented a pipeline useful for clustering mutations, it is possible that other factors, such as the type of amino acid substitutions, could be the underlying cause of the differences in disease severity. Nevertheless, by clustering residues into different sectors, our approach can be used as a valuable aid in further characterizing the possible causes of disease such as intellectual disability. Protein sector analysis may also provide useful information for understanding the effect of mutations at spatially distant positions on disease phenotypes.We have proposed an integrative approach that makes use of the statistical coupling analysis method for the study of disease-causing mutations in proteins. Our test cases provided information regarding the role of protein sectors and how they could be associated to disease variants. We were also able to identify the effect of mutations in distinct sectors with variations in the clinical signs of a disease. These findings support the possible role of the protein sectors in specific functions that when affected could lead to variable phenotypes associated with a complex syndrome.The authors declare that they have no competing interests.LW conceived and oversaw the study. JG designed the study, conducted the data analysis, and drafted the manuscript. LW and CES contributed to result interpretation and manuscript preparation.


Osteoarthritis (OA) is a heterogeneous group of conditions with disturbed integrity of articular cartilage and changes in the underlying bone. The pathogenesis of OA is multifactorial and not just a disease of older people. Hydroxychloroquine (HCQ) is a disease-modifying anti-rheumatic drug (DMARD) typically used for the treatment of various rheumatic and dermatologic diseases. Three studies of HCQ in OA, including one abstract and one letter, are available and use a wide variety of outcome measures in small patient populations. Despite initial evidence for good efficacy of HCQ, there has been no randomized, double-blind, and placebo-controlled trial in a larger patient group. In the European League Against Rheumatism (EULAR), evidence-based recommendations for the management of hand OA, HCQ was not included as a therapeutic option because of the current lack of randomized clinical trials.OA TREAT is an investigator-initiated, multicenter, randomized, double-blind, placebo-controlled trial. A total of 510 subjects with inflammatory and erosive hand OA, according to the classification criteria of the American College of Rheumatology (ACR), with recent X-ray will be recruited across outpatient sites, hospitals and universities in Germany. Patients are randomized 1:1 to active treatment (HCQ 200 to 400 mg per day) or placebo for 52 weeks. Both groups receive standard therapy  for OA treatment, taken steadily two weeks before enrollment and continued further afterwards. If disease activity increases, the dose of NSAID/coxibs can be increased according to the drug recommendation. The co-primary clinical endpoints are the changes in Australian-Canadian OA Index  dimensions for pain and hand disability at week 52. The co-primary radiographic endpoint is the radiographic progression from baseline to week 52. A multiple endpoint test and analysis of covariance will be used to compare changes between groups. All analyses will be conducted on an intention-to-treat basis.The OA TREAT trial will examine the clinical and radiological efficacy and safety of HCQ as a treatment option for inflammatory and erosive OA over 12 months. OA TREAT focuses on erosive hand OA in contrast to other current studies on symptomatic hand OA, for example, HERO .ISRCTN46445413, date of registration: 05-10-2011. Osteoarthritis (OA) is a heterogeneous group of conditions with defective integrity of articular cartilage and changes in the underlying bone. The pathogenesis is multifactorial and involves a complex interplay of genetic, metabolic, biochemical, and biomechanical factors with variable components of inflammation. Subsets of patients with OA develop an inflammatory and erosive form of the disease. As one of the most prevalent musculoskeletal diseases, the condition leads to pain in and around the affected joints and to swelling, stiffness, deformity, and gradual loss of function , 2. ThisIn OA and hand OA, symptomatic therapy with NSAIDs or coxibs is the most frequently administered treatment. Regarding tumor necrosis factor (TNF)-α-antagonists, one pilot study with adalimumab (ADA) in patients with erosive/inflammatory OA (EOA) of the hands demonstrated that ADA was well tolerated; however, treatment with ADA for three months did not significantly improve signs and symptoms of EOA, and most patients did not achieve an ACR20 . Three sIn vitro studies have shown that HCQ decreases the production of TNF-α, IL-6 and IFN-α by mitogen-stimulated peripheral blood lymphocytes ) and is carried out with German rheumatologic and statistical stakeholders with experience of treating hand OA. OA TREAT is a multicenter, double-blind, placebo-controlled phase III trial with a parallel group design.http://insider.charite.de/projekte/aktuelle_projekte/oa_treat/study_centers/).Recruitment aims are based on the design of the study as a national multicenter study and on the established cooperation with primary care physicians within the Regional Collaborative Arthritis Centers . All selected centers are very experienced in trial performance and approved by the local ethic committees (EC) in their quality management as a clinical trial center. Our partners are listed on our website for study (Patients with hand OA according to the classification criteria of the American College of Rheumatology (ACR) with recent X-ray of the hands , dating Participants must meet the inclusion and exclusion criteria in order to participate. These will be assessed at the screening visit. The key inclusion and exclusion criteria are listed in Table A total of 510 patients with erosive and inflammatory hand OA will be recruited and randomly allocated to either the treatment or placebo group. Recruitment methods will include advertisements through the local media and community groups, invitations to previous study participants who have given their consent to be contacted regarding future research projects and liaisons with general practitioners, rheumatologists, orthopedists, and German patients associations .Both groups receive standard therapy  for OA treatment, taken steadily two weeks before enrollment and continued afterwards Figure . If diseThe time schedule of enrollment, interventions, trial assessments, and visits for participants is described in Table OA TREAT examines a number of clinical, radiographic and patient-reported outcomes. These are listed in Table Our sample size considerations are based on the EULAR recommendations for the planning of RCTs in patients with hand OA . We consRandomization will be undertaken during normal working hours (Monday to Friday from 9:00 to 17:00) by the German Rheumatologic Research Center  of participating centers upon receipt of a randomization request form and after blinded review of X-rays of both hands.Identical HCQ and placebo capsules will be produced to ensure allocation concealment. Upon production, study medication will be packed into numbered bottles according to a randomization schedule. This will be prepared by the DRFZ using a computerized random number generator, thereby guaranteeing full allocation concealment. Trial medication will be issued in numerical order by the pharmacy of the Charité - Universitätsmedizin Berlin. Participants in each site will be randomly assigned to the intervention arm or the placebo arm in a ratio of 1:1 and the randomization will be double-blind.As this is a double-blind study, all parties involved including the investigator, study coordinator, and subject will remain blinded to each subject’s treatment throughout the course of the study. The blind will be maintained at all times until all data has been collected and the study database locked. Participants will be randomized centrally by computer generated random numbers stratified by center. Patients and investigators are blind to the treatment allocation. A sealed envelope containing the blinded information will be provided to the investigator and retained by him/her in a locked or secured area. This envelope may be opened in the case of emergency. Unopened envelopes will be returned to the sponsor at the end of the study. Use of NSAIDs and rescue medication (acetaminophen) will be assessed carefully throughout the trial. The possible influence of these drugs on the outcome will be discussed. Radiographs will be read in a concealed time order by two readers blinded to treatment assignment.2 test [et al. for comparing different drugs and different dosages will be considered [All patients who received at least one dose of the study medication will be included in the statistical analysis of the efficacy and safety of HCQ (modified intention-to-treat (mITT) population). To control the familywise error rate of 5%, a closed test procedure was applied . In the 2 test , 32. In nsidered . In the All subject data obtained during the study will be recorded using pseudonyms. Every patient will be clearly identified by a registration number and a pseudonym, which will be assigned during the registration process. The investigator keeps a confidential patient list, in which the patient number and pseudonym are connected to the complete patient’s name. Access to this list is only provided to the local study personal and the study monitor. The original source files and medical records directly related to the study can be accessed by study monitors, auditors or inspectors from regulatory authorities. All data collection of pseudonomized data will be done on standardized case report forms (CRFs), which will be completed by site staff, verified by the principal investigator, and returned to the clinical trials unit for double data entry. All data will be recorded electronically at the sponsor’s site by independent data managers. The sponsor will maintain a list of personnel authorized to enter data into the database. Validation of correct data entry will be ensured by the named data managers to check for discrepancies and to ensure consistency of the data. Validated data will be entered into an electronic trial database. Any addition, change or correction to the entered data must be approved by the investigator and will be recorded. Records of correction will be kept together with the case report form. Regular backups of the electronic data will be performed. When all data have been recorded and validated after termination of the study the database will be closed. This process will be recorded.The sponsor maintains a list of personnel authorized to access the data.http://www.tmf-ev.de) is utilized for monitoring. The trial monitoring requires a high degree of professional expertise of the monitor. We have a variety of very specific outcome criteria, which are new to the involved trial sites. The focus of monitoring will be on developing, checking and adjusting the trial procedures, and on providing training, mentoring and support to study