This study compared the protective effects in BoHV−1, BoHV−1 ΔgE, BoHV−1 gE/E2−Linker−E2+, and BVDV−1 inactivated antigen immunized guinea pigs and calves. The results showed that BoHV−1 gE/E2−Linker−E2+ could successfully induce guinea pigs and calves to produce specific neutralizing antibodies against BVDV−1. In addition, after BoHV−1 and BVDV−1 challenges, BoHV−1 gE/E2−Linker−E2+ can produce a specific neutralizing antibody response against BoHV−1 and BVDV−1 infections. Calves immunized with this type of virus can be distinguished as either vaccinated animals (gE-) or naturally infected animals (gE+). In summary, our data suggest that BoHV−1 gE/E2−Linker−E2+ and BoHV−1 ΔgE have great potential to prevent BVDV−1 or BoHV−1 infection.Bovine respiratory disease complex (BRDC) is a comprehensive disease in cattle caused by various viral and bacterial infections. Among them, bovine herpesvirus type I (BoHV−1) and bovine viral diarrhea virus (BVDV) play important roles and have caused huge financial losses for the cattle industry worldwide. At present, vaccines against BRDC include trivalent attenuated BoHV−1, BVDV−1, and BVDV−2 live vaccines, BoHV−1 live attenuated vaccines, and BoHV−1/BVDV bivalent live attenuated vaccines, which have limitations in terms of their safety and efficacy. To solve these problems, we optimized the codon of the BVDV−1 E2 gene, added the signal peptide sequence of the BoHV−1 gD gene, expressed double BVDV−1 E2 glycoproteins in tandem at the BoHV−1 gE gene site, and constructed a BoHV−1 genetics-engineered vectored vaccine with gE gene deletion, named BoHV−1 gE/E2−Linker−E2 Bovine respiratory disease complex (BRDC) is a comprehensive disease in cattle caused by a variety of viral and bacterial infections, which is causing significant economic losses for the cattle industry in China and worldwide ,2. MultiBovine viral diarrhea virus (BVDV) is the pathogen of bovine viral diarrhea–mucosal disease (BVD–MD), which has caused significant economic losses for the cattle industry worldwide ,14. AccoHerpesviridae family, the Alphaherpesvirinae subfamily, and the varicella virus genus [Alphaherpesvirinae subfamily [BoHV−1 is an enveloped, double-stranded DNA virus belonging to the us genus . The virus genus . The gE us genus ,26. In aus genus ,28,29. Mus genus ,31,32,33ubfamily ,34,35.Conventional vaccines include live attenuated and inactivated vaccines, which could prevent disease but could not differentiate infected from vaccinated animals (DIVA), creating difficulties in the identification, isolation, culling, and eradication of seropositive animals. Gene deletion labeled vaccines have an immunity marking system, which can use deletion genes for the differential diagnosis of wild virus infection and vaccine immunity, which has become the mainstream direction of new vaccine research and development ,37,38. C+. Using reverse screening, a recombinant virus expressing BVDV E2 protein was obtained, named BoHV−1 gE/E2−Linker−E2+. The BoHV−1 gE gene deletion virus was named BoHV−1 ΔgE. In this study, E2 mRNA and protein expression were analyzed at the molecular level and protein level, respectively. BoHV−1 gE/E2−Linker−E2+ and BoHV−1 ΔgE mutant viruses were immunized to guinea pigs and calves, respectively, and were evaluated in terms of their safety, immunogenicity, and potential serological differentiation. In this study, we provided data on the development of gE deletion (BoHV−1 ΔgE) strains, the BoHV−1 expression of the BVDV−1 E2 protein (BoHV−1 gE/E2−Linker−E2), and the use of inactivated antigens as vaccines. Our results show that BoHV−1 gE/E2−Linker−E2+ and BoHV−1 ΔgE viruses are effective vaccine candidates for the prevention of BVDV−1 and BoHV−1 infection when used as inactivated antigens.In this study, we aimed to use BoHV−1 as a delivery vector for the BVDV−1 subunit vaccine. Initially, we obtained a recombinant BoHV−1 expressing green fluorescent protein (EGFP), named BoHV−1 gE/EGFPBoHV−1 and BVDV were isolated, identified, and stored in the laboratory of Yong-Jun Wen, College of Veterinary Medicine, Inner Mongolia Agricultural University. Bovine Kidney Cell Line (MDBK) was purchased from Kunming Cell Bank (KCB90031YJ). The African Green Monkey Kidney Cell Line (Vero E6) was donated by the Harbin Veterinary Research Institute. MDBK and Vero E6 were cultured in modified Eagle’s medium . All media were supplemented with 10% heat-inactivated fetal bovine serum  and 1% penicillin and streptomycin .BVDV−1 E2 specific monoclonal antibody (mAb) stored in the laboratory. The BVDV FITC conjugate was purchased from VMRD (CJ-F-BVD-10 mL). BVDV−1 mAb E2 gp53 IgG2a Isotype was purchased from VMRD (CATALOG #: 157). BoHV−1 positive serum was stored in the laboratory. Goat Anti-Mouse IgG H&L (FITC) and Goat Anti-Mouse IgG H&L (HRP) were purchased from abcam . MONTANIDETM ISA 206 VG adjuvant was purchased from Seppic . Propolis adjuvant was purchased from Jinhe-Youben .http://www.e-crisp.org/E-CRISP/, accessed on 4 July 2020). After denaturing, annealing, and extending, the sgRNA was connected to the pX459 vector (Addgene) to obtain pX459−gE−sgRNA1 and pX459−gE−sgRNA2. Similarly, according to the EGFP gene (GenBank: MN153298.1), two pairs of sgRNAs were designed, denatured, annealed, extended, and connected to the vector pX459 to obtain pX459-EGFP-sgRNA1 and pX459-EGFP-sgRNA2.According to the BoHV−1 gene (GenBank: AJ004801.1) of NCBI, upstream and downstream primers targeting gE genes were designed by using Primer 5.0. After sequencing analysis, two pairs of sgRNAs were designed online (According to the BVDV−1 E2 gene (GenBank: AF083348.1) of NCBI, BVDV−1 E2 protein signal peptide and transmembrane region were analyzed online using signal P 4.1 Server and TMHMM Server, V.2.0. Codon optimization was performed based on mammalian cell preference, with BoHV−1 gD signal peptide gene added at the N terminus and TAA termination codons added at the C terminus. The optimized sequence was sent to Beijing Huada Company.Kpn I and BamH I, named pCDNA 3.1−EGFP. The E2 synthesized by Beijing Huada Company was amplified into E2-Linker-E2 using the Over-Lap PCR, and the restriction enzyme sites Kpn I and BamH I were cloned into the pCDNA3.1 vector, which is pCDNA 3.1-E2-linker-E2. According to the BoHV−1 gE gene, three pairs of primers LgE-F/R, RgE-F/R, and R1gE-F/R were designed. The upstream homologous arm (LgE) was amplified by LgE-F/R. The downstream homologous arms  were amplified by RgE-F/R and R1gE-F/R, respectively. The homologous arms were cloned into pCDNA 3.1−EGFP, pCDNA 3.1, and pCDNA 3.1-E2-linker-E2 vectors to construct the donor plasmid pCDNA 3.1-LgE-EGFP-RgE, pCDNA 3.1-LgE-RgE, and pCDNA 3.1-LgE-E2-linker-E2-R1gE. The plasmids constructed above were identified using enzyme digestion and sequencing. The used primers are shown in The EGFP was amplified using PCR, and the pCDNA 3.1 vector was cloned by the restriction enzyme sites Hind III or Xba I. CRISPR/Cas9 vectors pX459−gE−sgRNA1, pX459−gE−sgRNA2 and linearized fragments were co-transfected into Vero E6 cells at 0.5:0.5:1 using Lipofectamine®LTX and Plus Reagent transfected reagent, and the control group was transfected with 2 ug pX459 plasmid. Cell culture fluid was changed after 6 h. After 12 h, BoHV−1 with MOI = 1 was inoculated for 2 h, and the Dulbecco’s modified Eagle’s medium containing 1% low melting point agarose, 1% fetal bovine serum, and 1% penicillin and streptomycin was added. After 72 h, the recombinant virus was screened by inverted fluorescence microscopy and further cloned and multiplied in MDBK cells. In the same way, using CRISPR/Cas9 shear plasmids pX459-EGFP-sgRNA1 and pX459-EGFP-sgRNA2, BoHV−1 ΔgE and BoHV−1 gE/E2−Linker−E2+ were obtained by reverse screening based on EGFP.Donor plasmids pCDNA3.1-LgE-EGFP-RgE, pCDNA3.1-LgE-RgE, and pCDNA3.1-LgE-E2-linker-E2-R1gE were linearized using +, BoHV−1 ΔgE, and BoHV−1 gE/E2−Linker−E2+, the virus was inoculated with MDBK cells to expand the culture after six generations by plaque cloning. The viral genome was extracted as a template, the BoHV-gE−F/R primers were used for PCR (including the gene sequence of the coding region and the non-coding region), and the PCR conditions included initial denaturation at 94 °C for 2 min, then 94 °C 30 s, 64 °C 30 s, 72 °C 2 min 30 s, for a total of 35 cycles, and finally extended at 72 °C for 10 min. PCR products were sequenced and analyzed. The sequencing positive