Dusp6 further increased tube formation. In vivo, immunohistochemistry also detected ERK phosphorylation and DUSP6 expression in Podoplanin-positive cells on CCN2-supplemented Matrigel. These results indicated that CCN2 promotes lymphangiogenesis by enhancing integrin αvβ5-mediated phosphorylation of ERK and demonstrated that DUSP6 is a negative regulator of excessive lymphangiogenesis by CCN2.Lymphangiogenesis is essential for the development of the lymphatic system and is important for physiological processes such as homeostasis, metabolism and immunity. Cellular communication network factor 2 , is a modular and matricellular protein and a well-known angiogenic factor in physiological and pathological angiogenesis. However, its roles in lymphangiogenesis and intracellular signaling in lymphatic endothelial cells (LECs) remain unclear. Here, we investigated the effects of CCN2 on lymphangiogenesis. In in vivo Matrigel plug assays, exogenous CCN2 increased the number of Podoplanin-positive vessels. Subsequently, we found that CCN2 induced phosphorylation of ERK in primary cultured LECs, which was almost completely inhibited by the blockade of integrin αvβ5 and partially decreased by the blockade of integrin αvβ3. CCN2 promoted direct binding of ERK to dual-specific phosphatase 6 (DUSP6), which regulated the activation of excess ERK by dephosphorylating ERK. In vitro, CCN2 promoted tube formation in LECs, while suppression of  Formation of new lymphatic vessels, or lymphangiogenesis, plays an important role in normal physiological processes during development as well as in a number of pathological processes like inflammation, wound healing, and tumor metastasis2. Impairment of lymphatic function results in various diseases that are characterized by the inadequate transport of interstitial fluid, edema, impaired immunity, and fibrosis4. Abnormal lymphatic growth around tumors facilitate metastatic spread of malignant cells5. Vascular endothelial growth factor-C (VEGF-C)-Vascular endothelial growth factor receptor-3 (VEGFR-3) signaling is crucial for the development of lymphatic vessels7. Several angiogenic factors are also involved in growth of lymphatic vessels, such as the Tie /angiopoietin system, neuropilin-2 and integrin α9, which regulate lymphangiogenesis10.Lymphatic vessels regulate tissue fluid homeostasis, immune cell trafficking, and absorption of dietary fats11. CCN2 is a member of the CCN family of modular matricellular proteins, which contains six family members (CCN1-6)12. CCN2 is a multifunctional molecule, with roles in angiogenesis, development, tumorigenesis, fibrotic disease and wound healing13. CCN family proteins contains four structural domains: the IGF binding domain, the von Willebrand factor C repeat domain, the thrombospondin type 1 (TSP) domain and the cysteine knot C-terminal domain. Each module interacts with other cell surface receptors, growth factors, structural matrix proteins12. In particular, integrins are involved in many CCN2-mediated biological processes; The TSP domain in CCN2 interacts with integrin α6β114, and the C-terminal motif of CCN2 interacts with integrins αvβ3, α5β116. CCN2-mediated cell adhesion via integrin αvβ3 or α6β1 requires cell surface heparan sulfate proteoglycans (HSPGs), which act as co-receptors for integrins17. By binding to cell surface receptors, CCN2 can alter various intracellular signaling pathways, including the mitogen-activated protein kinase (MAPK) pathway20, WNT pathway21, NFκB pathway22 and others. Among them, the extracellular signal-regulated kinase (ERK)-dependent pathway provides a variety of CCN2-mediated functions, including angiogenesis, fibrosis, inflammation, bone metabolism, and tumorigenesis25. Activation of ERK by CCN2 can be transient or persistent17, depending on the cell type and microenvironment, but when and how ERK is inactivated after CCN2 stimulation is controversial. The dual-specific phosphatase (DUSP) family phosphatases are the largest group of protein phosphatases that specifically regulate MAPK activity in mammalian cells26. The relationship between DUSPs and CCN2 is unknown.Cellular communication network factor 2 (CCN2), also known as connective tissue growth factor (CTGF), was originally isolated as a platelet-derived growth factor (PDGF)-like factor in human umbilical vein endothelial cell (HUVEC)27. In vivo, it has been shown to promote angiogenesis in CAM assays, collagen pellet implantation, and Matrigel plug assays29. CCN2 is highly expressed in endothelial cells during development, and Ccn2 knockout mice have impaired angiogenesis in the growth plate, resulting in skeletal abnormalities and perinatal lethality30. Normal basement membrane formation is also impaired in Ccn2 knockout mice31. In breast and prostate cancers, tumor cells and tumor stroma secrete CCN2, which promotes endothelial cell migration and induces tumor angiogenesis33. CCN2 binds to integrin αvβ3 on endothelial cells and activates the p38 MAPK, ERK, and Jun N-terminal kinase (JNK) MAPK signaling pathways35. CCN2 is a complex regulator of angiogenesis because it not only directly regulates endothelial cell function, but also controls the production and activity of other angiogenic molecules  and molecules that affect extracellular matrix (ECM) integrity and stability , and tissue inhibitor of MMPs)36. Lymphatic vessels differ significantly from blood vessels in their specific structure and function and in molecular mechanisms that regulate their development and growth2, and it is still not well understood how CCN2 acts in lymphangiogenesis.The angiogenesis-promoting effect of CCN2 has been shown in vascular endothelial cell culture experiments and animal studies. CCN2 directly binds to integrin αvβ3 and promotes endothelial cell adhesion, migration, and survival38. Furthermore, CCN2 is involved in fibrosis-associated renal lymphangiogenesis through its interaction with VEGF-C39. These reports show that CCN2 is closely associated with lymphangiogenesis in fibrous diseases. However, it has not been elucidated how CCN2 acts on physiological lymphangiogenesis. In this study, we investigated the effect of CCN2 on lymphangiogenesis in vivo and the downstream signaling of CCN2 in primary cultured lymphatic endothelial cells (LECs).Previous studies in renal, peritoneal dialysis-related and liver fibrosis mouse models showed that CCN2 enhanced lymphangiogenesisTo examine the role of CCN2 in lymphangiogenesis in vivo, the Matrigel plug assay was performed. Recombinant CCN2 was mixed with Matrigel and subcutaneously injected onto the backs of mice. At 7 days after injection, blood vessels were macroscopically observed on the surface of plugs in the CCN2 group mice, but not in the control group Fig. A. HistolLyve1 and Vegfr3 and lower levels of Pecam1 compared to vascular endothelial cells  and prospero homeobox 1 (Prox1) were analyzed by quantitative RT-PCR. The expression levels of Lyve1, Pdpn and Prox1 were increased by CCN2 in a dose-dependent manner up to 100 ng/mL; at 200 ng/mL, their expression levels dramatically decreased compared with levels at 100 ng/mL  but did not affect Kdr (encording VEGFR2) expression     Fig. . Itgb2 a28. Therefore, we next analyzed the effects of CCN2 on the cell growth of LECs. Control LECs showed cell proliferation after 24 h of incubation (p = 0.01), whereas 10, 50 and 100 ng/mL CCN2-stimulated LECs did not show significant proliferation  . However, no changes in proliferation were observed at 48 and 72 h of treatment. These results indicate CCN2 has a weak growth inhibitory effect on LECs.CCN2 promotes cell proliferation in vascular endotheliumDusp1, -2, -4, -5, -6, -7, -8, -9 and -10 are expressed in LECs; Dusp6 was predominant, but Dusp16 was undetected    Fig. B,C.Figur38. CCN2 also enhanced angiogenesis and peripheral nerve cell migration. Previous angiogenesis studies showed that CCN2 is sufficient to induce angiogenesis, directly or indirectly, under in vitro and in vivo experimental conditions28. CCN2 expression increases after central