staff in response to the issues identified. In addition to the on-site monitoring, the following activities are planned: central monitoring of fax data and data management, training of and information on trial staff, and specific support of trial sites (information and download website) and investigators (investigator meetings). Independent audits will also take place in the course of study. The monitoring will be performed for 33% of source data verification, 100% of inclusion and exclusion criteria and 100% of primary endpoint criteria. Pre-study visits prior to the start of the study were carried out for 6 months in advance. A screening visit and six monitor visits per trial site are planned.The monitor has the responsibility to familiarize the investigator(s) and the entire center staff involved in the study with all study procedures including the administration of the study drug. The sponsor must provide a trained monitor to assist the investigator(s) in conducting the clinical study. The monitor must visit the clinical study center on a regular basis and at least once before the first subject has been enrolled, once during the course of the study, and finally at study completion. The monitor has the responsibility of reviewing the ongoing study with the investigator(s) to verify adherence to the protocol and to deal with any problems that arise. At all times the sponsor must maintain the confidentiality of the study documents. It is the responsibility of the study monitor to verify the study documents against the subject’s original medical records. The investigator (or his/her deputy) agrees to cooperate with the monitor to ensure that any problems detected in the course of these monitoring visits are resolved. The harmonized SOP ‘Monitoring’ of the trial master file (TMF) (The Data Monitoring and Safety Board (DMSB) will be independent of both the investigators and the sponsor. The DSMB will review the blinded safety data, including adverse events reports. The DSMB will be empowered to request the unblinding of the subject’s treatment assignment or to recommend to the study team that this subject should be withdrawn from the study - regardless of the treatment assignment - if an emerging safety signal is suggested. The constitution, governing principles, and mandate of the DSMB are explained in the DSMB charter. The main tasks of the DSMB are to review relevant information about the trial; to ensure adherence to protocol; to advise whether to continue, modify or stop a trial; and to provide the funding organizations with information and advice.An unexpected adverse reaction (UAR) is one that is not listed in the Summary of Product Characteristics or in the Investigator Brochure (SmPC or IB). A UAR includes any event that may be symptomatically and pathophysiologically related to an event listed in the labelling, but differs from the labelled event because of greater severity or specificity.Suspected unexpected serious adverse reactions (SUSARs) are serious events that are not listed in the IB or SmPC and that the investigator identifies as related to the investigational product or procedure.A SUSAR is defined by the following criteria: Description and severity of the SAE is not listed in the product labeling ) and isLife-threatening condition or death.Initial inpatient hospitalization or prolongation of hospitalization required.Significant or persistent or disability or incapacity.Congenital anomaly/birth defect (including that occurring in a fetus).AE (GCP-V § 3(7)). serious:Grade 1 - Mild: causing no limitations of usual activitiesGrade 2 - Moderate: causing some limitations of usual activitiesGrade 3 - Severe: causing inability to carry out usual activities.Grade 4 - Life-threatening: potentially life-threatening or disabling; urgent medical intervention requiredFor both serious and non-serious AEs, the investigator must determine both the intensity of the event and the relationship of the event to study drug administration. Intensity for each adverse event will be determined by using the Common Terminology Criteria for Adverse Events  of the National Cancer Institute of the USA as a guideline, wherever possible. In those cases where the CTCAE criteria do not apply, the intensity of an adverse event will be defined according to the following criteria:In addition, for each AE, the investigator must assess whether the criteria for an SAE are fulfilled.Any severe adverse event (SAE) and any adverse event (AE) must be documented, whether it may or may not be considered related to the study drug or the study procedures. The documentation includes the nature of the event, the time point of occurrence, the duration, the severity and the causal relationship of the event.When recording an AE, the investigator should use the overall diagnosis or syndrome using standard medical terminology, rather than recording individual symptoms, signs or changes in laboratory parameters. Laboratory parameters out of normal range must be assessed by the investigator with regard to the clinical relevance, and in case of clinical relevance, must also be reported as an AE. Any SAE and SUSAR, whether deemed treatment-related or not, must be reported by telephone and fax to the sponsor within 24 hours after the investigator or coordinator has become aware of its occurrence. The sponsor will ensure that all relevant information about SAEs and SUSARs that are fatal or life-threatening is recorded and reported as soon as possible to the national authorities  and to the responsible EC.All other SAEs and SUSARs will be reported to the BfArM and to the EC as soon as possible but within a maximum of 15 days of first knowledge by the sponsor. The sponsor will also inform all investigators.Once a year throughout the clinical trial, the sponsor will provide the national authorities BfArM and the EC with a listing of all SAEs and SUSARs that have occurred over this period and a report of the subjects’ safety.The treatment is generally considered as low risk, and most side effects are not dangerous. The drug has been used successfully for several years in rheumatology and for malaria prophylaxis.Patients report frequent side effects such as loss of appetite, flatulence, vomiting, diarrhea, headache, nausea, weight loss or unusual tiredness. These symptoms occur mainly during the first weeks of treatment.Uncommon side effects include dizziness, blurred vision, emotional lability, corneal clouding, hearing impairment, hearing loss, drowsiness, numbness, sleep disturbances, dizziness, tinnitus, confusion, agitation, headache, paresthesia, decreased muscle strength, weakening of the tendon reflexes, changes in sensory perception, ECG changes, conduction disorders, heart disease, and nerve-muscle diseases.Among the rare side effects are retina problems with visual field defects, impaired color vision, blurred vision, vision loss, changes in skin color, mucous membrane discoloration, bleached hair, gray hair, alopecia, pruritus, photosensitivity, liver problems or failure, beginning of psoriasis, the beginning of porphyria or exacerbation of myasthenia gravis. The very rare side effects include skin rashes, seizures, and methemoglobinemia.Regular collection and reporting of all adverse events.Medical control of the eyes at baseline and every 6 months.etcetera after becoming aware of the sponsor and follow-up reports within 7 days.Immediate notification of serious adverse event (SAE), SUSAR, suspected expected serious adverse reaction (SESAR), Regular meetings of the DSMB and decisions regarding the continuation of the study based on the AE reports.Immediate treatments stop after decision of the investigator.Regular monitoring of laboratory parameters of therapeutic safety.Eye diseases with visual field defects, retinopathy, myasthenia gravis, bone marrow suppression, glucose-6-Ph-dehydrogenase deficiency, known allergy to the substance, and breastfeeding are contraindicated. Only under strict indications should HCQ be used when there are concerns about hepatic and renal function, porphyria, psoriasis, seizure disorders, or with concurrent use of monoamine oxidases (MAO) inhibitors in pregnancy. The following measures of therapeutic safety monitoring are provided in this trial:Full ethical approval was granted by the independent EC of Berlin  and by the BfArM. Full approval also was given by the Medicines and Healthcare Products Regulatory Agency (EudraCT 2011-001689-16).The study will be announced to the LAGeSo Berlin. The sponsor and all participating investigators will be designated by their full name and position.All protocol modifications must be prepared by a representative of the sponsor and initially reviewed and approved by the Trial Core Team  and Monitoring Boards. All protocol modifications must be submitted to the appropriate Independent EC of Berlin. The sponsor informs the BfArM and BMBF about all protocol modifications.Approval must be awaited before any changes can be implemented, except for changes necessary to eliminate an immediate hazard to trial patients, or when the change(s) involves only logistical or administrative aspects of the trial , change of telephone number(s).All participating patients are covered for the occurrence of study-related health damage. According to the German Drug Law, the sponsor of the study covers patient insurance.Before the start of the study, the patients will be educated in detail - both verbally and in writing - about the study and the associated risks. Patients will have a decision time of at least 24 hours. It is the responsibility of the investigator, or a person designated by the investigator , to obtain written informed consent from each patient participating in this study after adequate explanation of the aim, methods, anticipated benefits, and potential hazards of the study. For patients not qualified or incapable of giving legal consent, written consent must be obtained from the legally acceptable representative. In the case where both the patient and his/her legally acceptable representative are unable to read, an impartial witness should be present during the entire informed consent discussion. After the patient and representative have verbally consented to participation in the trial, the witness’ signature on the form will attest that the information in the consent form was accurately explained and understood. The investigator or designee must also explain that the patients are completely free to refuse to enter the study or to withdraw from it at any time and for any reason. The case report form (CRF) for this study contains a section for documenting informed consent, and this must be completed appropriately. If new safety information results in significant changes in the risk/benefit assessment, the consent form should be reviewed and updated if necessary. All patients  should be informed of the new information, given a copy of the revised form and give their consent to continue in the study.Intercurrent diseases.Ophthalmologic changes associated with HCQ.Occurrence of any grade 3 to 4 WHO toxicity or an unacceptable adverse event.