recombinant viruses were purified by ten generations of plaque cloning to obtain recombinant viruses BoHV−1 gE/EGFP+, BoHV−1 ΔgE, and BoHV−1 gE/E2−Linker−E2+.To confirm the successful recombination of BoHV−1 gE/EGFP−1 to 10−10. MDBK cells were inoculated with 0.1 mL/well in 96-well plates. Then, they were placed in an incubator containing 5% CO2 at 37 °C. TCID50 was calculated using the Reed–Muench method after 4 days.BoHV−1, BVDV−1 (cp) were inoculated into a confluent monolayer of MDBK cells at MOI = 0.01, and the inoculated cells were freeze–thawed three times at 48 h for BoHV−1 and 72 h for BVDV−1 (cp). The cell supernatants were collected and continuously diluted 10-fold from 10−1 to 10−10. MDBK cells in 96-well plates were inoculated with serially diluted cell supernatant of 0.1 mL/well. At 4 days after inoculation, TCID50 was calculated using the Reed–Muench method. The multi-step growth curve of the virus was plotted by GraphPad Prism 7.0 software . All data were shown as averages of three independent experiments.A batch of 12-well plates containing confluent monolayer MDBK cells were inoculated with viruses with MOI = 0.01, and the inoculated cells were frozen thawed three times and collected at 12, 24, 36, 48, 60, and 72 h, respectively. The supernatants were collected using centrifugation and continuously diluted by 10-fold from 10+ were purified using sucrose density gradient supercentrifugation or filtered and concentrated using a 3000 MWCO Amicon Ultra-15 centrifugal filter . The concentrated viruses were adsorbed on the nickel grids for 5 min, the excess viral liquid on the nickel grids was sucked up with filter paper, the excess staining solution was stained with 2% phosphotungstic acid (pH 7.0) for 30 s, and the excess staining solution was sucked up with filter paper. Virus morphology was observed using JEOL 1230 transmission electron microscopy .BoHV−1, BoHV−1 ΔgE, and BoHV−1 gE/E2−Linker−E2+, and BVDV−1 (cp). Cell samples were collected when the cytopathic effect reached about 90%, and sample concentration was determined with a BCA protein concentration detection kit . Proteins were separated by SDS polyacrylamide gel electrophoresis and transferred to a PVDF membrane. A total of 5% skimmed milk was blocked at room temperature for 2 h. The PVDF membrane was washed three times with TBST, BVDV−1 E2 specific mAb (1:1000 dilution) was incubated at 4 °C for 10 h, and the PVDF membrane was washed three times with TBST. Goat Anti-Mouse IgG H&L (HRP) (1:5000 dilution) was incubated at room temperature for 1 h and the PVDF membrane was washed three times with TBST. The PVDF membrane was observed using ECL chromogenic solution  in a protein-scanning imager.MDBK were inoculated with BoHV−1, BoHV−1 ΔgE, BoHV−1 gE/E2−Linker−E29, 1 × 108, 1 × 107, 1 × 106, 1 × 105, and 1 × 104 were selected as templates. The reaction was performed on the applied biosystems QuantStudio 5  instrument using GoTaq®qPCR Master Mix  kit, with three replicates for each dilution. Reaction system: SYBR GreenI 10 μL, forward and reverse primers of 0.4 μL for each, DNA template 1 μL, ddH2O supplement to 20 μL. Reaction conditions: initial denaturation at 95 °C for 30 s, denaturation at 95 °C for 15 s, annealing and extension at 60 °C for 60 s, a total of 40 cycles, generating standard curves. The copy number of E2 gene in each sample was calculated based on the CT value of the standard curve and plotted by GraphPad Prism 7.0 software . All data were shown as averages of three independent experiments.The virus was inoculated into a confluent monolayer of MDBK cells with MOI = 0.01, and the inoculated cells were freeze–thawed three times and collected at 12, 24, 36, 48, 60, and 72 h, respectively. The supernatants were collected by centrifugation, and the DNA was extracted using the viral genome DNA/RNA extraction kit . The primers used in this study were designed according to the optimized sequences of E2 gene . Firstly+ (200 mL) antigens were centrifuged at 3000 r/min for 5 min and collected for preparation. The 2-bromoethylamine hydrobromide  powder (41 g) was added to the sterilized sodium hydroxide solution of 0.2 mol/L (8 g NaOH added to 1000 mL of injectable water) and fully dissolved so that the BEA concentration was 0.2 mol/L. This was placed in a 37 °C water bath and shaken every 10~15 min to make BEA fully cyclized. After 1 h, cyclization was terminated and Binary Ethylenimine (BEI) was produced at a final concentration of 0.2 mol/L. A total of 8 mL BEI inactivating agents were added to the tested BVDV solution (400 mL), with a final concentration of 0.004 mol/L, and 4 mL BEI inactivating agents were added to 200 mL of the BoHV−1 ΔgE and BoHV−1 gE/E2−Linker−E2+, with a final concentration of 0.004 mol/L. After heating to 30 °C, the inactivation started and the temperature was maintained at 30 ± 1 °C. Inactivation lasted for 28 h, and the solution was stirred once every 2~4 h, each time for 10~15 min. Immediately after inactivation, inactivated BVDV−1 was added to an 8 mL filtered solution and sterilized with 50% sodium thiosulfate solution , with a final concentration of 1%. BoHV−1 ΔgE and BoHV−1 gE/E2−Linker−E2+ were added to 4 mL of filtered sterilized 50% sodium thiosulfate solution  to make the final concentration 1%. This was fully and evenly stirred, and stored at 2~8 °C for future use. The antigens and adjuvant 206 were bathed in water at 35 °C for 20 min until the temperature of both antigens and adjuvant reached 35 °C and moved to the biosafety cabinet. The antigens and adjuvant were quickly mixed and poured into the beaker within 5 s. The beaker was covered with a sterilized cloth, magnetic stirring was performed for 15 min, and the emulsion was kept overnight at 2~8 °C to complete the first vaccine preparation. The above operations were repeated for secondary preparation.The BVDV−1 (400 mL), BoHV−1 ΔgE (200 mL), and BoHV−1 gE/E2−Linker−E27TCID50/mL. Guinea pig blood was collected to isolate serum every 7 days for 56 days. The serum was inactivated at 56 °C for 30 min and stored at −20 °C. Clinical evaluation of signs  and tissue  collection were performed within 14 days after the challenge.A total of 45 guinea pigs aged 2~3 months were divided into the immunization group (30 guinea pigs), blank control group (3 guinea pigs for simulated immunization), and challenge control group (12 guinea pigs). The immunization group was divided into five groups and each guinea pig received intramuscular immunization with inactivated antigen emulsified with propolis adjuvant 3 mL. The blank control group and the challenge control group received an intramuscular immunization with DMEM emulsified with propolis adjuvant 3 mL, with booster immunization after 21 days. After 42 days, BoHV−1 and BVDV−1 challenged in the nasal cavity (3 mL) with a viral titer of 107 TCID50/mL. Calf blood was collected to isolate the serum every 7 days for 56 days. The serum was inactivated at 56 °C for 30 min and stored at −20 °C before use. Body temperature was measured daily for 3 days before BVDV−1 challenge, and its average value was taken as the basal body temperature. Blood was collected daily to measure the number of white blood cells, and the average value was taken as the basal white blood cell number. After challenge, the calves were observed for 14 days, body temperature was measured daily, the number of white blood cells was measured, and nasal swabs were collected daily for pathogen detection. At 3 days before BoHV−1 challenge, body temperature was measured daily, and the average was taken as the basal body temperature. After challenge, the body temperature was measured, and nasal swabs were taken daily for pathogen detection. The immunized calves were euthanized 14 days after challenge, and the control group was euthanized 14 days later due to clinical severity.A total of 24 BoHV−1 and BVDV−1 double-negative calves aged 3~6 months were divided into the immunization group (15), blank control group (3), and challenge control group (six for simulated immunization). The immunization group was divided into five groups and each calf received intramuscular immunization in the neck with 4 mL of inactivated antigen. Immunization was boosted after 21 days. After 42 days, BoHV−1 and BVDV−1  were challenged in the nasal cavity (8 mL) with a viral titer of 10On immunization day (0 days), day 21, and day 42, the rectal temperature, feed, and water intake of