nervous system trauma in rodent models45. In zebrafish spinal cord injury models, injury-induced ccn2a directs glial cell bridging and neuronal regeneration46. Our results are consistent with these previous studies.Here, we provide novel insights on the role of CCN2 in lymphangiogenesis. Our results showed that CCN2, a well-known angiogenic factor, promotes lymphangiogenesis in in vivo Matrigel assays, similar to findings in fibrosis model studies47. Because the LYVE-1 detected by Western blotting had a high molecular weight, it could be the glycosyl-modified inactivated form of LYVE-1; CCN2 may decrease the inactivated form of LYVE-1. The molecular mechanism underlying the cell proliferation of LECs involves the activation of ERK signaling stimulated by autocrine or paracrine VEGF-C and VEGFR3 signaling48. However, the phosphorylation of VEGFR2 and VEGFR3 was not increased by CCN2, which suggests that CCN2 and endogenous VEGF-C secreted from LECs do not activate VEGF receptor signaling upstream of ERK in primary cultured LECs.CCN2 increased the mRNA expression of lymphatic vessel markers but did not change Podoplanin and VEGFR3 protein expression, while LYVE-1 was rather decreased. LYVE-1 in LECs is inactive by default because it is O-terminally glycosylated, and the glycan chain prevents binding to hyaluronan49. Integrin αvβ3 enhanced angiogenesis which is induced by basic fibroblast growth factors and tumor necrosis factor α, and integrin αvβ5 enhanced VEGF-induced angiogenesis and tumor metastasis49. In the present study, both integrin αvβ3 and integrin αvβ5 were detected in LECs, and the phosphorylation of ERK by CCN2 was impaired by antibodies targeting integrins αvβ3 and αvβ5, especially integrin αvβ5. We showed for the first time that integrin αvβ5 is involved in CCN2 signaling in LECs. Integrin α9β1 is required for the proper development of the lymphatic system49, while integrin β1 plays an important role in the invasion of LECs50. In this study, we also founded that the mRNA expression of Itga9 and Itgb1 was high in LECs, but suppression of integrin α9 and β1 did not decrease CCN2-induced ERK phosphorylation. These results indicate that CCN2-induced phospho-ERK is partially mediated by integrins αvβ5 and αvβ3 in LECs. Furthermore, inhibition of HSPGs by heparinase I decreased phosphorylation of ERK by CCN2, suggesting that HSPGs are required for the interaction of CCN2 and integrins αvβ5 and αvβ3 in CCN2-mediated lymphangiogenesis.Integrins play key roles in endothelial cell migration and survival during angiogenesis and lymphangiogenesis. Among them, integrins αvβ3 and αvβ5 are both expressed in vascular endothelial cells and promote different angiogenesis pathways51, and DUSP5 modulates ERK1/2 downstream of the Ang1/Tie2 receptors52. We showed DUSP6 is highly expressed in LECs, and immunoprecipitation assays showed that CCN2 enhanced the interaction of ERK and DUSP6 in LECs. Suppression of Dusp6 by siRNA increased phospho-ERK levels compared with control cells. Similar to the in vivo findings, CCN2 promoted tube formation in an in vitro tube formation assay, and suppression of DUSP6 further enhanced tube formation. However, in control gels without CCN2, suppression of DUSP6 did not promote tube formation. Moreover, in the proliferation assay, suppression of DUSP6 did not affect the proliferation of LECs. These results suggest that DUSP6 does not contribute to cell proliferation but inhibits tube formation in CCN2-mediated lymphangiogenesis. In addition, because the effect of DUSP6 occurred only when LECs were stimulated with CCN2, it is assumed that DUSP6 acts as a brake to inhibit abnormal lymphangiogenesis in inflammatory and fibrotic conditions where CCN2 is in excess in vivo.The process of lymphangiogenesis in the adult body is based on cell proliferation, branching and migration. Our in vitro cell proliferation assay results showed that CCN2 did not increase the proliferation of LECs but rather weakly suppressed proliferation. In HUVECs, DUSP1 and DUSP5 regulate the MAPK signaling pathways that are activated by VEGF17. DUSP6 was increased between 8 and 12 h after CCN2 stimulation, suggesting that the negative regulation of ERK by DUSP6 may be one of the mechanisms underlying the sustained activation of MAPKs by CCN2. In a previous study, Forkhead box C1 (FOXC1) and Forkhead box C2 (FOXC2) were identified as negative regulators of ERK53. In mice, lymphatic endothelial cell-specific deletion of FOXC1, FOXC2, or both resulted in lymphatic hyperplasia via hyperactivation of ERK53. Here we identified DUSP6 as a suppressor of ERK in LECs; however, the relationship between FOXC1, FOXC2 and DUSP6 has not yet been analyzed.In immunocytochemistry, both ERK and DUSP6 were detected in the cytoplasm of LECs, and the initial nuclear translocation of phospho-ERK by CCN2 was not impaired. In Matrigel plug assay, phosphorylated ERK and DUSP6 expression were detected in Podoplanin-positive cells in the CCN2 gel. In this study, CCN2-induced ERK signaling peaked within a short period of time, but then persisted to some extent for 4 to 8 h after CCN2 stimulation. Although integrin-mediated MAPK signaling is generally transient, previous reports have shown that CCN2 stimulation sustained ERK activation for at least 9 h in fibroblasts56, lymphangiogenesis in in vivo Matrigel assays was promoted. The results between these assays may not be comparable, and our present results are not contradictory.The discrepancy observed between the in vitro cell proliferation and in vivo Matrigel assays may be due to effects of ECM and MMP in Matrigel. Since CCN2-signaling is mediated by ECM and activated through proteolytic processing by MMP-2, 357. Moreover, mesenchymal stem cell-derived CCN2 promoted tongue squamous cell carcinoma progression in vitro and in vivo. CCN2 is expressed at higher levels in tongue squamous cell carcinoma tissues than in paraneoplastic tissues, and its expression correlated with lymph node metastasis58. A study of patient-derived tumor samples in the Cancer Genome Atlas database showed that CCN2 expression was higher in head and neck cancers, and high expression of CCN2 and MMP3 was associated with worse prognosis in head and neck cancers54. Lysine-specific demethylase 1 (LSD1) is a nuclear histone demethylase that functions as an epigenetic regulator to promote cancer initiation, progression, and relapse. LSD1 inhibitors markedly downregulated CCN2 mRNA and protein expression and inhibited further xenograft growth in a tonsillar OSCC patient-derived xenograft mouse model59.A recent study found significant expression of CCN2 in the epithelium and connective tissue of oral submucous fibrosis, which was classified as a potentially malignant oral disease, as well as oral squamous cell carcinoma (OSCC) cases, increasing with disease progression, whereas healthy buccal mucosa had no CCN2 expressionWe demonstrated that CCN2 activates ERK signaling via integrin αvβ5 and αvβ3 to promote lymphangiogenesis. In addition, CCN2 enhances the binding of DUSP6 to ERK, and DUSP6 suppresses excessive CCN2-induced lymphangiogenesis. In OSCC, CCN2-signaling is associated with disease progression and lymph node metastasis. Our results suggest that integrins αvβ5 and αvβ3, ERK, and DUSP6-mediated lymphangiogenesis may be therapeutic targets of OSCC.2. LECs were cultured using the EGM2-Endothelial cell growth medium-2 bullet kit . Cells up to 6 passages were used in in vitro experiments.C57BL/6 mouse primary cultured LECs and VECs  were cultured in 0.1% gelatin-coated culture dishes at 37 °C with 5% CO4 cells per well and cultured overnight. The medium was changed to medium containing 0, 10, 50 or 100 ng/mL recombinant rat CCN2/CTGF protein, Carrier Free . At 0, 24, 48 or 72 h after treatment, cells were fixed with methanol, stained with 0.5% crystal violet  in 25% methanol for 5 min, washed with water five times and dried. Absorbance at 590 nm was measured using the SPARK 10 M plate reader . Cell proliferation was expressed as the ratio of the absorbance