>2 weeks because of toxicity.A treatment delay or interruption of Patient request.Noncompliance.General or specific changes in the patient’s condition unacceptable for further treatment in the judgment of the investigator.Protocol violations.Medical and ethical discretion of principal investigator.Failure to return for follow-up.Patients will be informed that they have the right to withdraw from the study at any time for any reason, without prejudice to their medical care. The investigator also has the right to withdraw patients from the study for any of the following reasons :Administrative reasons.Incidence of severe adverse events or fatal adverse events. Stopping rules are decided jointly by the data monitoring and safety board.The whole trial may be stopped for the following:To avoid simultaneous inclusion in several studies, patients who had an experimental therapy or participated in clinical study with investigational medicinal products in the 3 months previous to the start of the treatment are excluded from this study. After a time interval of 3 months, inclusion of these patients is possible.Before the trial started, all local trial investigators gave written statements about conflicts of private, economical or financial interests with regard to the above-mentioned clinical trial and the investigational drugs that will be used.The investigator will ensure that this study is conducted in full conformance with the principles of the ‘Declaration of Helsinki’ or with the laws of the country in which the research is conducted, whichever affords greater protection to the individual . The study must fully adhere to the principles outlined in ‘Guideline for Good Clinical Practice’ ICH Tripartite Guideline (January 1997) or to local law if it affords greater protection to the patient.http://www.dgrh.de), which have been developed by the working group of the German Society of Rheumatology (DGRh). The results of this study will be presented at scientific meetings and published in peer-reviewed scientific or medical journals. The study will be registered with the European Science Foundation as part of an effort to achieve European-wide registration of all randomized controlled clinical trials; it is possible that the results will spark international discussions in scientific circles to allow for the possibility of extrapolating the findings to other conditions. The cooperation with the Competence Network Rheumatology, the DRFZ and the various national trial centers enables us to use the results and experience in the field of other therapy options in the treatment of OA. Also, other medical disciplines, in which the disease is often treated, can benefit from these results. For this reason, the dissemination of the results in general medical journals and interdisciplinary conferences is planned.The results of the OA Treat study will consecutively improve the quality of rheumatologic treatment in Germany. The results will be analyzed and published independently of the pharmaceutical industry. Another important aspect is the implementation of the results of this study into the guidelines ‘Quality Assurance in Rheumatic Diseases’ and the ‘German Recommendations for Physicians - GC’  surgical intervention, with NSAIDs or coxibs being the most frequently administered treatment. Unfortunately, their symptom-relieving effect is often too small, and adverse effects can be substantial. There is, therefore, huge unmet clinical need for effective and safe treatment options.There are currently few therapeutic options to treat hand OA. Often NSAIDs are prescribed. In long-term use, however, these effective drugs have a very high risk of gastrointestinal bleeding and impairment of the renal function. Coxibs are alternatively available, but cardiovascular side effects have been reported for both NSAIDs and coxibs. Although clinical trials showed a trend for a good efficacy of HCQ in hand OA, the development of the drug was not pursued further. A major reason was the lack of interest from the pharmaceutical industry. In the treatment of RA and SLE, HCQ is a very effective drug and its side effect profile has been known for many years. Provided that all safety measures are carried out, this drug is very well tolerated and also safe in continuous treatment. Hand OA is not a life-threatening illness, but leads to a severely impaired quality of life due to pain. In addition to the definition of termination criteria, safety-related parameters are regularly investigated and questioned  during the study. Before the start of the study, the patients will be educated in detail about the associated risks. Patients will receive a decision time of at least 24 hours before giving written consent. Furthermore, the patient may withdraw his consent at any time during the study. The project will follow all regulations and legal requirements for data protection, AMG/GCP, and the Declaration of Helsinki.The co-primary clinical endpoints in this study are the changes in AUSCAN dimensions for pain and hand disability at week 52. The AUSCAN  is a patient-centered self-administered questionnaire developed specifically for hand OA studies . It consThe following potential limitations of the study should be noted: generalizability of the results may be limited by specific inclusion and exclusion criteria, and results may not apply to the entire target population. However, selection criteria were kept as broad as possible to ensure maximal generalizability. For ethical reasons, changes in the NSAID dosages including new treatment starts or treatment terminations are allowed. Since an imbalance in the use of co-medications might have an influence on the outcome in the clinical parameters, the use of NSAID/analgesics is recorded at each study visit and will be analyzed as a secondary outcome. In case there is any imbalance, we anticipate an increased use of co-medication in the placebo group, which could weaken the differences between both treatment arms especially regarding the AUSCAN scales for pain and hand function. However, even if this increased use of NSAIDs/analgesics would reduce the assumed effect sizes for both scales by 25%, the power of the multiple endpoint test comparing the first primary outcome will be greater than 80%. In the unlikely case of an increased use of NSAIDs and/or analgesics in the verum arm, this observation will be considered in the interpretation of the findings to discuss the possibility of a false positive result.HCQ has been used since the 1950s for the treatment of various rheumatic and dermatologic diseases. It is also used for inflammatory erosive OA in clinical practice, but there are no high-quality clinical trials to support its use in hand OA. Three pilot studies of HCQ in EOA are available, but their results are contradictory, population sizes are small, and there is a lack of standardized outcome measures. Despite a favorable safety profile and initial evidence for good efficacy of HCQ, there has not been a randomized, double-blind, and placebo-controlled trial in a larger patient group. HCQ was therefore not included as a therapeutic option in the EULAR evidence-based recommendations for the management of hand OA . To closRecruitment and follow-up of participants are in progress.


Herein we investigate the molecular bases of DNA polymerase I conformational dynamics that underlie the replication fidelity of the enzyme. Such fidelity is determined by conformational changes that promote the rejection of incorrect nucleotides before the chemical ligation step. We report a comprehensive atomic resolution study of wild type and mutant enzymes in different bound states and starting from different crystal structures, using extensive molecular dynamics (MD) simulations that cover a total timespan of ~5 ms. The resulting trajectories are examined via a combination of novel methods of internal dynamics and energetics analysis, aimed to reveal the principal molecular determinants for the (de)stabilization of a certain conformational state. Our results show that the presence of fidelity-decreasing mutations or the binding of incorrect nucleotides in ternary complexes tend to favor transitions from closed toward open structures, passing through an ensemble of semi-closed intermediates. The latter ensemble includes the experimentally observed ajar conformation which, consistent with previous experimental observations, emerges as a molecular checkpoint for the selection of the correct nucleotide to incorporate. We discuss the implications of our results for the understanding of the relationships between the structure, dynamics, and function of DNA polymerase I at the atomistic level. Escherichia coli, Pol is involved in lagging-strand replication, as well as repair of damaged DNA. The protein is a multifunctional enzyme that contains three different activities, namely DNA polymerase, 3′-5′ exonuclease, and 5′-3′ endonuclease, hosted in different domains of the enzyme. The large proteolytic fragment, known as the Klenow Fragment (KF), retains both the DNA polymerase and 3′-5′ exonuclease activities, which act in concert to carry out DNA synthesis with high fidelity. The 5′-3′ endonuclease activity is involved in excision of DNA flaps that result from polymerase action during DNA repair and lagging-strand DNA replication. Moreover, Okazaki fragment processing is one of the major roles of Pol I , identifying three conformations of the fingers subdomain  to examine fingers-closing in In this context, it was shown that the enzyme reactivity and fidelity of DNA replication strongly depends on the identity of the amino acids near the polymerase active site  simulations, covering a total of ~5 μs of simulation time. In particular, we focus on the Pol at different stages of the conformational mechanism preceding the chemical reaction step, investigating wild type, single, and double mutants of the protein in the open, ajar  and closed conformations. We also consider different ligand states, ranging from the apo enzyme to the binary and ternary complexes, with correct and incorrect dNTPs, and the effects of mutations E710A and Y766A on the properties of the conformational ensembles spanned by the protein  from We choose here a comparative analysis approach, which exploits accurate descriptions of the different proteins' internal dynamics, to investigate and characterize the salient traits of the functional-oriented internal dynamics of the different mutators, starting from different structures. We thus integrate different structural, dynamic and energetic analyses of the trajectories , and closed conformations. To this end, the structural ensemble spanned by the simulations was compared to the distinct conformations obtainable from experimental crystal structures. Principal Component Analysis (PCA) was used to assess the major conformational differences.First, we extracted 91 structures of Klenow Fragment (KF) homologs from the Protein Data Bank  structural basin.Projection of the trajectories onto the two main PCs obtained from the analysis of the crystal structures showed that, starting from the closed Figure  and openThe ajar system, first simulated in the presence of the non-complementary nucleotide as derived from the crystal structure  bound ligand(s) that relays the