guinea pigs and calves were clinically assessed. After the challenge, clinical symptoms were recorded daily for 14 days. Clinical evaluation included rectal temperature, nasal and eye secretions, difficulty breathing, cough, drowsiness, anorexia, and diarrhea. The clinical score for each animal was calculated based on the criteria listed in Percentage of lung peribronchiolar/bronchial infiltrates: 0 = none, 1 = little (less than 25%), 2 = many (25–75%), 3 = all (greater than 75%). Lung bronchiolar/peribronchiolar infiltrates: 0 = none , 1 = mild , 2 = moderate (full ring or crescent ring), 3 = severe (complete ring). Interstitial pneumonia symptoms: 0 = none, 1 = mild, 2 = moderate, 3 = severe. Small intestine: 0 = intestinal villi and epithelium are intact and histology is normal, 1 = slight submucosa or lamina propria swelling and separation, 2 = moderate submucosa or lamina propria separation, submucosa or basal edema, 3 = severe submucosa or lamina propria edema, local necrosis and exfoliation, 4 = disappearance of intestinal villi with intestinal necrosis.After euthanizing guinea pigs and calves, the muscle tissue of immunization sites, lung, and small intestinal tissue were collected and fixed in 10% buffered neutral formalin for 12 h. Depending on the tissue, different concentrations of ethanol  were used for dehydration treatment for 30 min, and the ethanol and xylene mixture (1:1) liquid was transparently treated. The tissue was treated overnight with paraffin and xylene mixture (1:1) embedded in paraffin. After cutting the tissue into 7 μm sections and staining with hematoxylin and eosin, the pathological and the morphological changes in each tissue were observed under ZEISS microscopy .50 respectively, with a final volume of 200 μL of samples, and incubated at 37 °C for 1 h. MDBK were inoculated with a 100 μL neutralizing mixture. Four replicates of each sample were made at 37 °C, with 5% CO2 for 2 h, and replaced by DMEM containing 2% FBS for 4 days to observe the cytopathic effect. The 50% neutralizing titer of serum was calculated using the Reed–Muench method.Serum was heat-inactivated at 56 °C for 30 min, diluted two times consecutively in DMEM, and added to equal volumes of DMEM diluted BVDV−1 or BoHV−1 containing 200 TCIDThe BVDV−1 Antibody Detection Kit  and the BoHV−1 gB Antibody Detection Kit  were used to detect virus specific antibodies. All procedures were following the manufacturer’s instructions. BVDV was determined by S/P ratio ≥0.3 as a positive sample and S/P ratio ≤0.5 as a negative sample. BoHV−1 was determined as a positive sample with a blocking rate of ≥55 and a negative sample with a blocking rate of <45.To calculate white blood cells in an EDTA whole-blood sample, an automatic hematology analyzer  was used. Blood was collected 3 days  before the challenge and 1~14 dpc, and the total number of white blood cells was measured and recorded. The average value was taken 3 days  before challenge as the base number of white blood cells. In addition, a drop in the white blood cell count of more than 15% for a guinea pig and a drop of more than 25% in the white blood cell count for a calf was considered leukopenia .p <  0.05 was considered significant.The experimental data were expressed as mean ± standard deviation. The differences were analyzed using the ANOVA test. Hind III or BamH I) with Vero E6 cells in a ratio of 0.5:0.5:1, and BoHV−1 was inoculated after 12 h. After 72 h, the cytopathic effect and EGFP expression were observed using inverted fluorescence microscopy. The presence of the cytopathic effect and green fluorescence signal in cells was considered to show EGFP positive recombinant viruses, and the suspected virus was harvested and generated on MDBK cells, which were observed again 48 h later using an inverted fluorescence microscope. EGFP-positive recombinant virus was performed for six generations by plaque cloning  was inoculated using MDBK cells. These were verified using a BVDV−1 E2 specific mAb, and we found that BoHV−1 successfully expressed the BVDV−1 E2 gene as a vector  and cell lysates were collected at 12, 24, 36, 48, 60, and 72 h after inoculation, using the above method to verify the expression of E2-linker-E2. The results showed that the expression of E2-linker-E2 could be vaguely detected 24 h after inoculation, and E2-linker-E2 expression accordingly increased as the time of infection increased and peaked at 60 h (+ (MOI = 0.01) after inoculating MDBK at 12, 24, 36, 48, 60, and 72 h. The results showed that the number of E2-linker-E2 copies also increased with time, reaching a peak at 60 h, consistent with protein levels , day 8 (7.53%), and day 9 (11.83%); the reduction was within 15%. The number of white blood cells on day 10 increased by 3.23%, showing no difference from the blank control group, and the number of white blood cells decreased from 8.566 × 109 to 7.1 × 109 on day 7, remaining within the normal range. After the BVDV−1 challenge, mild and transient leukopenia occurred in guinea pigs in the challenge control group, while no leukopenia occurred in all immunization groups.The total number of white blood cells was measured and recorded 3 days before the challenge  and 14 days after the challenge. According to the criteria for a percentage decrease in white blood cell count described in the Materials and Methods section, the results showed that two guinea pigs in the challenge control group had symptoms of leukopenia from days 4~6 G. The pe+  had an increase in body temperature on day 2, and the body temperature of another (#105) started to increase on day 4, reaching 40.7 °C in day 7 . Nasal swabs were taken from calves in the above immunization groups for antigen detection using BoHV−1 gE−F/R primers and the challenge control group was positive for PCR on day 2. However, until day 12, BoHV−1 was not detected  had an elevated body temperature on day 1, and the other two calves had an elevated temperature on day 4, had watery diarrhea on day 5, and on day 10, the symptoms were reduced, lasting for about 5 days . Anal swabs were taken for calves in the above immunization groups for antigen detection using 5′ UTR−F/R primers, and the maximum viral shedding time of one of the calves in the challenge control group was found to be 12 days. The maximum viral shedding time in the BVDV−1 and BoHV−1 gE/E2−Linker−E2+ immunization groups was approximately 6 days  in the BoHV−1 gE/E2−Linker−E2+ immunization group was found to have BVDV−1 in day 11, while BVDV−1 could not be detected in the remaining calves on day 11, as in the BVDV−1 immunization group  and day 8 (32.91%), lasting until day 9, at which point the calf returned to normal and there was no downward trend on days 9~12. The remaining calves did not differ from the blank control group, and the number of white blood cells decreased from 7.3 × 109 to 7.0 × 109 on day 7, within the normal range. In summary, inoculating BoHV−1 gE/E2−Linker−E2+ can protect calves from BVDV−1 infection.The total number of white blood cells was measured and recorded 3 days before the challenge  and 14 days after the challenge. The results showed that the calves in the challenge control group all had symptoms of leukopenia. One calf showed a decreasing number of white blood cells on days 4, 5, 6, 9, 10, and 11, and another showed a decrease in the number of the white blood cells on days 5, 6, 7, 9, 10, 11, and 12, and the last calf showed a decrease in the number of white blood cells on days 4, 5, 6, 7, and 8. It is worth noting that the maximum percentage decrease in white blood cells in the challenge control group of calves was in the range of 25.52%~54.72%, and the average percentage decrease in the group was 35.38% G. AccordInactivated antigen immunized in the neck of calves, and muscle tissue was taken from the immune site on day 4 of immunization. The results showed that the blank control group had normal muscle cell structure, striations, and no inflammatory cell infiltration A. In the+  vaccine, external environmental stress can cause the virus to reactivate and detoxify from the nasal cavity, causing miscarriages in the dairy industry (BoHV−1), respiratory diseases in the beef cattle and dairy industry (BoHV−1 and BVDV), and persistent infections in the dairy industry (BVDV). Therefore, only BoHV−1 labeled vaccines lacking the virulence gene gE are allowed in several EU countries. Under pressure from the external