of each time point to the absorbance at 0 h.Cell proliferation was assessed by crystal violet staining method. Cells were plated in 24-well plates at 2 × 10Total RNA was prepared from cells using the Purelink RNA purification Kit . cDNA was prepared from 1 μg of total RNA using the QuantiTect RT Kit . Quantitative RT-PCR was performed using TB Green Premix ExTaq II  and a LightCycler96 . Primers used for qPCR are listed in Supplementary Table http://sidirect2.rnai.jp/). Sense and complementary strand RNAs having a two-mer overhang were synthesized and annealed. The siRNA sequences for each mRNA target are listed in Table 5) were transfected with 25 pmol of target siRNA or non-target siRNA  using Lipofectamine RNAiMAX  according to the manufacturer’s instructions.Expression of integrins and DUSP6 was suppressed by siRNA. siRNAs were designed by an siRNA design site, siDirect ver.2 (LECs were pre-cultured with FBS-free and supplement-free EGM2 medium (Lonza) for 24 h. Cells were then treated with CCN2-containing FBS-free and supplement-free EGM2 medium and cultured for the appropriate time. Cells were washed with PBS and lysed in RIPA Buffer (Merck) containing cOmplete protease inhibitor cocktail (Roche) and phosphatase inhibitor cocktail . Protein concentration was determined with the BCA protein assay kit (Thermo). Protein samples (5–20 µg) were separated with SDS-PAGE and transferred to PVDF membranes (Invitrogen). The membranes were blocked in Blocking-one  for 30 min and cut prior to hybridization with antibodies, then incubated with primary antibodies at 4 °C overnight. The primary antibodies are listed in Supplementary Table LECs were pre-cultured with FBS-free and supplement-free basal medium for 24 h and then treated with medium containing 100 ng/mL of recombinant rat CCN2/CTGF (R&D Systems) for 5 min. The phosphorylation levels of RTK were analyzed using a Proteome profiler mouse phospho-RTK array kit (R&D Systems) according to the manufacturer’s instructions. Briefly, 200 µg of total protein was incubated with the receptor antibody-spotted membrane at 4 °C for 24 h. After washing, the membrane was incubated with anti-phospho tyrosine kinase antibody, and the phosphorylation levels of receptor tyrosine kinase were detected with the image analyzer 680 .Cells were lysed in Lysis Buffer (CST) containing cOmplete protease inhibitor cocktail and phosphatase inhibitor cocktail. Protein concentration was determined with the BCA protein assay kit (Thermo). Cell lysates containing 200 µg of total protein were incubated with anti-ERK (CST) or anti-DUSP6  antibody at 4 °C overnight. Protein A magnetic beads  were then added to the cell lysate and antibody mixture, and the samples were mixed for 20 min. Magnetic beads were separated and washed with lysis buffer five times; the samples were denatured in 20 µl of 3 × SDS Sample Buffer and subjected to Western blotting.To study the phosphorylation of ERK and the localization of DUSP6, LECs were plated in 35 mm glass bottom dishes , pre-cultured with FBS-free and supplement-free basal medium for 24 h and then treated with medium containing 100 ng/mL of recombinant rat CCN2/CTGF (R&D Systems) for 0, 5, 15, 30, 60, 120 min. Cells were fixed in 4% paraformaldehyde in PBS. Cell permeabilization was performed by using ice-cold 100% methanol, incubate in methanol for 20 min at − 20 °C. After washing three times with PBS, cells were blocked in Blocking-one Histo  for 30 min and then incubated with primary antibodies at 4˚C overnight. The primary antibodies are listed in Supplementary Table Dusp6 siRNA or non-targeting siRNA. Each well of a 24-well plate was coated with 100 μL growth factor-reduced Matrigel  with or without 1.6 μg CCN2, which was allowed to polymerize for 30 min at 37 °C. Then, LECs were seeded onto the coated wells at a density of 5 × 104 cells per well and cultured in 500 μL FBS-free and supplement-free EGM2 medium. After incubation for 8 h at 37 °C under 5% CO2, tube formation images were captured at 100 × magnification by microscopy  in three random fields. For quantification, the tube length and branching points were measured.A Matrigel-based tube formation assay was performed as previously described. LECs were transfected with 60. Male 8-week-old C57BL/6 mice were purchased from Charles River Japan. Mice were kept under specific pathogen-free conditions and used at 9 weeks of age. All experiments were performed in compliance with the relevant laws and institutional guidelines and were approved by the Animal Care and Use Committee of Fukuoka University , and in accordance with the ARRIVE guidelines.Matrigel plug assay was conducted to measure the lymphangiogenesis as previously describedWe mixed 450 µL of growth factor reduced Matrigel (Corning) and 8.33 µg of recombinant rat CCN2/CTGF (R&D Systems) in 50 µL of PBS or 50 µL of PBS as control. We then randomly divided into two groups , and subcutaneously injected the Matrigel mixture on the back of each mouse under anesthesia by 2% isoflurane. At day 7 after injection, mice were euthanized with cervical dislocation. Matrigel plugs were removed, fixed in 4% paraformaldehyde in PBS, and embedded in paraffin. Sections (4 µm thick) were deparaffinized and stained with hematoxylin–eosin (HE). Primary antibodies used for immunohistochemistry are listed in Supplementary Table t test, one-way and repeated measures analysis of variance (ANOVA), or two-way ANOVA. For multiple comparisons, we performed Bonferroni or Sidak analysis. Statistical significance was set at a p-value of less than 0.05.Statistical analysis was performed using GraphPad Prism software version 8. All experiments were performed more than three times, except the RTK assay (n = 2), and data are expressed as the mean ± standard error (S.E.). Comparative analysis was performed by Student’s Supplementary Information.


The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein–ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein–ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. We evaluate the properties of the generated molecules and demonstrate that they change significantly when conditioned on mutated receptors. We also explore the latent space learned by our generative model using sampling and interpolation techniques. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning. We generate 3D molecules conditioned on receptor binding sites by training a deep generative model on protein–ligand complexes. Our model uses the conditional receptor information to make chemically relevant changes to the generated molecules. To reduce the time and cost of drug development, there is growing recognition of the need for new algorithms for sampling compounds with a high chance of success and predicting their biological activity through virtual screening.Chemical space is enormous, but the subset of molecules that have desirable biological activity is much smaller. Drug discovery typically requires searching through this space for molecules that bind to a specific target, such as a protein implicated in a disease. Thus the search for new drugs involves an alternating procedure of (1) sampling compounds from promising regions of chemical space and (2) screening them for activity against the biological target. The difficulty of searching chemical space for novel therapeutics has lead to the development of computational methods that sample and screen compounds 1 The increasing availability of structural data in public repositories like the Protein Data Bank2 has led to the widespread adoption of machine learning in structure-based drug discovery. Machine learning allows complex nonlinear models of protein–ligand binding to be learned automatically from structural features. Furthermore, the impressive performance of deep learning in computer vision and natural language processing has inspired researchers to apply these methods to biological structures as well. Deep neural networks can learn highly abstract functions from structural data with minimal featurization. This is recently