signal encoded by the nucleotide into the selection of a specific conformational state.Previous experimental evidence , populating structures that could reasonably be considered to connect the closed with the open states Figures . The douOverall, these results confirm a key role for the E710A and Y766A mutations in modulating the stability of the closed state of Pol, consistent with experimental FRET measurements  with all other protein domains. Interestingly, in the ajar state containing the non-complementary nucleotide, the region containing the sensing residue Y766, helix O1 and the fingers helix immediately following it appear to be much more globally flexible and less coordinated with the rest of the protein, while the region containing E710 appears highly coordinated with the palm and thumb domains. Strikingly, substitution of the non-complementary nucleotide with the correct one leads to a dramatic change in the patterns of coordination, with the fingers sub-domain now showing strong mechanical couplings with the rest of the protein, similar to the situation initially observed for the closed state Figure .In the open state, containing only DNA in a binary complex, the fingers are highly coordinated with the palm, defining an extended substructure combining the two regions in a coherent dynamic domain.The peculiar dynamic behavior observed for the fingers sub-domain, and specifically for the substructure containing the fidelity-defining residues, once more corroborates the role of DNA-contacting sequence stretches in sensing the presence and identity of the ligand and transmitting this information, through mechanical coordination, to different functional protein substructures.In this context, the analysis of mutators (ternary complexes starting from closed states with correct complementary nucleotide) shows different internal coordination patterns. In the cases of both E710A and Y766A single mutants, large areas of low coordination are evident in the matrices, indicative of overall increased conformational flexibility within the protein, even in the absence of major conformational changes for the protein. As expected, Y766F returns a coordination matrix sharing all the main traits of the WT closed state Figure .In the case of the double mutant E710A/Y766A, the coordination network tends to resemble the one of the open state, with the fingers and palm forming a single, extended block of coordination . This is consistent with the increase of spontaneously populated open-like conformations for the double mutant, previously characterized with PCA analysis, whereby the fingers/palm part can move as a coherent mechanical unit facilitating the population of the open state.N×N matrix (M) of average non-bonded interactions between pairs of residues calculated along an MD trajectory, with N being the number of residues stabilization of a certain conformation in response to varying conditions, we analyzed the energetics of different trajectories using the Energy Decomposition method  for the fingers, consistent with the coordination propensity analysis, which could lead to a substantially decreased fidelity in the nucleotide recognition process. Interestingly, the interaction networks typical of the WT closed state are mostly recovered with the Y766F mutation, albeit with a decreased coupling between the thumb and fingers.Finally, the interaction matrix for the double mutant E710A/Y766A returned a set of interaction patterns similar to those observed for the open state analyzed in a binary complex with DNA. It is worth noting here that the starting conformation is the closed one and that these results show the capability of the energetic analysis to capture the destabilizing effects on the closed state of mutations that favor the open one.Interestingly, these observations are in line with results from the Thirumalai group  approach  pre-defined reaction coordinate(s). We subsequently carried out comparative analyses of the resulting conformational ensembles by novel methods aimed to highlight the principal traits of internal coordinated dynamics and the underlying interaction networks. These methods have been previously applied to the investigation of protein stability and dynamic changes in response to ligand binding and sequence variation . The components of such vector, whose length equals the sequence length, indicates the contribution of each residue in conformational stabilization ] (see Methods) of the closed and open conformations. p(i) reports on the local deformation of the structures in the different conformational ensembles  and which are in contact with a region that shows significant differences in flexibility between the different dynamic states of the protein. Such residues are principally: F423, K426, W434, Y451, E521, Q522, F658, R822, Y824. The first three residues of the group are in the upper part of the exo domain and form a network of contacts that, in our model, can connect the exo with the thumb domain. Interestingly, F423 is part of the helix that hosts Y451. The latter is part of a tightly packed ensemble of aminoacids that contact the long helix  or away from the closed state toward the open one (non-complementary nucleotide) favoring the population of an ensemble of intermediate structures  was used as a tool to speed up conformational search, using the same acceleration parameters for all simulations. Each of the analyzed structures was minimized to the nearest local minimum. This method was used to probe the conformational energy surface of the protein and compare the results to available experimental data.We also performed reweighting of the trajectories as described by Miao et al. in Miao et al. . We repoAll the structural and principal components types of analyses were carried out using the AMBERtools suite of programs.A:dij is the (time-dependent) distance of the Cα atoms of amino acids i and j and the brackets indicate the time-average over the trajectory. The A matrix, and various quantities derived from it, can be used to characterize the salient elasticity and plasticity properties of a protein undergoing structural fluctuations  was used to analyze the relationships between superposed structures and molecular dynamics trajectory output. PCA, or Essential Dynamics (ED) has been proven to provide relevant details regarding the nature and directions of the main conformational changes in different protein families Garcia, . Herein,C, with elements Cij, built from the Cartesian coordinates, r, of the superposed structures:i and j represent all possible pairs of 3N Cartesian coordinates, where N is the number of atoms being considered. This matrix can be used to highlight protein regions that move coherently. The eigenvectors of the covariance matrix correspond to a linear basis set of the distribution of structures, called the principal components (PCs). The respective eigenvalues associated to each eigenvector define the variance of the distribution along the corresponding eigenvectors. This method describes global protein motions that are represented by the matrix eigenvectors and eigenvalues and emphasizes the amplitude and direction of dominant protein motions. The projection of the MD structures into the sub-space defined by the largest principal components recapitulates the major differences between structures, highlighting possible relationships between different conformational states.PCA is based on the diagonalization of the covariance matrix, For the case described in this paper, the PCA calculation was carried out on the C-alphas of the various experimental structures, using only those regions for which homologous residues are found in all structures, based on sequence alignment.Mij, which is determined by evaluating average, interresidue, non-bonded  interaction energies between residue pairs, calculated over all structures visited during an MD trajectory. For a protein of N residues, this calculation yields an N×N matrix of pair couplings mij such that the total average non-bonded energy of the protein is given by the sum over the matrix entries. We showed that, after diagonalizing the matrix Mij, one can approximate pair couplings using the first eigenvalue lambda and eigenvector w:The Energy Decomposition Method is based on the calculation of the interaction matrix The first eigenvector profile reports on the single residue contributions to the essential stabilization energy.N components of the eigenvector associated with the lowest eigenvalue have been shown to identify residues behaving as strong, attractive interaction centers, characterized by components with an intensity higher than the threshold value corresponding to a “flat” normalized vector whose residues would all provide the same contribution. The method thus provides an effective energy approximation, which focuses on the main determinants of stabilization of a certain fold, neglecting highly repulsive interactions . It must be noticed that EDM allows to take into account the modulation of interaction energies due to different structural and dynamical contexts that might not be caught by using more simplified energy models, neglecting motional aspects of protein conformations.In this context, it must be underlined that the spirit of the method is to provide a simplification of the noisy energy matrix obtained from MD simulations . The The method was validated against experimental data and a relationship was found between the topological and energetic properties of a protein and its stability domain and of the whole protein. This is based on the concept of domain as a compact and independent folding unit. In particular, starting from the analysis of the non-bonded interaction energy matrix associated with whole protein, our method filters out and selects only those specific subsets of interactions that define possible independent folding nuclei within a complex protein structure. This allows grouping different protein fragments into energy clusters that are found to correspond to structural domains  domains the matrix Efold  completely neglects the inter-domain interactions whereas each of its blocks represents the essential folding interactions of the corresponding domain.Therefore, in analogy with the case of one-domain proteins, we postulate that: (a) for each domain there should exist only one associated eigenvector recapitulating the most significant interactions of the domain, in the same way as the eigenvector associated with the lowest eigenvalue recapitulates the most significant protein interactions for single-domain proteins; (b) each “domain eigenvector” should have a block structure, namely the significant components of each “domain eigenvector” should correspond to the residues belonging to the domain; (c) the union of all the significant blocks should cover all the protein residues. In an ideal case the previous hypotheses would be satisfied and, after selecting the proper set of eigenvectors, the “filtered” non-bonded interaction energy matrix would be simply obtained as,Enb are characterized by overlapping blocks of significant components and more than one eigenvector for each domain is generally needed. In order to satisfy as much