environment, when using a BoHV−1 labeled vaccine that lacks the virulence gene gE, the virus does not spread from the nasal cavity of the challenged animal after it is reactivated during the incubation period. The use of live attenuated BVDV as a vaccine is suspected to cause problems associated with wtBVDV infection in the cattle industry ,52,53, m+ and BoHV−1 ΔgE, both of which deleted the gE virulence gene. In addition, BoHV−1 gE/E2−Linker−E2+ at the gE gene location was inserted into our optimized BVDV−1 E2 gene and combined with the BoHV−1 gD signal peptide sequence. The final protein expression of BoHV−1 gE/E2−Linker−E2+ can reach 66% of that of BVDV−1. In this study, two E2 proteins were expressed in tandem by codon optimization and the addition of signal peptide sequences, which increased the expression of E2 proteins to a certain extent. In addition, Xu et al. used a prokaryotic expression system to express the E2 protein of the Changchun 184 strain, and the expressed protein accounted for 6.25% of the bacterial protein, but the E2 protein expressed after removing the transmembrane region of the E2 gene accounted for 35.7% of the bacterial protein. The results showed that BoHV−1 could highly express BVDV E2 protein. Similar to the findings of Schmitt et al., pestivirus glycoproteins can be efficiently expressed and incorporated into the viral envelope by BoHV−1, which is a suitable vector for expressing BVDV E2, and the conformation is similar to the E2 protein expressed by BVDV [+ allowed guinea pigs to produce serum-neutralizing antibodies against BVDV−1 infection. Similar to previous studies, Bhuyanaa et al. constructed a recombinant Lactobacillus casei pELX1-E2 to express the E2 gene of BVDV and evaluated the immune response elicited in mice, revealing that the recombinant strain could induce high levels of E2-specific antibodies [+ immunization group was significantly different from that of the BVDV−1 immunization group. We speculate that the inactivated virus does not have replication properties, which makes the protein amount of BoHV−1 gE/E2−Linker−E2+ different from that of BVDV−1. We found that the final neutralizing antibody produced by BoHV−1 gE/E2−Linker−E2+ immunization group was significantly different from that of the BVDV−1 immunization group, but still protected guinea pigs from BVDV−1 infections , the body temperature of the other calves in the immunization groups did not increase. In addition, combined with the results of lung pathology sections, we found that lung lesions were significantly reduced in calves immunized with BoHV−1 ΔgE and BoHV−1 gE/E2−Linker−E2+  and the BoHV−1 ΔgE gene deletion vaccine had the advantages of safety and a remarkable effect on the prevention of BVDV−1 and BoHV−1. In our study, the results demonstrate that tandem expression of the two optimized E2 proteins, in combination with the gD gene signal peptide, can increase the expression of the BVDV−1 E2 protein, protecting calves from BVDV−1 infection. Therefore, we believe that the BoHV−1-gE/E2-linker-E2+ genetically engineered subunit vaccine will be a substitute to the current BoHV−1 and BVDV−1 prophylaxis as a conventional bivalent vaccine.At present, live polyvalent vaccines that are modified against BoHV−1 and BVDV have limitations in terms of their safety and efficacy. In this study, the gene-engineered vector subunit vaccine (BoHV−1 gE/E2−Linker−E2


Herein, MTX acted in a versatile manner as both a morphology-regulating agent and a small molecular hydrophobic anticancer drug. Moreover, different morphologies presented diverse drug release profiles, which may be caused by the distinctive interactions between PAH and MTX molecules. Synergistically non-covalent interactions, including electrostatic interactions, van der Waals forces, and hydrogen bonding, favored easier matrix corrosion and more rapid drug release of non-spherical structures  through the ligand exchange process. On the other hand, the highly sealed encapsulation mode for hydrophobic MTX molecules made the nanospheres exhibit slower and better controlled release. In addition, in vitro bioassay tests showed that nanostrips displayed the most obvious suppression on the viability of cancer cells among other morphologies, especially after a longer duration. The strategy of using small molecular anticancer drugs not as passively delivered cargoes but as effective molecular building blocks, opens up a new way to develop self-delivering drugs for anticancer therapy.Poly–methotrexate (PAH–MTX) nanoassemblies with novel morphologies ( Nanospheres exhibited the best controlled drug release and nanostrips displayed the most obvious suppression of A549 cells. It has been demonstrated that disc-shaped NPs possessed longer systemic circulation time and higher specific bio-distribution than their spherical counterparts,2–4 and that rod-shaped5,6 or worm-shaped particles7,8 exhibited higher specific and lower nonspecific accumulation than spherical NPs. Moreover, rod-like NPs with higher aspect ratios possessed much faster cell internalization than that of the lower ones.9 Since non-spherical NPs presented many impressive advantages, more and more nanocarriers, such as gold NPs,10 Magnetic NPs.11 silicon dioxide,12 and polymeric nanoassemblies,13 have been developed with diverse morphologies to evaluate their biological application in vitro and in vivo.For drug delivery systems, nanoparticle morphology has become a highly attractive research area especially for cancer therapy since it plays critical roles in regulating payload the release, phagocytosis, cell internalization, pharmacokinetics, and bio-distribution of particles.via molecular amphiphiles, have emerged as a kind of helpful and effective system to deliver small molecular drugs owing to their unique properties, such as high loading capacity, increasing solubility of the hydrophobic drugs, long circulation time, stimuli-responsive characteristics, and favorable enhanced permeability and retention (EPR) effect.1 Based on that, multiple morphologies of the polymeric nanocarriers, including nanospheres,2,3,14–17 nanowires,3,18 nanorods,14 nano disks,2,19 multicompartment micelles,20 and vesicles,3,21–23 have been developed and presented unexpected virtues during the drug delivery process. As well known, multiple non-covalent interactions have been well engineered to create hierarchical structures, highly complex systems, and functional entities at all scales.24–26 Self-assembly system driven by the non-covalent interactions such as van der Waals forces, hydrogen bonding, electrostatic forces, hydrophobic interaction, π–π stacking interaction, and host–guest interaction, may show great potential to create new nanostructures.The nanoassemblies formed by self-assembling multi-structured polymers, especially  PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.dbd" format="PBM" resolution="300" x-size="8" y-size="10" xml:space="preserve">00000000000000000000000000000000111111110000000011111111000000000000000000000000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="13.200000pt" height="16.000000pt" viewBox="0 0 13.200000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata>Created by potrace 1.16, written by Peter Selinger 2001-2019</metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>N–) and then promoting the formation of diverse nanostructures by molecular self-assembly, such as core–shell PAH–pyrene nanorods,27 nanotubes protruding PAH-graft-Py microcapsules,28 PAH-g-porphyrin nanocapsules , and wormlike nanostructures (WSs)).29 Most typically, PAH was often used to construct multilayers or nanocapsules with poly(sodium 4-styrene sulfonate) (PSS) through the layer-by-layer assembly based on the electrostatic interaction.30–37 The resultant PAH nanocapsules also presented biocompatibility, non-toxicity, good encapsulation and controlled release behavior, thus being widely used for gene and drug delivery purposes as well as other biomedical applications. Since many drug molecules were passively encapsulated into the inner side of the pre-assembled nanocapsules, no effort has yet been devoted to construct highly efficient nanomedicines via self-assembly directed by the integration of multiple non-covalent interactions between PAH and cargo molecules. The overall balance of these forces may induce interesting and surprising properties for nanostructures. Indeed, the functional primary amine groups