exemplified by AlphaFold, a deep learning model capable of predicting the 3D structures of proteins with high accuracy directly from their amino acid sequence and evolutionary information.3 Despite the utility of deep learning models, it is crucial that they are designed with the appropriate inductive biases and assessed with sufficient cross-validation to avoid overfitting.Given that the structure of biomolecules determines their function, leveraging the three-dimensional (3D) structure of the target when screening drug candidates has the potential to improve prediction quality.4,5 pose ranking,5 and affinity prediction.6,7 Furthermore, grid-based CNN scoring functions have been integrated into ligand pose optimization8 for molecular docking,9 where they outperform traditional scoring functions. Neural networks have also been applied to binding affinity prediction10 and quantum energy estimation11 using atomic coordinate-based representations. Deep learning is now widely regarded as the state-of-the-art in virtual screening.Deep learning was first introduced in structure-based drug discovery for scoring the 3D interactions between target proteins (receptors) and small molecules that could potentially bind to them (ligands). Protein–ligand scoring can be formulated as three-dimensional image recognition by training convolutional neural networks (CNNs) on docked protein–ligand poses represented as atomic density grids. This approach has been successfully applied to binding discrimination,12–14 took cues from language modeling by representing molecules with the SMILES string syntax.15 Improvements on these approaches used reinforcement learning to guide the generation process towards desired cheminformatic criteria.16,17 Other work included grammatical constraints that alleviate the tendency for generative models to produce invalid SMILES strings.18,19 Despite these improvements, SMILES strings are not permutation invariant, so they do not capture the notion of chemical similarity. They also lack conformational information, which limits their applicability to structure-based drug discovery.In contrast, it has only recently become viable to use deep learning to sample molecules with drug-like properties prior to virtual screening. Initial efforts to train deep generative models on molecules20 and can be generated as output using fully-connected layers.21 However, assuming that generated bonds are independent can result in invalid valences. Solutions include producing molecules as trees of chemically valid substructures22 or hard-coding valency constraints into the generative process.23,24 Another concern is that comparing molecules in the loss function requires a graph matching algorithm, which is computationally expensive unless approximations are made.21,25 Generative adversarial networks (GANs) avoid this by only comparing molecules implicitly, but they are notoriously difficult to train.26,27 The generation of molecular graphs with deep neural networks can also be biased towards cheminformatic objectives using reinforcement learning.25–27Molecular graphs have been used as a more natural representation of molecules than SMILES strings. Graphs can be provided as input to message-passing neural networks28 These have been extended for generating conformers with arbitrary chemical composition,29 simultaneously producing the coordinates and molecular graph,24,30 and generating linker atoms that connect fragments into valid 3D molecules.31 Autoregressive models can be made invariant to rotations and translations by modeling distributions over interatomic distances instead of coordinates. However, they require selecting a canonical atomic ordering due to lack of permutation invariance. On the other hand, non-autoregressive approaches generate distance matrices all at once,32 and have been extended to generating conformers conditioned on a molecular graph.33,34 One challenge of non-autoregressive models is that generating distance matrices requires enforcing the triangle inequality. Another drawback is that the Hungarian algorithm, which has cubic time complexity in the number of atoms, must be applied to compare distance matrices in a permutation agnostic manner.Most work on deep generative models of molecules have used 2D representations, but contemporary methods can also generate 3D conformations. Early efforts generated different conformers of a single chemical formula using autoregressive models, which output atoms sequentially.35 but this does not lead to an unambiguous set of atoms and bonds. Another group trained an auxiliary captioning network to output SMILES strings based on density grids,36 but this relinquishes the 3D structure generated by the model. Iterative atom fitting and bond inference is the only approach, to our knowledge, that produces 3D molecular structures from atomic density grids.37Atomic density grids can also be used as a 3D representation of molecules for generative modeling. Unlike distance matrices, grids are coordinate frame-dependent. However, they are permutation invariant and can be compared without expensive matching algorithms. Density grids also provide holistic shape information that is not easily accessible from atomistic representations, and is arguably of equal importance for protein–ligand binding as pairwise interactions. The main obstacle to generative modeling with atomic density grids is converting them into discrete molecules. Past work has used Wiener deconvolution to approximate the inverse of the density kernel,38 or Coulomb matrices.39 Others used reinforcement learning to guide the sampling of 3D ligands towards high affinity for a target protein30 or conditioned the generation of molecular graphs on density grids of 3D pharmacophores.40 However, generating 3D molecular structures directly from protein binding pockets remains an unsolved challenge.41 To address this, we make the following contributions:The use of protein structure to generate molecules with deep learning an under-explored research area. Preliminary work has generated SMILES strings based on receptor binding site information represented as atomic density grids(1) The first demonstration of 3D molecular structure generation with receptor-conditional deep generative models.(2) Evaluation of the effect on generated molecules of conditioning the generative model on mutated receptors.(3) Exploration of the latent space learned by the generative model through sampling and interpolation.2.2.1.Np atomic property functions p and value ranges for those properties v, which are listed in a, the atom type vector t ∈  PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.dstrR.i" format="PBM" resolution="300" x-size="13" y-size="11" xml:space="preserve">00011111111000000101001010000010100100100010010010010001010001010000101111110000010000100000001011001000001001010010000101000100100111110011110</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="18.545455pt" height="16.000000pt" viewBox="0 0 18.545455 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="M240 840 l0 -40 40 0 40 0 0 -80 0 -80 -40 0 -40 0 0 -200 0 -200 -40 0 -40 0 0 -80 0 -80 -40 0 -40 0 0 -40 0 -40 200 0 200 0 0 40 0 40 -40 0 -40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 -40 0 -40 40 0 40 0 0 -80 0 -80 160 0 160 0 0 80 0 80 -40 0 -40 0 0 40 0 40 -40 0 -40 0 0 40 0 40 -40 0 -40 0 0 40 0 40 80 0 80 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 80 0 80 -40 0 -40 0 0 40 0 40 -40 0 -40 0 0 40 0 40 -320 0 -320 0 0 -40z m240 -160 l0 -120 40 0 40 0 0 120 0 120 80 0 80 0 0 -160 0 -160 -120 0 -120 0 0 40 0 40 -40 0 -40 0 0 -80 0 -80 120 0 120 0 0 -40 0 -40 40 0 40 0 0 -40 0 -40 40 0 40 0 0 -40 0 -40 40 0 40 0 0 -40 0 -40 -80 0 -80 0 0 40 0 40 -40 0 -40 0 0 40 0 40 -40 0 -40 0 0 40 0 40 -80 0 -80 0 0 -80 0 -80 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 80 0 80 40 0 40 0 0 200 0 200 40 0 40 0 0 80 0 80 40 0 40 0 0 -120z m400 80 l0 -40 40 0 40 0 0 -80 0 -80 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 160 0 160 40 0 40 0 0 -40z"/></g></svg>NT is