as possible the working hypotheses introduced above, it is necessary to select the smallest set of eigenvectors that cover the largest part of residues  with the minimum redundancy. Hence, the corresponding essential folding matrix is obtained asNe, namely the number of essential eigenvectors, is generally > ND. The matrix Efold is afterwards further filtered through a symbolization process to emphasize the significant non-bonded interactions and, finally, it is subject to a proper clustering procedure leading to the domains identification.In a real situation, the eigenvectors of the non-bonded interaction energy matrix The goal of the analysis is a matrix whose relevant elements correspond to the essential components of the protein non-bonded interaction energy matrix, components that allow to identify the most important residues in defining and stabilizing the three dimensional organization of the different sequences. In turn, this gives insights into the determinants of the structural stability of the detected domains.In the case of Pol seven eigenvectors were used.The full procedure for the selection of essential eigenvectors is described in Genoni et al. J. Phys. Chem. B 2012 (Genoni et al., GC, designed research, ran experiments, wrote the paper; MM, designed research, ran experiments, analyzed results; MS, wrote the paper, analyzed results; TC, analyzed results, wrote the paper; AK designed research, wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The aim of the present study was to present a novel non-local mean (NLM) method to denoise diffusion tensor imaging (DTI) data in the tensor space. Compared with the original NLM method, which uses intensity similarity to weigh the voxel, the proposed method weighs the voxel using tensor similarity measures in the diffusion tensor space. Euclidean distance with rotational invariance, and Riemannian distance and Log-Euclidean distance with affine invariance were implemented to compare the geometric and orientation features of the diffusion tensor comprehensively. The accuracy and efficacy of the proposed novel NLM method using these three similarity measures in DTI space, along with unbiased novel NLM in diffusion-weighted image space, were compared quantitatively and qualitatively in the present study. The Bro smoothing , was proin vivo DTI data were assessed.The present study introduces the basic principle of this smoothing method, as well as the implementation details. Performance of the algorithm was evaluated on the basis of three similarity measures, namely, Log-Euclidean distance (LED) , Riemannet al  is an ED of the gray level between pixels p and q, h controls the rate of decay of the exponential function and Z is a normalizing factor calculated as follows ::16):V is the tensor matrix and λi is the ith eigenvalue of the matrix, V(p)−1V(q). Compared with the Euclidean metric, which admits non-positive tensors and exhibits a swelling effect, the affine invariant metric coincides with the Fisher information metric ::V is theTrace denotes the sum of the eigenvalues.where According to the original NLM method , the grawhere the weight can be calculated using equations 6in vivo conditions, synthetic tensors in the curves were constructed to have a trace of 2.1×10−5 cm2/sec and an FA of 0.8. These diffusion parameters were similar to those in normal brain parenchyma. Diffusion-weighted imaging was simulated along 32 non-collinear directions with a b value of 1,000 sec/mm2, and the diffusion-weighted data were corrupted with 5% Rician noise as follows  and mean deviation of the FA prior to and following denoising. The first assessment demonstrated the improvement of the visual effect with the denoising methods. The second assessment measured the capability of these denoising methods to restore the tensor orientation, while the last assessment gauged the extent to which the tensor directionality was restored with the denoising methods.Finally, the effects of denoising were more rigorously examined with permutation tests, which compared the influences of different denoising methods on the uncertainties of the DTI data. Experimental procedures were conducted as follows. Firstly, the DWI data were computed from the denoised and uncorrupted DTI data. Secondly, residuals were obtained by calculating the differences between the DWIs from the denoised DTI data and those from the uncorrupted DTI data. The residuals were then randomly permuted and added to the uncorrupted DWI data. This process was repeated 1,000 times to produce 1,000 new DWI datasets, from which DTI data were derived. The 1,000 samples of each of the six independent tensor components were fitted with normal distributions, and the mean and 95% confidence intervals for each component were calculated. Finally, an ellipsoid was constructed corresponding to the mean DTI data, which was sandwiched by ellipsoids corresponding to the upper and lower bounds of the confidence interval.h controls the decay of the exponential function, which impacts the degree of filtering. Typically, the value of h is selected on the basis of the variance of noise  and an eight-element SENSE coil. A single shot, echo-planar pulsed gradient spin-echo imaging sequence was used, and diffusion weighting was performed along 32 non-collinear directions with a b value of 1,000 sec/mm2. A total of 64 contiguous, 2-mm-thick slices with a matrix size of 128×128 were acquired from a field of view of 256×256 mm2, yielding an in-plane pixel size of 2×2 mm2. Three repeated scans were obtained from each subject, which were motion and distortion corrected and then averaged using the Philips diffusion registration PRIDE tool . Diffusion tensors were estimated from the averaged DWI data using a linear least-square fitting procedure (To assess the performance of the proposed algorithm on in vivo dataset and the parameter settings were the same as for the synthetic experiments. Denoising results were evaluated visually and quantitatively, on the basis of the tensor geometry, the mean angular deviation of PD and the mean deviation of FA, as before.The same four methods were utilized with the Results of the proposed DTI-NLM method are demonstrated in Among the different weighting schemes using NLM, the RD and LED methods  yielded Quantitative evaluations of the smoothing effects are shown in Changes in the tensor uncertainties following denoising are shown in in vivo human DTI dataset, which demonstrated marked artifacts resulting from noise. in vivo data were similar to those on the simulated data.To assess the denoising effects in the tensor structure, the same section presented in The computational efficiency of the proposed NLM-DTI algorithm with the three different weighting schemes and UNLM in DWI space is demonstrated in In the present study, a novel DTI denoising method in the tensor space is proposed. Following the widely recognized NLM method, DTI data were denoised by averaging the tensors in a specific neighborhood according to their similarity to the tensor in the center. Under the proposed NLM framework, three weighting schemes corresponding to different features extracted from the tensor, as well as GF in DTI space and UNLM denoising in DWI space, were compared quantitatively and qualitatively with synthetic and real human brain DTI datasets.The results of these experiments demonstrated that the proposed method is able to suppress noise and preserve explicit boundaries in DTI data, with the extent varying depending on the similarity weighting scheme used. Specifically, the denoising performance relied primarily on the accuracy of tensor comparison. When the tensor was compared on the basis of a greater number of structural features, as implemented in RD and LED, the proposed NLM method achieved the best effect. By contrast, when the comparison used fewer structural features, as implemented in ED, the denoising effect was markedly decreased.b values. In addition, due to the nonlinearity between the DWI and DTI spaces, optimal denoising results in the DWI space are not necessarily optimal in the DTI space. Therefore, denoising DTI directly is able to achieve the most intuitive restoration results and convenience for subsequent applications.As previously discussed, DTI denoising may be broadly divided into two categories: Denoising in the DWI space and denoising in the DTI space. Compared with DWI denoising, DTI denoising is much more convenient to implement and is able to achieve similar denoising results for orientation and structure information. Unlike DTI denoising, which involves only six independent parameters, DWI data normally entail a much greater number of directions and the denoising procedure has to be duplicated for all the directions. As the application of HARDI increases , the comin vivo human DTI data. With the application of an affine invariant matrix, which encapsulates the comprehensive information of the diffusion tensor, NLM-DTI denoising is able to achieve comparative restoration results to resemble the denoising method in DWI space. In addition, the time efficiency demonstrates the practical use for this method as a routine denoising algorithm.Finally, it should be considered that the proposed method does not come without limitations, despite its great potential. Although denoising in DTI space is timesaving when compared with DWI denoising, the NLM method itself is generally computationally intensive. This can be resolved by improving the NLM framework  or by im


Antibody drug conjugates (ADCs) are an efficacious class of anti-cancer drugs that comprise monoclonal antibodies (mAbs) conjugated to small-molecule cytotoxic agent via a stable linker. This review summarizes the current knowledge and developments in the field of ADCs. Over the past couple of decades, antibody–drug conjugates (ADCs) have revolutionized the field of cancer chemotherapy. Unlike conventional treatments that damage healthy tissues upon dose escalation, ADCs utilize monoclonal antibodies (mAbs) to specifically bind tumour-associated target antigens and deliver a highly potent cytotoxic agent. The synergistic combination of mAbs conjugated to small-molecule chemotherapeutics, via a stable linker, has given rise to an extremely efficacious class of anti-cancer drugs with an already large and rapidly growing clinical pipeline. The primary objective of this paper is to review current knowledge and latest developments in the field of ADCs. Upon intravenous administration, ADCs bind to their target antigens and are internalized through receptor-mediated endocytosis. This facilitates the subsequent release of the cytotoxin, which eventually leads to apoptotic cell death of the cancer cell. The three components of ADCs  affect the efficacy and toxicity of the conjugate. Optimizing each one, while enhancing the functionality of the ADC as a whole, has been one of the major considerations of ADC design and development. In addition to these, the choice of clinically relevant targets and the position and number of linkages have also been the key determinants of ADC efficacy. The only marketed ADCs, brentuximab vedotin and trastuzumab emtansine (T-DM1), have demonstrated their use against