and cationic character could open more opportunities for the non-covalent self-assembly between PAH and cargo molecules.Polyallylamine hydrochloride PAH,  possessei.e. nanostrips, nanorolls, nanosheets, and nanospheres, i.e. nanostrips, nanorolls, and nanosheets) through the ligand exchange process and then a rapid drug release while the highly sealed encapsulation mode for hydrophobic MTX molecules made the nanospheres exhibited a slower and better controlled release. Finally, in vitro bioassay tests showed that nanostrips displayed the most obvious suppression on the viability of cancer cells than other morphologies, especially after longer duration.In the present work, we adopt the supramolecular self-assembly strategy directed by the synergistic action of various non-covalent interactions between PAH and MTX molecules in aqueous solution to develop four kinds of well-defined novel morphologies of poly–methotrexate (PAH–MTX) nanoassemblies (2.2.1l-Ascorbic acid (l-AA) and ammonia solution (NH3·H2O) were purchased from Sinopharm Chemical Reagent Co., Ltd. . Methotrexate (MTX) was purchased from Huzhou Prospect Pharmaceutical Co. . Dialysis membrane (molecular weight cut-off = 8000–14 000 Da) was purchased from Shanghai Green Bird Technology development Co., Ltd. Human lung adenocarcinoma cells (A549) were purchased from Shanghai Cell bank . All reagents were of analytical grade and used without further purification. All the aqueous solutions were prepared using Milli-Q water with a resistivity of 18.2 MΩ .Polyallylamine hydrochloride  was supplied by Nitto Boseki Co., Ltd. . 2.2−1, molarity of PAH given with respect to the repeating unit) was added into 9.6 mL of deionized water under continuous stirring, together with adding l-AA . Then the mixture (pH 3.5) was treated at 40 °C, 60 °C, 80 °C, respectively. Under each temperature gradient, freshly prepared solutions of MTX/NH3·H2O  with different concentrations  were added into the mixture under vigorous stirring for 4 h. After being cooled to room temperature, the corresponding PAH–MTX nanoassemblies were obtained and named as samples a1, a2, a3, b1, b2, b3, c1, c2, c3, d1, d2, and d3. The samples were then stored at 4 °C.In a typical synthesis, PAH  were added into 9.6 mL of deionized water under continuous stirring at 40 °C. Then freshly prepared solutions of MTX/NH3·H2O  with different concentrations  were added into the PAH solution (pH 3.7) under vigorous stirring for 4 h. After being cooled to room temperature, the corresponding PAH–MTX nanoassemblies were obtained and named as samples e1, e2, e3, and e4. The samples were then stored at 4 °C.In a typical synthesis, PAH  were added into 9.6 mL of deionized water under continuous stirring at 40 °C, together with adding l-AA (10 mg mL−1) for different amounts . The pH values of the resulting mixtures were 3.4, 3.4, 3.3, respectively. Subsequently, freshly prepared solution of MTX/NH3·H2O  were added into the mixtures under vigorous stirring for 4 h. After being cooled to room temperature, the corresponding PAH–MTX nanoassemblies were obtained and named as samples f1, f2, and f3. The samples were then stored at 4 °C.In a typical synthesis, PAH  and then diluted to 500 mL in volumetric flask. The concentration of MTX was measured by UV-vis spectroscopy at 306 nm. The loading capacity (LC) and encapsulation efficiency (EE) were calculated according to the following equations:The data were collected in triplicate and are presented in 2.6−1, pH 7.4) as the release medium. An appropriate amount of PAH–MTX nanoassemblies were weighed and dispersed in PBS to obtain a final concentration of MTX of 2 mg mL−1. Then a fraction of the prepared solution (1 mL of) was added into a dialysis membrane bag (MWCO = 8000–14 000) and immersed in PBS (100 mL). The system was shaken at a speed of 150 rpm at 37 °C. At desired time intervals, 3 mL of sample was withdrawn and replaced with an equal volume of fresh medium. As a control experiment, the release of free MTX from the dialysis bag was measured. All samples were measured by UV-vis spectroscopy at λmax = 306 nm. At last, the release profiles were plotted as the relative release percentages of MTX against time. These data were collected in triplicate and are presented in The release profiles of PAH–MTX nanoassemblies were studied by the dialysis with PBS (10 mmol L2.7In vitro bioassays were undertaken with human lung adenocarcinoma cells (A549). Cells were cultured at 37 °C in a humidified atmosphere containing 5% CO2 in 75 cm2 flasks charged with Dubecco's modified eagle medium  and supplemented with 10% (v/v) fetal bovine serum (FBS), penicillin , and streptomycin . When the cells reached 80–90% of cellular confluence, the fault culture cells were differentiated with trypsin–EDTA and then washed twice with freshly prepared PBS (pH 7.4). The cells were then diluted with a volume of DMEM containing 10% (v/v) FBS. For cell proliferation and viability study, cells were seeded onto 96-well plates. The cells were then incubated overnight at 37 °C under CO2 atmosphere. After that, the medium in the wells was replaced with fresh medium containing the examined samples (i.e. free MTX or PAH–MTX nanoassemblies). The anticancer effect of all samples was examined using the 3--2,5-diphenyltetrazolium bromide (MTT) assay at the drug concentration of 100 μg mL−1 after 24 h and 48 h of incubation. Simply, the supernatant was removed, followed by the addition of MTT stock solution  and DMEM (90 μL) in the absence of FBS into each well. After incubation for 4 h at 37 °C, the supernatant was discarded and DMSO (100 μL) was added into each well. The mixture was shaken before recording the absorbance at 570 nm using a microplate reader . Cell viability was calculated using the following formula:382.8−1 using KBr pellets (with a mass ratio of sample to KBr being 1 : 100), and the resolution of the instrument was 4 cm−1. The UV-vis spectra were recorded at room temperature on a UV3600 spectrophotometer  equipped with 1.0 cm quartz cells. The X-ray diffraction (XRD) patterns were obtained with a D/max-2500PC rotating anode X-ray powder diffractometer (Rigaku Co.), using Cu Kα radiation (λ = 1.5406 Å) from 5° to 45° at a scanning rate of 1° min−1. Zeta potential analysis was performed with a PALS Zeta Potential Analyzer, Version 3.43 (Brookhaven Instruments Co.). The morphology change of A549 cells was observed with a light microscope . The cells were initially seeded into cell culture dishes (35 mm × 10 mm) in DMEM (1 mL) medium with 10% (v/v) FBS. After 24 h for cell attachment, the medium in the wells was replaced with DMEM, free MTX, and sample a4 for another 24 h, 48 h or 72 h, and the cells were observed using a light microscope with 10× objective lens.Transmission electron microscope (TEM) images were obtained using a H-7650-HITACHI  machine operating at 200 kV. Samples for TEM were prepared by depositing a drop of sample solution on carbon-coated copper grid and dried at room temperature. Fourier transform infrared spectroscopy (FTIR) spectra were recorded on a Bruker Tensor 27 spectrometer in the wavenumber region of 400 to 4000 cm3.3.139–41 As observed, PAH–MTX nanoassemblies at 40 °C changed from the nanorolls  were 3.7, 3.5, 3.4, 3.4, and 3.3, respectively, indicating very small pH changes of the initial reaction solutions. Hence, the possibility could be ruled out that various pH induced different assembly behaviors.43 Considering many active hydrogen-bonding sites characterizing l-AA  were illustrated in −1, 1548 cm−1 and 1450 cm−1, respectively, which were assigned to the stretching vibration of the CO group, amide II N–H bending mode and carbon–carbon stretching vibrations in benzene ring of MTX, respectively.53 These observations indicated the successful loading of MTX into PAH–MTX nanoassemblies. As for PAH, two strong broad absorption peaks at 3484 cm−1 and 3419 cm−1 corresponded to the N–H asymmetric and symmetric stretching modes of the primary amine groups. As for PAH–MTX nanoassemblies, disappearance of the corresponding double peaks and appearance of single peak at the relatively low wave number between 3398 and 3427 cm−1, pointed out the strong hydrogen-bonding or ionic interaction between –NH2 groups and other polar groups.53–55 This observation may reveal the occurrence of molecular assembly and then the subsequent formation of different nanoassemblies.The FTIR spectra of the