created by concatenating Np atomic property vectors p through the following:An overview of our methods can be viewed in The atomic properties we used were element, aromaticity, H-bond donor and acceptor status, and formal charge. Different element ranges were represented for receptor atoms and ligand atoms, but the value ranges for all other properties were the same. The process we used to construct value ranges for properties and compare different type schemes is described in the supplement.2.2.f:  ×  →  that takes as input the distance d between the atom coordinate and the grid point and the atomic radius r:After assigning atom type vectors, we convert molecules to an atomic density grid format. Atoms are each represented as continuous densities with a truncated Gaussian shape. The density value of an atom at a grid point is defined by a kernel function r = 1.0 for all atoms in this work. Grid values are computed by summing the density kernel of each atom at each point on a 3D grid, multiplied by the value of the atom's type vector in the corresponding grid channel. A molecule with N atoms and atom type vectors of length NT can be represented as a matrix of atom types T ∈ N×NT and a matrix of atomic coordinates C ∈ N×3. The function that computes atomic density grids g: N×NT × N×3 → NT×NX×NY×NZ is then defined as follows:N is the number of atoms in the input molecule, so there is no maximum number of atoms per grid. All atoms that fit within the spatial extent of the grid are represented. We used cubic grids with side lengths of 23.5 Å and 0.5 Å resolution, resulting in spatial dimensions NX = NY = NZ = 48. s:  PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.dstrZ.i" format="PBM" resolution="300" x-size="14" y-size="11" xml:space="preserve">0001111111110000011000010100000100001010000000000101000000000010100000000001010000000000101000000000010100000000001010000010000101000001100001111111111000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="19.818182pt" height="16.000000pt" viewBox="0 0 19.818182 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="M240 760 l0 -120 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 160 0 160 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -80 0 -80 400 0 400 0 0 120 0 120 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -200 0 -200 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 80 0 80 -360 0 -360 0 0 -120z m640 0 l0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 40 0 40 40 0 40 0 0 -40z"/></g></svg>3 → 3 grid indices to spatial coordinates. We center the grids on the input molecule before adding random translations and rotations during both training and evaluation. This is facilitated by computing grids on-the-fly using libmolgrid, a GPU-accelerated molecular gridding library.42The radius was fixed at 2.3.Gref into a discrete 3D molecular structure does not have an analytic solution, so we solve it as the following optimization problem:The inverse problem of converting a reference density grid 2.4.43,44 An overview of the procedure is shown in Algorithm S1.†We construct valid molecules from the sets of atoms detected by atom fitting using a sequence of inference rules that add bond information and hydrogens while trying to satisfy the constraints defined by the atom types. The algorithm is based on customized bond perception routines implemented in OpenBabel.2.5.45 of atomic density grids with the architecture displayed in p(lig|rec), where rec is a receptor binding site density and lig is the density of a ligand that binds to it. We assume that there is a latent variable z representing binding interactions that follows a prior distribution we can sample, such as a standard normal distribution. The generative process consists of drawing a sample z ∼ p(z) followed by liggen ∼ pθ, where pθ is a decoder neural network and c is an encoding of a receptor density rec produced by a conditional encoder network. Training this model by naive maximum likelihood estimation would require computing the latent posterior probability pθ, which is intractable. The key is to instead train an input encoder network to learn an approximate model qϕ of the posterior distribution. The training task minimizes two objectives:Our generative model is a conditional variational autoencoder (CVAE)Lrecon term maximizes the probability that latent samples from the approximate posterior distribution z ∼ qϕ are decoded as realistic ligand densities – specifically, the real ligand density lig that was provided to the input encoder. The Kullback–Liebler divergence term LKL encourages the approximate posterior distribution to match the true prior distribution, p(z) = N. The combined effect is that the latent space follows a normal distribution, enabling generative sampling, while the decoded samples are expected to appear realistic in the receptor context. The model is trained by providing real  examples to the encoder to get latent representations of their interactions, then maximizing the likelihood of decoding the latent vectors back to the corresponding ligand densities when conditioned on the cognate receptor density.The reconstruction loss term We included an additional term in the loss function that minimized steric clash in terms of the overlap between the generated ligand density and the receptor density. This was calculated by first summing across the grid channels, then multiplying the receptor and ligand density at each point:We validated this as a measure of steric clash by checking empirically that real protein–ligand complexes did not have density overlap, owing to our use of a density kernel with a relatively small, fixed atomic radius. We combined the three loss terms with weights into the final loss function like so:λrecon = 4.0, λKL = 0.1, and λsteric = 1.0, though the KL divergence loss weight was gradually ramped up to 1.6 over 200 000 iterations, starting at iteration 450 000. The model was trained using RMSprop with learning rate 10−5 for 1 000 000 iterations with a batch size of 8.The loss weights were initialized at 2.6.49 An initial set of 18 450 bound protein–ligand crystal structures were clustered by pocket similarity and then input to a combinatorial docking procedure. Each ligand was re-docked to its known receptor and cross-docked to every other receptor with a similar pocket. Though noisier than crystallized or re-docked poses, cross-docking greatly increases the amount of training data and captures the distribution of structures that are typically used in drug discovery. The CrossDocked2020 data set has cross-validation splits based on pocket similarity. We used the first split to construct our training and test data sets. We omitted any poses that had root-mean-squared deviation (RMSD) greater than 2 Å from the crystal pose of the ligand in its cognate receptor. We also omitted molecules that could not be sanitized with RDkit.50The CrossDocked2020 data set is a massive collection of small molecules docked into cognate and non-cognate receptors.2.7.We randomly selected ten targets from the CrossDocked2020 test set to evaluate our model. Each target came from a different pocket cluster, and we only considered targets with at least five unique ligands. We used the top-ranked docked pose of each ligand in the set. The test targets and ligands are shown in 2.8.Our model has two distinct sampling modes called posterior and prior sampling. The difference is whether the generative process is biased towards a particular real protein–ligand interaction, or if it is only based on the conditional receptor. With posterior sampling, a real protein–ligand complex is encoded into the latent variable parameters before drawing samples. In contrast, prior sampling draws latent vectors from a standard normal distribution, so it has no intentional bias towards a specific real ligand. Using either method, the latent vectors are combined with the conditional receptor encoding before decoding an output ligand density. The known ligand for the conditional receptor is called the reference molecule, which is the same molecule provided to the input encoder for posterior sampling.