both haematological and solid malignancies respectively. The success of future ADCs relies on improving target selection, increasing cytotoxin potency, developing innovative linkers and overcoming drug resistance. As more research is conducted to tackle these issues, ADCs are likely to become part of the future of targeted cancer therapeutics. The advent of modern-day cancer chemotherapy dates back to the mid-1900s when a chemical warfare agent known as nitrogen mustard was seen to destroy the bone marrow and lymph tissue of exposed individuals  took cenIn recent years, enhanced understanding of cancer biology has shifted the focus of cancer treatment from traditional chemotherapy to targeted cancer therapies that take advantage of the differentiating features of tumour cells to provide a framework for drug development. These distinctive features, collectively known as the ‘hallmarks of cancer’ ,8 enableCancer cells differ from normal cells due to genomic mutations in oncogenes and/or tumour suppressor genes . Once thThe use of antibodies as ‘magic bullets’ to treat disease was first proposed more than 100 years ago by the founder of chemotherapy, Paul Ehrlich . Due to mAbs exert their therapeutic effect  by bindiADCs are tripartite drugs comprising a tumour-specific mAb conjugated to a potent cytotoxin via a stable linker . The thrAlthough the concept of ADCs is theoretically simple, it is difficult to combine its various components into an optimized and functional therapeutic agent. To date, three ADCs have gained entry into the market, of which only two remain . The firWhen designing an ideal ADC, it is essential to understand the mechanism of action in order to identify the desired features of each of its three components. An ideal ADC is one that retains the selectivity and killing capacity of a mAb while still being able to release the cytotoxic drug in quantities large enough to kill tumour cells. Each of the steps involved in the mechanism of action is associated with unique challenges  that comADCs are administered intravenously in order to prevent the mAb from being destroyed by gastric acids and proteolytic enzymes. The mAb component of the ADC enables it to circulate in the bloodstream until it finds and binds to tumour-specific (or tumour-associated) cell surface antigens present on target cancer cells. In the interest of preventing unwarranted release of the cytotoxin and maximizing drug delivery to cancer cells, an ideal linker would not only have to be stable in the bloodstream but also capable of releasing the active form of the cytotoxic drug when required .+ ions into the endosome results in an acidic environment that facilitates the interaction between the mAb component of a fraction of ADCs and human neonatal Fc receptors (FcRns). The bound ADCs are transported outside the cell, where the physiological pH of 7.4 enables the release of the ADC from the FcRn . Because of potential linker instability and poor ADC internalization, several drugs are required to be linked to each mAb to achieve adequate cytotoxicity. On the other hand, excessive drug conjugation might result in increased clearance and/or immunogenicity as the ADC is more likely to aggregate and be recognized as a damaged, malformed or foreign protein . In a st potency .A critical aspect of linker function is the presence or absence of a phenomenon known as the bystander effect. This effect refers to target-cell mediated killing of healthy cells neighbouring the tumour cell. The bystander effect is generally caused by cellular efflux of hydrophobic cytotoxic drugs, capable of diffusing out of an antigen-positive target cell and into adjacent antigen-negative healthy cells. Although the bystander effect undermines the target specificity of ADCs, it can be advantageous when tackling solid tumours that lack homogenous expression of the target antigen . HoweverIn recent years, a great deal of research has been conducted in order to develop novel conjugation techniques for use in future ADCs. Traditionally, cytotoxic drugs have undergone chemical conjugation to mAbs, whereby reactive portions of native amino acids are made to interact and bind a specific part of the linker molecule . ExampleHeterogeneity, with respect to the number of cytotoxin molecules attached per antibody , resultsin vivo efficacy compared with heavy chain conjugates.Heterogeneity, with respect to the position of the cytotoxin on the mAb, plays an important role in the therapeutic effect of ADCs. In an experiment conducted by Shen et al. , ADCs wiCurrently researched methods for the development of site-specific drug conjugation are based on introducing selectively reactive molecules at specific locations along the mAb. This allows more control over the site and number of drug attachments and can potentially increase the therapeutic window of ADCs, while reducing toxicity and improving pharmacokinetics . The thrConjugation of novel unpaired cysteine residues to a small portion of the mAb relies on site-directed mutagenesis to introduce a fixed number of engineered cysteines at specific, controlled sites along the mAb ,142. Thiin vivo (and in vitro) activity. Apart from transglutaminases, other enzymes such as glycotransferases- and formylglycine-generating enzymes have also been investigated for use in a similar manner [A second method of site-specific drug conjugation uses a microbial transglutaminase enzyme in order to conjugate an amine-containing cytotoxic drug to a mAb modified to have a specific number of glutamine side chains ,147. A mr manner ,150.in vitro transcription and translation [The final method of drug conjugation is based on the use of non-natural amino acids, such as selenocysteine or acetylphenylalanine ,152. Thenslation .As ADCs are most often prepared in an aqueous solution and administered intravenously, it is important that the cytotoxic drug has prolonged stability in such environments to prevent damage to healthy cells and increase the availability of the drug at the tumour site. Similarly, it is important that the molecular structure of the cytotoxin allows for its conjugation to the linker while avoiding immunogenicity, maintaining the internalization rate of the mAb and promoting its anti-tumour effects  . RegardlFirst generation ADCs made use of early cytotoxins such as the anthracycline, doxorubicin or the anti-metabolite/antifolate agent, methotrexate ,160. AltThe auristatins, monomethyl auristatin E (MMAE) and monomethyl auristatin F (MMAF) are specific types of mitotic inhibitors that share their mechanism of action with the traditionally used taxane chemotherapeutics. Auristatins interfere with the formation of microtubules by binding to the β-subunit of α-β tubulin dimers in the cytoplasm. The drug subsequently takes effect by preventing the hydrolysis of GTP molecules on the β-subunit, causing continuous and excessive growth of microtubules . As the Similar to auristatins, maytansines, the derivatives of which are known as maytansinoids (DMs), also interfere with microtubule assembly but are mechanistically similar to vinca alkaloids . MaytansCalicheamicins, duocarymycins and PBD dimers are all different types of DNA-damaging agents that are functionally similar to anthracyclines in that they all target the minor groove of DNA –176. TheMany lessons have been learnt since the FDA withdrawal of gemtuzumab ozogamicin regarding the design and development of ADC components. Pharmaceutical companies investing in ADC research have made significant advances in linker technology, conjugation chemistry, antibody engineering and the identification of potent cytotoxins, resulting in rapid evolution of the field. This has not only led to the recent approvals of brentuximab vedotin and trastuzumab emtansine but has also driven the clinical development of ADCs. Currently approved ADCs and those in advanced clinical development are listed in One particularly promising ADC, inotuzumab ozogamicin (Pfizer), has recently been discontinued for the treatment of aggressive non-Hodgkin's lymphoma following the failure of a once-a-month dose of the drug with rituximab to meet the primary objective of improving overall survival. This was in comparison with a combination of bendamustine/gemcitabine with rituximab. However, it continues to be investigated in phase 3 for acute lymphoblastic leukaemia, recognized in the U.S. and the E.U. as an orphan indication with few other treatment options .Despite the strong ADC pipeline, the cost of ADC development remains a major drawback in terms of their widespread use in the clinic. The current cost of treatment with the mAb trastuzumab, is approximately $767 per dose , with thThe major challenges associated with the development of ADCs arise from factors that interfere with ADC efficacy and/or those that result in ADC-mediated non-target cell toxicity. All three components of an ADC contribute to these challenges and need to be optimized to create a successful conjugate. Once the therapeutic effect of an ADC has been maximized, it is also desirable to prolong these effects by avoiding resistance mechanisms that decrease the duration of ADC efficacy. All targeted cancer therapies (including ADCs) are prone to resistance mechanisms that alter the function of the target antigen and render the treatment redundant . The futWith more research being conducted on the use of various targeted therapies, there has been a considerable increase in the number of target antigens viable for antagonism by ADCs . A selecWith regard to mAbs, lack of specificity to the target antigen might result in cytotoxic effects on healthy tissue, whereas poor internalization or inadequate antigen-binding affects the therapeutic effect of the ADC. In addition to these challenges, the large size of mAbs limits their capacity to penetrate tumour tissues, which therefore complicates the use of ADCs against solid tumours. In order to combat this, current research has explored the use of antibody fragments such as diabodies and minibodies  which, bThe future of linker chemistry is moving strongly toward the identification of new conjugation techniques that yield homogenous ADC mixtures. Research within this area has greatly expanded in the past few years with the identification of new techniques to attach cytotoxic drugs to antibodies.Despite advances in many aspects of ADC development, the list of amenable cytotoxins for use in ADCs has remained relatively short . This isMost cytotoxins are susceptible to resistance via an array of mechanisms . ResistaDespite challenges in their design, ADCs have created a new paradigm for novel cancer chemotherapy. With the specificity of mAbs and the cytotoxic capacity of small molecule drugs, ADCs promise to be a large part of the future of precision medicine as well as combination treatment. As more clinical trials are conducted on existing ADCs, it should be possible to fine-tune the components of forthcoming conjugates and improve their therapeutic risk-benefit ratio. Finding new target antigens for solid tumours, improving the understanding of mAb activity and developing novel cytotoxin-linker pairs would all pave the way for a new generation of ADCs.


Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions. Protein-protein interactions (PPIs) are central to the majority of biological processes, from cell adhesion to immune functions. Hormone-receptor binding, chaperone-regulated client folding, antigen-antibody complex formation and signal transduction (to name a few) are all regulated through a delicate interplay of different physico-chemical factors, ranging from the conformational organization of the interacting surfaces to the evolutionary optimization of their sequences for efficient binding. An improved understanding of PPIs could not only advance our knowledge of chemical biology but also facilitate the rational discovery of new molecules targeting PPIs for therapeutic interventions, as well as design novel protein interactions for research and biotechnological purposes.Despite the advances of experimental genomic and proteomic efforts producing large amounts of data, combined to the development of databases and bioinformatics algorithms123467891011In this context, a great effort has been put in computational studies aimed at illuminating the structural or physico-chemical properties that differentiate the regions in the proximity of binding sites. These can be formalized in descriptors that, once implemented in prediction systems, would allow the prediction of interaction interfaces. State-of-the-art PPI prediction methods usually achieve remarkable performance relying on different sets of combined parameters121314in silico epitope prediction of protein-antibody and/or peptide-MHC recognition regions based on a novel physico-chemical descriptor of the internal energetics of proteins. At its core, the method employs a matrix of pairwise non bonded interactions, decomposed by Principal Component (PC) analysis and rebuilt according to its principal components (eigenvectors): each element defines the strength of interaction between residue pairs. The matrix is used to identify networks of energetically non-optimized pairwise interactions, meaning subnetworks of aminoacids characterized by a very low non-bonded interaction energy with the rest of the protein. These in turn can define antibody binding sites on an antigen’s three-dimensional structure. Such method is freely accessible as a web-based predictor under the name of BEPPE1617In this framework, our group has recently developed a new way to approach Here, we hypothesize that the reach of this analysis should not be restricted only to antibody binding sites and T cell receptor recognition. With this in mind, we formulated the hypothesis that the analysis of intra-protein energetic couplings with different intensities can uncover new and more general aspects of protein-protein recognition and interaction.In this paper, we used and expanded the Energy Decomposition Method to search for those protein surface networks characterized by strong couplings among constituting residue pairs, as well as surface networks characterized by weak couplings among residue pairs. Strong couplings define residue networks important for structural stabilization20212223We analyzed 30 binary interactions and their respective 60 constituent monomers, each one available in both crystallographic forms  in the Protein Data Bank, and verified whether the patches of coupling or uncoupling energies generate specific motifs (marks) across the interfaces. In order to be useful for mechanistic analysis or predictive purposes, the marks need to be persistent and detectable from the constituting proteins, so that the analysis of an isolated molecule can provide information on the bound state.The results of our analysis revealed that the energetic subnetworks consisting of strongly coupled residues locate preferentially the inner core of the binding sites, a region that is more prone to accommodate PPI hotspots2526a priori.Our approach thus revealed a notable effect of co-localitazion of regions of strong coupling or uncoupling upon complex formation, unexpected monomeric, isolated partners for a diverse ensemble of protein-protein assemblies. As a consequence, we developed an algorithmic implementation of the method for the analysis of monomers and complexes under the name of BLUEPRINT (BEPPE-Like Un-coupling Energy for PRotein INTeraction). Examples, applications and impact for further practical and theoretical studies will be discussed.The analysis of such energetic motifs could help identify binding sites and binding sites orientations from the The dataset was composed of binary complexes (heterodimers) chosen from the Protein Docking Benchmark 4.0et al.Our protocol describes the energetic coupling as a measure of the local non-bonded energy contribution of surface residues to intraprotein stability. The concept is an expansion of the Energy Decomposition Method (EDM) used for assessing the determinants of stability of protein 3D folds193016172021The final matrix is simplified in the form of a profile, averaging all the energetic contributions for each residue to one value . From thAfter the analysis, all coupled and uncoupled residues are mapped on the 3D structure of monomers and complexes. The primary objective of the analysis is to find patches of surface residues with a shared energetic property in their local networks (coupled or uncoupled) and to map them on the constituent proteins of a complex in search of possible characteristic motifs across the binding interface.The results of the energetic analysis can be mapped on the surface of the isolated proteins. They lead to define patches of strongly interacting residues represented as peaks in blue stripes”, whenever the motifs co-localize at the interface of the binding partners. Similarly, the strongly coupled patches that form a continuous mark across the interface will be referred as “red stripes”, following the color code adopted in the images (blue stripe.From now on we will refer to the uncoupled networks as “e images . To defiblue stripe in the majority of cases (26 out of 30), while the presence of a sharp red stripe can be verified in 20 candidates  and 62% (red) residues between the two cases . A contrblue stripe and 2 lacking a red one. If we consider the absence of a stripe in only one of the two conditions, the only candidates for which is impossible to assess both blue and red stripes are 1KTZ and 1FFW. Overall, there is a prevalence of dimeric complexes featuring a full, concomitant blue and red coordination (11 cases). Dimers featuring just one of the two coupling effects at the interface are also common, 10 for the blue stripes and 7 for red ones.There are only six cases for which a stripe cannot be assessed in either condition (monomers and complexes), and they comprise 4 dimers lacking a stripe are evenly distributed among the three categories in which the dataset is subdivided , and since the entries in each category are sorted by number of residues  in the majority of cases (26/30 monomers and 20/30 complexes), producing an interesting geometrical continuity . The red stripes, on the other hand, are sparser though present in the majority of cases .Visual inspection of the monomers highlights three main elements: first, the uncoupled areas actually define “stripes. The blue stripes are not limited to the interface region, but they tend to expand across the protein surface . Conversely, red stripes, indicating spots of high energy coupling, are smaller, and usually confined across the interface area.The second element is the size of the blue stripes and red stripes tend to gather on opposite regions of the protein/complexes .The third characteristic indicates that blue stripes and sparser red ones. In the complex analysis we have the opposite situation. The blue stripes are still visible for the most part, but they are less refined. Red stripes conversely are clearer, less sparse and characterized by higher continuity. This is consistent with the previous observation concerning the number of cases showing continuity, with uncoupled regions forming more blue stripes when monomers are analyzed compared with the same analysis over the complexes, vice versa for red stripes . These residues may switch to highly coupled behavior instead . The effect is interesting but not unexpected: The BLUEPRINT analysis is dependent on the local network one residue establishes with the amino acids in its proximity. In the event of protein binding, the network of one amino acid can change dramatically in the proximity of the binding interface, and as a consequence of conformational rearrangements.blue stripe. Importantly, other proteins for which a major conformational rearrangement is present  also show stripe continuity . We applied the same methodology described for the dimers to those proteins of the dataset for which a higher supramolecular organization level was present, since proteins engaged in transient interactions are commonly promiscuous or part of larger protein complexes. These cases  represent a first valuable opportunity to test the hypothesis of the conservation of the properties of patches and stripes from the monomeric state to a higher level of complexity.blue stripe, and three out of four complexes featured a red stripe, with the exception of 1IB1  over the full supramolecular assemblies, returning a full 4/4 red ones  and confstripes, we evaluated the identities of their constituting aminoacids. We thus compared the residues selected by BLUEPRINT with the overall aminoacid composition of the protein surfaces, calculating the percentage of occurrence of each amino acid as part of an uncoupled patch (blue stripe) or a strongly coupled one (red stripe) with respect to the occurrence of the amino acid in the whole surfaces. The BLUEPRINT analysis focuses on the local, non-bonded coupling energy connecting the surface residues of a protein. To further characterize the physico-chemical properties of the identified The returned propensities are very similar comparing monomer and complex analyses . The disUnderstanding the processes underlying protein-protein interactions (PPIs) at the molecular level is important for both fundamental and practical issues. From the fundamental point of view, it can help understand