typical nanostrips, nanorolls, nanosheets and nanospheres (n → π* (enone) transitions, respectively.56,57 Obviously, PAH presented no characteristic absorption from 200 nm to 800 nm. As for the PAH–MTX nanoassemblies, samples a2 and b1 exhibited two distinct peaks both at 261 nm and 306 nm, presenting a small red shift of 5 nm and 13 nm. Meanwhile, samples c2 and d1 exhibited two distinct peaks both at 265 nm and 309 nm, presenting a small red shift of 9 nm and 16 nm, respectively. These different red-shifts may be caused by the synergistic non-covalent interactions, including electrostatic interactions, hydrogen bonding, hydrophobic interaction, and van der Waals forces.56As for UV-vis measurements , pure MT2, b1, c2 and d1 were shown in θ of 7.6 and 9.2, characteristic of its trihydrate form.58,59 It was well-known that the width of X-ray diffraction peak was related to the size of crystallite and the broadened peak usually results from imperfect crystal. Hence, the broad peak of PAH indicated a amorphous phase. Compared to free MTX and PAH, however, almost no crystallinity was observed from different PAH–MTX nanoassemblies, indicating a fully molecular dispersion of MTX into the polymeric matrix or amorphization during drying.59,60Generally, drug distribution into particles was directly correlated with drug release rate and bioavailability, therefore being an important parameter in the development of nanomedicine. The XRD patterns of free MTX, PAH, samples a3.32, b1, c2 and d1 were 90.84 ± 0.45, 88.94 ± 0.33, 91.08 ± 0.54, and 86.68 ± 0.25, respectively. Moreover, their encapsulation efficiencies were 72.67 ± 0.25, 81.28 ± 0.45, 76.28 ± 0.35, and 59.81 ± 0.39, respectively. The data showed that different PAH–MTX nanoassemblies presented high and approximate loading capacities but distinct encapsulation efficiencies. Compared with other shapes, the nanospheres exhibited the lowest encapsulation efficiency, indicating that increasing MTX did not evidently enhance drug loading capacity but apparently regulate the morphology evolution.42,43 In addition, highly positive zeta potentials suggested that hydrophilic amino groups exposed on the surface of the nanoassemblies.Drug loading capacities of all samples were listed in in vitro release profiles of MTX from samples a2, b1, c2 and d1 were investigated and illustrated in the 61 Notably, different morphologies of nanoassemblies presented diverse release profiles,62,63 which may be due to the different interactions between PAH and MTX. Release rates of the four samples within 200 min were 61.11%, 62.26%, 77.55%, and 62.46%, respectively. Within 500 min, the release rates reached to 85.77%, 83.56%, 89.27%, and 71.41%, respectively. As well known, PAH was a positively charged polyelectrolyte with strong hydrophilicity while MTX was a hydrophobic drug but possessed negative charge in basic solution. As for sample d1, MTX molecules as hydrophobic parts were mainly encapsulated into the inner side while the hydrophilic amino groups of PAH exposed outside. The highly sealed and spherical structure caused a finally lower rate of matrix erosion and then a slower drug diffusion.64 The initially fast release rate may be due to the fact that many MTX molecules also physically adsorbed on the surface of nanospheres based on the electrostatic interaction. As for samples a2, b1, c2, positively charged PAH chains and negatively charged MTX molecules cross-linked into regular nanoassemblies based on the mainly electrostatic self-assembly as well as other non-covalent interactions . Accordingly, it was more favorable for the exchange process of negative ligands to induce matrix erosion of PAH–MTX nanoassemblies and then a fast drug diffusion.65 Consequently, these samples presented higher release rates after 500 min.Meanwhile, typical 66 To gain more insights into the mechanism of drug release, four equations were often applied:67 the first-order equation  dissolution of PAH–MTX nanoassemblies; (2) ion-exchange process between MTX-containing nanoassemblies and the phosphate anions in buffer solution.equation , the Higequation , the Bhaequation , the Ritequation , see theR) and n values obtained from the fittings were summarized in R = 0.95–0.99, indicating that these two models can better describe the release mechanism. For all samples, Bhaskar equation fitted the release data best, indicating that the release process mainly belonged to intraparticle-controlled diffusion. Additionally, the R–P equation were used to explain drug diffusion and dissolution of polymeric matrix. The value of n < 0.45 corresponded to the drug diffusion control, which was based on ion exchange process; n > 0.89 was attributed to the dissolution of PAH–MTX nanoassemblies; 0.45 < n < 0.89 was due to the cooperation of PAH–MTX nanoassemblies dissolution and drug diffusion. The release process of all samples with release value of n between 0.5842 and 0.7969 (0.45 < n < 0.89) belonged to the combination behavior control, which can be based on the fact that the MTX anions diffused from the inner side of PAH–MTX nanoassemblies to the surface of particles accompanying the matrix corrosion of the nanoassemblies.On the basis of the four different kinetic models, the fitting results of drug release profiles were given in 3.42, b1, c2 and d1 measured at different concentrations after 24 h of incubation. The data revealed that samples a2, b1, c2 and d1 presented obvious suppression efficiency on the A549 cells when the concentration of PAH–MTX nanoassemblies increased while the cell viability of the samples treated with free MTX remained almost the same. Thus, the PAH–MTX nanoassemblies were more efficient than free MTX in the suppression of the cancer cells. Two factors could account for the better suppression. One was that the electrostatic attraction between positively charged PAH–MTX nanoassembelies and negatively charged cell membrane were more favorable for cell adhesion and internalization towards the nanoassembelies.30,68,69 The other may be due to the receptor-mediated endocytosis of PAH–MTX nanoassemblies, where Janus-like MTX acted as an early-phase targeting ligand coordinated to a late-phase anticancer drug.14 Notably, under the same drug concentration, different morphologies of PAH–MTX nanoassemblies exhibited differences in cell viabilities. Relatively, nanostrips presented strongest suppression towards the cancer cells while nanosheets showed the weakest. This may be due to the fact that strip structure was more favorable for the cell endocytosis compared with the nanosheets.9,70 The −1. The cell viability of all samples constantly decreased as the incubation time increased, suggesting a controlled and sustained drug release process.Cell cytotoxicity of the PAH–MTX nanoassemblies was evaluated by the bioassay test using A549 cells and was presented in 2 were displayed in 2  via supramolecular self-assembly strategy directed by the synergistic action of multiple non-covalent interactions between PAH and MTX molecules in aqueous solution. Herein, MTX acted versatilely as both a morphology-regulating agent and a small molecular hydrophobic anticancer drug. Moreover, non-spherical structures  favored easier matrix corrosion and then a rapid drug release through the ligand exchange process while the nanospheres exhibited a slower and better controlled release due to the highly sealed encapsulation mode for hydrophobic MTX molecules. In addition, the nanostrips displayed the most obvious suppression on the cancer cells among all the morphologies, especially after longer duration. Finally, this strategy can be extended to construct nanostructures of other types of anticancer drugs and thus presents new opportunities for the development of self-delivering drugs for anticancer therapy.In summary, we have successfully developed four kinds of well-defined novel morphologies of PAH–MTX nanoassemblies (There are no conflicts to declare.


Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Structural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution. PLOS Computational Biology Methods paper.This is a Identifying structural features associated with macromolecular dynamics is crucial to our understanding of the underlying physical behavior of proteins and their broader impact on biology and health. Structural and dynamical properties of proteins often serve as signatures of their functions and activities . Subtle Conventionally, the structural dynamics of proteins have been modeled using molecular dynamics (MD) simulations, which work by sampling structural ensembles from conformational landscapes. In infinite timescales, such structural ensembles are expected to represent all physical states such that their ensemble-averaged observables converge to true physical values and are thus physically meaningful. While MD simulations have provided key insights into the atomistic motions that underpin many protein functions , biologiA to protein B must be differentiable). There are many scenarios in protein dynamics where no such transformation is guaranteed because atomic features can be gained or lost during the evolution of the system  with the natural interpretation of providing relative evidence of association for each DEC statistic . From a classical hypothesis testing point-of-view, the null hypothesis for There are two key takeaways from this scaled formulation. First, the KLD is non-negative, and it equals zero if and only if the posterior distribution of β . In this setting, the term −log(|Σj−Λ−j|) + tr(Σj−Λj−) + (1 − p) remains relatively equal for each feature j and makes a negligible contribution to the entire sum. Thus, we simplify In practice, we use a few approximations to scale the otherwise computationally expensive steps in Λ. For a large number of topological features J, this calculation is expensive; however, it is only done once and and can be done with efficient matrix decomposition. The rate of change parameter j-th topological feature. This requires inverting a (J − 1) × (J − 1) matrix J times. Fortunately, we can reduce this computational burden by taking advantage of the fact that any j-th row and column from the precision matrix Λ. Therefore, given the partition in This approximation of the KLD still relies on the full precision matrix σj is the j-th column from the posterior covariance matrix Σ, and each j-th row and column from Ωj) operations which can be parallelized.Here, γ for each topological feature computed via reconstruction algorithm . A maximum of 100 represents the threshold at which the first atom of the protein is reconstructed, while 0 denotes the threshold when the last atom is reconstructed. Annotated are regions of interest (ROIs) according to literature sources that have previously suggested some level of structural association for each chemical change of interest, including: (i) the Ω-loop (residues 163–178) in TEM; (ii) the flap region (residues 47–55) in HIV-1 protease; and (iii) Domain 2 (residues 208–308) in EF-Tu.In these analyses, we compare the molecular dynamics (MD) trajectories of alternative states for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S6 Fig(i) Met290Val Abl1 and (ii) unbound Importin-β, respectively. We analyze Abl1 based on different fragments the protein. Specifically, we analyze (A) the whole protein structure, (B) residues 242–502, and (C) residues 242–315. Here, consistency between fragments within a protein type shows the robustness of SINATRA Pro to identify the same signal even when it does not have access to the full structure. The heatmaps highlight the atomic evidence potential on a scale from [0–100]. A maximum of 100 represents the threshold at which the first atom of the protein is reconstructed, while 0 denotes the threshold when the last atom is reconstructed. Annotated are regions of interest (ROIs) according to literature sources that have previously suggested some level of structural association for each chemical change of interest, including the DFG motif in Abl1. Note that, in the context of Importin-β, the superhelix includes the entire structure and so we do not include any additional annotations.In these analyses, we compare the molecular dynamics (MD) trajectories of the alternative states for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S7 Figβ-lactamase versus the Arg164Ser mutant  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) whole protein, (B) fragment 65–230, and (C) fragment 65–213. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the molecular dynamics (MD) trajectories of wild-type TEM r mutant , 22. Forysis see  with the(PNG)Click here for additional data file.S8 Figβ-lactamase versus the Arg164Ser mutant  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). This figure depicts results after applying SINATRA Pro using parameters {r = 6.0Å, c = 20, d = 8, θ = 0.80, l = 120} chosen via a grid search. We compare these results to the analyses with chains A and B presented in the main text. Here, the signal in the region of interest  persisted in the dimeric form, but is overshadowed by the noise in the other parts of the protein since the relative orientation between the two monomers causes each chain to be misaligned with itself. Highlighted are residues for regions of the protein corresponding to the fulcrum (brown), elbow (purple), flap (blue), cantilever (red), and I/V50 (yellow)  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) chain A and (B) chain B, respectively. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the molecular dynamics (MD) trajectories of wild-type HIV-1 protease versus the Ile50Val mutant  , 31, 32.ysis see  with (A)(PNG)Click here for additional data file.S11 FigtMD =  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). This figure depicts results after applying SINATRA Pro using parameters {r = 6.0Å, c = 20, d = 8, θ = 0.80, l = 120} chosen via a grid search. The heatmaps in panels (A)-(C) highlight residue evidence potential on a scale from [0–100]. A maximum of 100 represents the threshold at which the first residue of the protein is reconstructed, while 0 denotes the threshold when the last residue is reconstructed. Panel (A) shows residue-level evidence potential when applying SINATRA Pro to the whole protein, while panels (B) and (C) illustrate results when strictly applying the SINATRA Pro pipeline to atoms in residues 208–308 and 311–405, respectively. Panel (D) shows the association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (E) whole protein, (F) fragment 208–308, and (G) fragment 311–405.In this analysis, we compare the molecular dynamics (MD) trajectories of GTP-bound EF-Tu versus GDP-bound EF-Tu , 87, 88.ysis see . Here, t(PNG)Click here for additional data file.S13 FigtMD =  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) whole protein, (B) fragment 208–308, and (C) fragment 311–405. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the the molecular dynamics (MD) trajectories of GTP-bound EF-Tu versus GDP-bound EF-Tu , 87, 88.ysis see  with the(PNG)Click here for additional data file.S14 FigtMD =  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis (see (A) whole protein, (B) fragment 242–502, and (C) fragment 242–315. The overlap of lines shows the robustness of SINATRA Pro to identify the same signal regardless of trajectory length.In this analysis, we compare the the molecular dynamics (MD) trajectories of wild-type Abl1 kinase domain versus the Met290Val mutant , 89–91. ysis see  with the(PNG)Click here for additional data file.S16 FigtMD =  × 1.5 ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 3000 proteins in the study: 150 ns long interval × 15 different choices δ = {0.0, 0.1, 0.2, …, 1.4} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization  ns + δ, where δ = {0.0, 0.15, 0.3, …, 1.35} ns is a time offset parameter. Panels (A)-(C) show the mean association metrics (and their corresponding standard errors) computed for each residue within each analysis  trajectories of IBB-bound Importin-portin-β –47. For ysis see . The ove(PNG)Click here for additional data file.S18 Figβ versus unbound Importin-β  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). This figure depicts results after applying SINATRA Pro using parameters {r = 6.0Å, c = 20, d = 8, θ = 0.80, l = 120} chosen via a grid search. The heatmap in panels (A) highlights residue evidence potential on a scale from [0–100]. A maximum of 100 represents the threshold at which the first residue of the protein is reconstructed, while 0 denotes the threshold when the last residue is reconstructed. Panel (B) plots the correlation between the SINATRA Pro association metrics and the root mean square fluctuation (RMSF) for all atoms with correspondences. Panel (C) shows the SINATRA Pro association metrics (and their corresponding standard errors) computed for each residue