λvar. This parameter scales the standard deviations used to sample the latent space:We investigated different levels of sampling variance through a setting called the variability factor, denoted λbias here:We also created a technique for controlling of the amount of bias towards the reference molecule. Instead of using either the posterior or prior distribution, we can sample distributions whose parameters are linearly interpolated between those of the prior and posterior according to a bias factor, referred to as For every sampling method that we evaluated, we generated 100 samples for each protein–ligand complex in the test set.2.9.51 We measured the internal energy and root-mean-squared-deviation (RMSD) of the molecules via UFF minimization. Both real and generated molecules then underwent Vina minimization and scoring with respect to the receptor. Lastly, we estimated the binding affinity of the minimized structures using an ensemble of CNN scoring functions that were trained on the CrossDocked2020 data set. The Vina minimization and CNN affinity prediction were performed using gnina, a deep learning-based molecular docking program.9We measured the validity, novelty, and uniqueness of the molecules generated from our model, which were defined as follows: a molecule is valid if it consists of a single connected fragment and is able to be sanitized by RDkit, which checks valency constraints and attempts to kekulize aromatic bonds. A molecule is novel if its canonical SMILES string was not in the training set. A molecule is unique if its canonical SMILES string was not generated already in the course of test evaluations. We also relaxed the internal bond lengths and angles of each generated molecule in the context of the binding site by Universal Force Field (UFF) minimization.3.3.1.98.5% of molecules generated from posterior sampling were valid and 90.9% generated from prior sampling were valid, as seen in The distribution of Tanimoto fingerprint similarity in Two measures of molecular shape similarity are depicted in Fig. S4.52 between real and generated molecules is shown in Fig. S5.Prior molecules were smaller than posterior molecules by about 50 Da, but there was considerable overlap in their molecular weight distributions, shown in 3 kcal mol−1 and −104 kcal mol−1 for posterior and prior molecules, respectively, compared to −102 kcal mol−1 for real molecules. During UFF minimization, the conformation changed by less than 2 Å in 91.3% of posterior molecules and 81.0% of prior molecules.When minimizing the energy of the generated molecules with UFF, we measured both the change in energy and the RMSD of the initial pose to the minimized pose. Fig. S5The relative stability of the generated molecules in the receptor binding site was quantified as the difference in Vina energy and CNN predicted binding affinity compared with the reference molecule, which was also minimized with Vina. The Vina energy and predicted affinity of posterior molecules were similar to those of the reference molecule, but shifted slightly towards higher energy and lower affinity. The Vina energy of prior molecules was significantly higher than reference ligands, and the predicted affinity tended to be lower and more variable. The diversity in the generated molecules was represented in their Vina energy and CNN affinity scores. Some structural differences decreased the stability and others improved it. 30.8% of posterior molecules and 17.3% of prior molecules had lower minimized Vina energy than the reference molecule. Moreover, 15.4% of posterior molecules and 15.9% of prior molecules had greater predicted affinity than the reference molecule after minimization. That is to say, a significant minority of sampled molecules were predicted to bind more strongly to the receptor than the reference ligand.Fig. S6Fig. S7Fig. S8Fig. S93.2.We tested whether controlling the amount of sampling variability would alter the distribution of generated molecules in useful ways. Increasing the variability factor caused a corresponding increase in the diversity of the generated molecules, while decreasing this parameter reduced the diversity. Specifically, molecules generated from the posterior appeared more similar to the input ligand when using a lower variability factor. This is reflected in Fig. S10,The effect of the variability factor on prior molecules, exemplified in the second row of Next we evaluated the impact of sampling from interpolated latent distributions using the bias factor. 3.3.i.e. mutant) receptor in this evaluation, for both real and generated molecules. Therefore, these metrics can be used to determine whether the model takes the conditional receptor structure into account or if it generates similar molecules regardless.To test the extent that our model uses the conditional receptor when generating molecules, we compared molecules generated from the same reference ligand, but conditioning the generative process on mutated versions of the receptor. Then we compared the molecular similarity, difference in Vina energy, and difference in CNN affinity with respect to the reference molecule. The Vina energy and predicted affinity were calculated using the conditional (Mycobacterium tuberculosis (PDB ID: 1zyu).53 This enzyme is involved in the biosynthesis pathway for chorismate, a precursor for aromatic amino acids, in bacteria. Because it is not found in animals or plants, shikimate kinase has been proposed as a promising target for the development of non-toxic antimicrobial agents for a variety of uses including the treatment of tuberculosis.54 We focused on the shikimate binding site of the kinase in this work, which has been suggested to be more druggable than the ATP binding site. The residues that were identified in the literature as important for binding with shikimate are Asp34, Arg58, Glu61, Gly80, Gly81, and Arg136. Out of these, the charged residues Asp34, Arg58, and Arg136 were most frequently found to interact with the ligand. Asp34 and Arg58 were specifically depicted participating in hydrogen bonds.The target we selected for this analysis was shikimate kinase from We considered every residue of shikimate kinase within 5 Å of the docked pose of shikimate as a binding pocket residue. For each of these, we mutated the receptor by replacing the residue with alanine. For charged residues, we created additional mutants by replacing the residue with an oppositely charged residue of similar size. Finally, we created four additional structures with multi-residue mutations. One of these replaced every binding pocket residue with alanine (All). Another replaced every residue known to be relevant for binding shikimate with alanine (All interacting). A third replaced every binding pocket residue not considered relevant for binding with alanine (All non-interacting). Finally, we created a mutant that replaced every charged binding pocket residue with an oppositely charged one of similar size (All charges).Molecules generated based on mutated shikimate kinase receptors are depicted in The single-residue shikimate kinase mutations had varying effects on the generated molecules. Only a few mutations of the non-interacting residues caused notable effects. Replacing Pro11 with alanine transformed the carboxylic acid into a phosphate group. Changing Phe49 to alanine created a hole towards the right rear of the pocket where the model extended a rigid alcoholic tail. Transforming Leu132 to alanine extended the carboxylic acid into the new space by inserting a nitrogen atom. The rest of the non-interacting mutations caused only modest scaffold alterations, but mutating the interacting residues caused more interesting effects. Replacing Asp34 with alanine inserted a carbon to form a ring out of the two former hydroxyl groups, which was consistent with the reduced polarity and increased space at the front of the pocket. Replacing Asp34 with lysine broke the ring by removing a carbon, but kept the hydroxyl groups that might interact with the lysine. Converting Arg136 to alanine transformed the bottom oxygen of the carboxylic acid into an aliphatic tail extending into the open space. Mutating Arg136 to glutamic acid turned the carboxylic acid group from a hydrogen bond acceptor into a donor by adding a nitrogen. Surprisingly, none of the mutations to Arg58, Gly80, or Gly81 resulted in drastic chemical changes, despite that these were mentioned in past work as relevant for binding.