the correlations between sequence, structure and function as well as reveal the molecular basis for the (dis)regulation of important biochemical pathways. From the practical standpoint, it could set the basis for the design of bioactive molecules, to develop biosensors or to engineer probes for the discovery and validation of disease targets. Interesting examples of such applications are the realization of protein mimics of antigenic epitopesSystematic studies of transient interactions have highlighted key factors for the formation and stabilization of protein complexes, including geometric complementarity, hydrophobicity, solvent accessibility or desolvation propensity36stripes” (red and blue stripes), and carried out further investigations. Indeed, from the analysis of a heterogeneous dataset of known interacting proteins, we assessed the co-localization of the patches identified in the individual proteins once they are displayed on their respective complexes as a general property. The whole protocol has been named BLUEPRINT (BEPPE-Like (Un)coupling Energy for PRotein INTeraction).In this paper, we approached another physico-chemical aspect of interacting proteins, studying the interplay between the interaction networks of surface residues in isolated proteins and their roles in respective protein complexes. The initial hypothesis entailed the presence of specific intramolecular energetic signatures for the residues participating in the interaction process. This is based on the results of a recently developed computational approach that proved successful in the prediction of antibody binding sites20212223Energetically non-optimized areas  defining the rims of the binding surfaces, are conserved on passing from the isolated proteins to their complexes, where they end up forming continuous stripes spanning the two contacting macromolecules.In addition to the observations made possible with a dataset of dimeric interactions, the general principle of our model is supported by results obtained for proteins forming larger tetrameric and hexameric multimers: the large number of patches observed for these proteins in isolation indicates the presence of several possible binding sites for the formation of polymeric supramolecular aggregates , extendistripes with high identity between monomers and complex is unchanged in the latter categories  kinetic model3637383941This hypothesis is corroborated by the differences observed between blue and red stripes in monomer vs. complex analysis. Blue stripes are clearer when calculated on isolated structures, while red stripes are well-defined in the actual protein-protein complexes, and include regions across the binding interface that were previously uncoupled in the monomeric form.We propose that the persistent motifs, isolated in the energetic footprint of the constituent proteins in their monomeric isolated form, define a signature property of proteins involved in protein-protein interactions. To further explore this hypothesis, in a possible future evolution of this work, one could analyze the presence of such patterns on proteins not involved in PPIs , as well as look for similarity in the stripe morphology of structurally homologue proteins, possibly correlating it to functional similarity.In conclusion, the proposed method provides interesting novel insights into the salient aspects of protein energetics that determine protein-protein interactions. The central concept of the approach we presented entails a shift from interface-focused prediction to an energy based pattern analysis. This could represent a complementary method to classical interface predictors and docking algorithms to gain information on the binding process, including the location of binding sites, the properties of their surroundings and how these can preferentially orient the binding partners. Energetic coordination carries some interesting theoretical implications too, posing new questions, such as the investigation of the link between non-bonded energy networks and co-evolution11Each protein (and complex) was prepared for analysis by conversion of selenomethionines (MSE) to methionine and modeling of missing sidechains using MAESTRO version 9 from SCHRÖDINGER software and the package Prime4445Molecular Mechanics Generalized Born Surface Area) included in the AMBER 11 software package. The polar solvation term was approximated with the Generalized Born (GB) model482.All input structures were converted in AMBER-compatible PDB files (Assisted Model Building with Energy Refinements)The electrostatic and Van der Waals interaction terms from GBSA decomposition were used to build a matrix composed of the interaction energy between residue pairs in the structure. The matrix was decomposed through Principal Component Analysis. The resulting main eigenvectors and eigenvalues were used to recover a simplified matrix recapitulating the most relevant interactions within the protein structure.vi} is calculated:The first eigenvector recapitulates the main contribution to the stabilization energy, which is due to the strongest pair interactions defining the energy core. In the case of small, monodomain proteins, it is typically sufficient to correctly identify the residues constituting the core. In contrast, in the presence of more complex topologies or multi domain proteins, more eigenvectors in the energy approximation can effectively improve the definition of the energy core. The relevant eigenvectors are those containing multiple consecutive components of equal sign, which points to the presence of a cluster of similarly interacting residues. To select them, for each eigenvector and all eigenvectors with a non zero lag-l autocorrelation for l lower than a threshold value are selected. The threshold, adjusted to yield optimal energy coverage among the broadly heterogeneous dataset, was set to 30.Ri, Rj) is assigned a positive value (1) in the contact map if any of these two elements are in proximity  within a radius of 6 Å, otherwise the contact is flagged as negative (0). At the same time, the contact map was used to restrict the analysis to the surface residues only. The solvent accessibility for each residue (SASA) was calculated with the software naccess2.The new and simplified matrix was filtered by a contact map, in order to remove all distal contributions. The topology is defined reducing each residue to a pair of coordinates: the Cα carbon (CA) and the center of mass of the sidechain (CM). Each pair of residues .


The fluctuation scaling (FS) law has been observed in a wide variety of phenomena. It states that the variance of a quantity has a power function relation with the mean. Since Taylor found it in ecological systems , the FS In order to quantify the variability of neural firing, two quantities: inter-spike interval (ISI) and spike count, are often measured from spike trains. We first consider the ISI statistics. Let The scaling exponent Consider next the counting statistics. Let with In the presentation, I show the following two results:The fluctuation scaling law emerges in the first-passage time to a threshold of certain diffusion processes .The likelihood function of spike trains is constructed, based on which I propose a method for extracting the scaling exponent from nonstationary spike trains. This method is applied to biological spike train data to characterize the variability of neuronal firing.Possible implications of these results are discussed in terms of characterizing intrinsic dynamics of neuronal discharge.


One of the great challenges of contemporary neuroscience is understanding in a quantitative manner how neurons and neural ensembles encode and process information in the form of action potentials. The advent of multi-electrode arrays which can record large amounts of data simultaneously from several neurons has made this task more urgent. This task, however, is made difficult by the inherent complexity of neural systems which are highly nonlinear, interconnected, dynamic, and subject to stochastic variations. Furthermore, while several methods exist which offer good predictive performance for spike train modeling, their adoption in the broader neuroscience community has been limited due to their mathematical complexity and lack of interpretability.th order PBV kernel, PBVn, is derived in two steps. First, we calculate the conditional probability of an output spike given n input spikes at various lags. Second, we subtract lower order effects to isolate the nth order nonlinearity. Thus, the first PBV kernel, PBV1(τ), is the conditional probability of an output spike given a spike in the input spike train at time τ, minus the probability that there will be a spike in the output, i.e.:Here we present a novel and intuitive methodology of modeling nonlinear dynamic systems with point process inputs and outputs, such as interconnected neuronal ensembles. The method relies on the expression of the nonlinearity in the form of Volterra-like kernels, termed the Probability-Based Volterra (PBV) kernels. The n2, which is the first nonlinear kernel, is the conditional probability of an output spike given a pair of input spikes minus the conditional probability of an output spike given either one of those input spikes individually, i.e.:This first order kernel describes a linear impulse response filter similar to that derived from spike triggered averaging and cross-correlation methods. The second order PBV kernel, PBVth PBV kernel. We show that the PBV kernels are equivalent to the Wiener kernels when the input is a Poisson process, thus placing the PBV kernels in the context of a well-established and rigorous mathematical theory [This method may be extended to describe the contribution of n pairs spikes to the output in the form of the nl theory .The proposed PBV methodology was applied to synthetic systems where the ground truth of the model was available. The PBV kernels were found to both accurately estimate the ground truth kernels and to reproduce the given output, thus validating the method. Finally, the proposed PBV methodology was applied to real neural data derived from the CA3 and CA1 regions of the rodent hippocampus . Althoug