within the analysis  trajectories of IBB-bound Importin-portin-β –47. For (PNG)Click here for additional data file.S19 Figβ versus unbound Importin-β  ns + δ, where δ is a time offset parameter). Altogether, we have a final dataset of N = 2000 proteins in the study: 100 ns long interval × 10 different choices δ = {0.0, 0.1, 0.2, …, 0.9} ns × 2 phenotypic classes (wild-type versus mutant). To generate these results, we first concatenate the -coordinates of all atoms within each protein and treat them as features in a data frame. Next, we use Elastic Net regularization [(A) shows the mean absolute coefficient of all atoms within each residue computed over each fragment-based analysis (see (B), we plot the correlation between the SINATRA Pro association metrics and the Elastic Net coefficients for all atoms with correspondences.In this analysis, we compare the molecular dynamics (MD) trajectories of IBB-bound Importin-portin-β –47. For rization  to assig(PNG)Click here for additional data file.S20 Fig(A) Arg164Ser mutant β-lactamase, (B) Ile50Val mutant HIV-1 protease, and (C) GDP-bound EF-Tu, respectively. We analyze datasets based on different fragments of each protein. Specifically, (A) in TEM β-lactamase, we analyze the whole protein structure, residues 65–230, and residues 65–213; (B) in HIV-1 protease, we analyze chain A and chain B; and, (C) in EF-Tu, we analyze the whole protein structure, residues 220–310, and residues 311–405. The y-axis denotes the absolute difference (or Δ-change) in RMSF between wild-type and mutants classes.In these analyses, we compare the molecular dynamics (MD) trajectories of the alternative state for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S21 Fig(A) Met290Val Abl1 and (B) unbound Importin-β, respectively. We analyze Abl1 based on different fragments the protein. Specifically, we analyze the whole protein structure, residues 242–502, and residues 242–315. Note that, in the context of Importin-β, the superhelix includes the entire structure and so we do include any additional sub-fragment analyses. The y-axis on denotes the absolute difference (or Δ-change) in RMSF between wild-type and mutants classes.In these analyses, we compare the molecular dynamics (MD) trajectories of the alternative state for each protein to the corresponding trajectories of (PNG)Click here for additional data file.S22 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) the whole protein, (B) fragment 65–230, and (C) fragment 65–213. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S23 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) chain A and (B) chain B. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S24 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) whole protein, (B) fragment 208–308, and (C) fragment 311–405. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S25 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. In each plot, we show the association metrics (and their corresponding standard errors) computed for each residue while analyzing (A) the whole protein, (B) fragment 242–502, and (C) fragment 242–315. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S26 Figr, namely dist|, | < r. Next, we fill in all the triangles (or faces) formed by these connected edges. We treat the resulting triangulated mesh as an simplicial complex with which we can perform topological data analysis. Here, we consider the construction of mesh representations for each protein while setting r = {2.0, 4.0, 6.0} ȧngströms (Å). Other SINATRA parameters were fixed: c = 20 cones, d = 8 directions per cone, θ = 0.80 cap radius used to generate directions in a cone, and l = 120 sublevel sets per filtration. Note that an overlap in signal shows the robustness of SINATRA Pro to this input parameter value.Recall that we use the atomic positions for each protein to create mesh representations of their 3D structures (see (PNG)Click here for additional data file.S27 Figimplicitly normalize the 3D protein structures by rotationally aligning their topological summary statistics. To carry out this alignment procedure, we first take each pair of protein structures and superimpose the center of mass of the backbone alpha-carbons (Cα) atoms to the same origin. Next, we compute topological summary statistics over the mesh representation of each structure in m = 500 spherically uniformly distributed directions (see the R) between the association metrics from SINATRA Pro calculated from the structures aligned using Euler characteristic (EC) transform and structures pre-aligned using RMSD for β-lactamase: (A) the whole protein, (B) fragment 65–230, and (C) fragment 65–213; monomers of HIV-1 protease: (D) chain A and (E) chain B; and fragments of the elongation factor EF-Tu: (F) the whole protein, (G) fragment 220–310 (Domain 2), and (H) fragment 311–405 (Domain 3). Correlations near one symbolize high agreement between the two alignment schemes.Here, we perform sequence-independent structural alignment where we (PNG)Click here for additional data file.S28 Figimplicitly normalize the 3D protein structures by rotationally aligning their topological summary statistics. To carry out this alignment procedure, we first take each pair of protein structures and superimpose the center of mass of the backbone alpha-carbons (Cα) atoms to the same origin. Next, we compute topological summary statistics over the mesh representation of each structure in m = 500 spherically uniformly distributed directions (see the R) between the association metrics from SINATRA Pro calculated from the structures aligned using ECT and structures pre-aligned using RMSD for fragments of Abl1 Tyrosine protein kinase: (A) the whole protein, (B) fragment 242–502, and (C) fragment 242–315; and (D) unbound Importin-β. Correlations near one symbolize high agreement between the two alignment schemes.Here, we perform sequence-independent structural alignment where we (PNG)Click here for additional data file.S1 Table(i) the total number of proteins analyzed N = 50, (ii) the number of cones of directions c = {15, 20}, (iii) the number of directions within each cone d = {4, 8}, and (iv) the number of sublevel sets  used to compute the Euler characteristic (EC) along a given direction l = {25, 50}. We simulate 10 different datasets for each combination of parameter values. Values appearing after the ± symbol are the standard deviations of these estimated times across the different runs. Each analysis was performed using simulated protein structures with ∼2700 atoms and all runtimes were computed using a central processing unit (CPU) with 8 cores and 128 gigabytes (GB) of RAM.Each entry represents the time (in seconds) it takes to run each step of the SINATRA Pro algorithm based on: (PDF)Click here for additional data file.S2 Table(i) the total number of proteins analyzed N = 100, (ii) the number of cones of directions c = {15, 20}, (iii) the number of directions within each cone d = {4, 8}, and (iv) the number of sublevel sets  used to compute the Euler characteristic (EC) along a given direction l = {25, 50}. We simulate 10 different datasets for each combination of parameter values. Values appearing after the ± symbol are the standard deviations of these estimated times across the different runs. Each analysis was performed using simulated protein structures with ∼2700 atoms and all runtimes were computed using a central processing unit (CPU) with 8 cores and 128 gigabytes (GB) of RAM.Each entry represents the time (in seconds) it takes to run each step of the SINATRA Pro algorithm based on: (PDF)Click here for additional data file.S3 Table(i) the total number of proteins analyzed N = 200, (ii) the number of cones of directions c = {15, 20}, (iii) the number of directions within each cone d = {4, 8}, and (iv) the number of sublevel sets  used to compute the Euler characteristic (EC) along a given direction l = {25, 50}. We simulate 10 different datasets for each combination of parameter values. Values appearing after the ± symbol are the standard deviations of these estimated times across the different runs. Each analysis was performed using simulated protein structures with ∼2700 atoms and all runtimes were computed using a central processing unit (CPU) with 8 cores and 128 gigabytes (GB) of RAM.Each entry represents the time (in seconds) it takes to run each step of the SINATRA Pro algorithm based on: (PDF)Click here for additional data file.