α = 0.05 with one-sided alternative hypotheses. We tested whether conditioning on the mutant decreased the molecular similarity and change in Vina energy, and whether it increased the change in predicted binding affinity for the conditional receptor.To test whether conditioning on mutated receptors altered the generated molecules, we ran Kolmogorov–Smirnov tests on the distributions of fingerprint similarity, change in Vina energy, and change in CNN predicted binding affinity with respect to the reference molecule. The distributions we compared were for the posterior molecules conditioned on the mutant and the ones conditioned on the wild type receptor, which are shown in Fig. S13.For posterior molecules, the distributions of molecular similarity and change in predicted affinity were significantly different for each of the multi-residue mutations and several single-residue mutations. Modifying the interacting residue Arg136 caused significant differences in all three metrics. Mutating Asp34 to alanine significantly decreased the similarity and increased the change in predicted affinity, but did not decrease the change in Vina energy. Mutating Arg58 to alanine resulted in no significant differences. Interestingly, inverting the charge of either Arg58 or Asp34 only caused significant differences in the molecular similarity, but did not improve the change in Vina energy or predicted affinity. It was also unexpected that mutating the non-interacting residue Phe49 caused significant differences in all three metrics.We tested for differences in the property distributions of prior molecules generated from mutant receptors, shown in Fig. S14,3.4.We explored the latent space of our model further through interpolation between different known ligands of shikimate kinase. Our test set contained seven different binders for this target, four of which were bound at the shikimate active site. We encoded each of these four ligands with our model to obtain posterior latent vectors, then performed a spherical interpolation in latent space passing through each latent vector, and decoded molecules along the resulting latent trajectory. Each latent vector was decoded using the conditional information from the shikimate kinase receptor, and the center of the conditional grids were interpolated smoothly between the real ligand centers. The resulting interpolation can be viewed in The initial prior molecule was quite dissimilar from the endpoint of the first interpolation (reference molecule 1), but the intermediate steps resembled each endpoint. The molecule halfway between them has three hydroxyl groups bound to an aliphatic ring, but one phosphate was retained from the prior molecule in place of the carboxylic acid of reference molecule 1. The next endpoint (reference molecule 2) was extremely similar to the first, which was reflected in the interpolation between them. The conserved atoms smoothly moved through space except for an abrupt change in chirality at one of the ring carbons. A smooth translation of this carbon would have resulted in geometry with a different hybridization state and bond orders, so this sudden chiral shift better maintains the chemical similarity to the endpoints. The third endpoint (reference molecule 3) had a slightly different scaffold, but similar functional groups. The trajectory passed through a few strained molecules with small rings that have high overall shape similarity. The final interpolation gradually transformed the newly added ring of reference molecule 3 into an alkyl branch that then became a diol before finally ending at the phosphate group of reference molecule 4.4.We have demonstrated for the first time the ability to generate three dimensional molecules conditional on receptor binding pockets with deep learning. Over 90% of the generated molecules were valid, novel, and unique, though these metrics are insufficient to evaluate the quality of 3D molecular conformations. For this, we highlight the fact that over 80% of the generated molecules moved less than 2 Å RMSD when minimized with UFF, which provides an indication of their energetic stability within the binding site. To further emphasize the potential utility of our model for discovering active molecules, a significant number of generated molecules had lower Vina energy and higher predicted binding affinity for the receptor than the reference molecule.55The generated molecules tended to have more strain than real molecules which is why we relaxed their internal bond lengths and angles with UFF. This is probably due to the lack of explicit bond information used by the model, which instead relies only on the relative positions of local density maxima to determine atom locations. Small differences in the generated density can alter the position of a single atom, which can translate into disproportionately high energy. One promising avenue for future work is to integrate an energetic term into the loss so that the model is directly trained to produce stable molecules. This would require the ability to differentiate through the atomic coordinates, which is easier with an atomistic representation than atomic density grids. We are currently exploring a multi-modal approach that combines density grids with 3D molecular graphs to learn both global shape and inter-atomic features.We successfully showed that our generative model conditions its output on the receptor structure. We qualitatively and statistically verified that the model produced chemically relevant modifications in the generated molecules when conditioned on shikimate kinase mutants. All multi-residue mutations caused significant changes in the properties of generated molecules, and some of the important single-residue mutations did as well. Modifying Arg136 caused significant changes in all relevant properties we assessed, for both posterior and prior molecules. There were some differences between what residues were reported in the literature as important for binding compared with the mutations that caused changes in our model's output. For instance, Arg58 mutations did not tend to cause significant changes, even though it was described as a hydrogen bond participant. Phe49 mutations had a significant impact on posterior molecules even though this residue was not described as interacting with shikimate. It was also surprising that inverting charges did not cause more drastic changes to the generated molecules. This could be due to inconsistent charges and protonation states in the training data.e.g. aromatic carbon rings), the density of each atom can overlap and produce a peak in the middle of the ring or bond. This makes it difficult to resolve the individual atoms through atom fitting. By reducing the radius of the density kernel, it becomes easier to distinguish atoms in close proximity at a given grid resolution, thereby producing more accurate and chemically realistic structures. Optimization of these grid settings had a significant impact on the quality of the generated molecules.We also found that the generation of molecules using atomic density grids is quite sensitive to the hyperparameters of the grid representation. Augmentation of training with random rotations was crucial for counteracting the coordinate-frame dependency of the density grids. There was also an interaction between the grid resolution and atomic radius used in the density kernel. When several atoms are nearby in the same grid channel (In future work, we will experiment with training setups that emphasize the use of the conditional receptor. One interesting augmentation would be to apply different random rotations on the input branch and conditional branch, then train the model to generate ligands in the conditional coordinate frame. This would encourage a coordinate frame-invariant latent space and enforce reliance on the structural characteristics of the conditional receptor to determine the generated ligand orientation. Another enhancement would be to train using different receptors within the same binding pocket cluster of the CrossDocked2020 set in the input and conditional branch. A more challenging future direction would be to provide higher-RMSD ligand poses as the input to the model and train using the lowest-RMSD pose as the label, essentially performing instantaneous minimization for docking.We hope that this work accelerates the usage of 3D protein structure in molecular generative models. There is vast potential for further development of this approach. To enable the reproduction and extension of this work, we provide open access to all data, code, and model weights.https://github.com/mattragoza/liGAN.The data sets, code, and model weights used in this work are available for download at Matthew Ragoza: conceptualization, software, resources, data curation, methodology, investigation, validation, visualization, formal analysis, writing – original draft, project administration.Tomohide Masuda: conceptualization, software, investigation, methodology, resources, validation, writing – review & editing.David Ryan Koes: conceptualization, software, resources, investigation, validation, writing – review & editing, project administration, funding acquisition.There are no conflicts to declare.SC-013-D1SC05976A-s001


Mechanical characterization and tensile testing of biological samples is important when determining the material properties of a tissue; however, performing tensile testing and tissue stretching while monitoring cellular changes via fluorescence microscopy is often challenging. Additionally, commercially available cell/tissue stretchers are often expensive, hard to customize, and limited in their fluorescence imaging compatibility. We have developed a 3D printed Open source Biaxial Stretcher (OBS) to be a low-cost stage top mountable biaxial stretching system for use with live cell fluorescence microscopy in both upright and inverted microscope configurations. Our OBS takes advantage of readily available open source desktop 3D printer hardware and software to deliver a fully motorized high precision (10 ± 0.5 µm movement accuracy) low cost biaxial stretching device capable of 4.5 cm of XY travel with a touch screen control panel, and an integrated heated platform with sample bath to maintain cell and tissue viability. Further, we designed a series of tissue mounts and clamps to accommodate varying samples from synthetic materials to biological tissue. By creating a low-profile design, we can directly mount the stretcher onto a microscope stage, and through coordinated biaxial stretching we maintain a constant field of view facilitating real-time sample tracking and time-lapse fluorescence imaging. Our goals in the creation of the OBS were to: (1) utilize open source 3D printer hardware, software, and electronics for construction of a low cost biaxial stretching device, (2) create a low profile design capable of direct mounting on top of a microscope stage, (3) maintain a constant field of view when performing biaxial stretching for effective sample tracking and time lapse imaging, and (4) design submersible, removable, tissue mounts and clamps to accommodate varying sample types and sizes. The result is a customizable, inexpensive, 3D-printable biaxial stretcher capable of 4.5 cm of XY travel, with an integrated heated platform to maintain cell and tissue viability . Full co1.2Commercial biaxial stretchers, while high performance devices, can cost between $20,000 and $100,000  depending on the capabilities and configurations one chooses. They are also limited in their customization due to precision machined components and a mix of proprietary hardware and software that can be difficult to adapt to new use cases for which they were not originally designed. Several research groups have designed their own uniaxial or biaxial stretching devices for mechanical characterization of soft materials and biological specimens Bill of Materials). With the addition of 3D printed interchangeable tissue clamps and rakes to accommodate a range of samples, the OBS system offers a unique opportunity for researchers to implement a customizable biaxial stretching system into their experimental workflow. Most of the parts were printed using a Prusa i3 MK3 desktop 3D printer, and the remaining components such as the Duet WiFi control board, PanelDue 5i screen, power supply, steel rods, nuts, bolts, and belts were purchased from online retailers.To address this issue of cost, design flexibility, and performance, we utilized desktop 3D printing technology and readily available 3D printer-based hardware and electronics to construct the high-performance OBS for $580  with custom mounting brackets. The top of the enclosure has mounts for the PanelDue display enclosure. The stepper motor cables, heater bed plate cables, PanelDue cables, and input power are routed into this enclosure. The 12 V power supply is mounted to this enclosure using the PowerSupplyMountingPlate that fits through the ventilation grates and mounts to the power supply.nclosure : The DueAcrylic Build Platform : The acrylic build plate is laser cut to provide precise mounting locations for the stretching components and a windowed cutout for sample bath and use with an inverted microscope.Platform : The acrHeated Bed Plate : A 200 × 200 mm silicone heater mat is adhered to a ⅛″ thick aluminum heat spreader. The heat spreader is coupled with silicone thermal tape to a ⅛″ borosilicate glass.ed Plate : A 200 ×Sample Bath: This bath fits into the cutout in the acrylic build platform and can rest upon the heated bed plate. The bath allows the sample to remain submerged in the appropriate fluid maintained at the appropriate temperature.Stepper Motors : One stepper motor is needed for each of the orthogonal axes. These stepper motors stretch the samples along each axis using a belt and pulley transmission that turns their rotation into linear motion.r Motors : One steMotorMounts : These stepper motor mounts are mounted at two specific precut locations on the acrylic platform. They support the stepper motors and prevent them from moving.orMounts : These sLong (155 mm) and Short (140 mm) Linear Rods : These linear rods, one pair for each orthogonal axis, simultaneously transfer the motion of the stepper motors to the pulley driven timing belts and allow for translation of the orthogonal axes along them. One each of the long and short rods is coupled to a stepper motor. These rods are supported by the acrylic platform mounted rod support housings.ear Rods , Fig. 12Stepper Motor Coupling : These couplers clamp to the stepper motor drive shaft and the linear rods to transfer the rotation of the stepper motors to the rods.Coupling : These cRodSupports : These rod support housings are mounted at specific precut locations on the acrylic platform. Each housing has an 8 mm sleeve bearing through which the long and short linear rods run.Supports : These r28 and 32 tooth GT2 Pulleys : These pulleys (28 tooth for the long linear rods and 32 tooth for the short linear rods) mate with the timing belts to transfer the rotation of the linear rods to the linear motion carriages. Pulleys , Fig. 13BeltTensionerC, D, G, H : One pair of tensioning linear motion carriages is needed for each of the orthogonal axes. These carriages simultaneously tension the timing belts that drive the biaxial stretching motion and couple one each of the long and short crossbars to the belt. D, G, H , Fig. 17BeltTensionerA, B, E, F : One pair of rider linear motion carriages is needed for each of the orthogonal axes. These carriages couple one each of the long and short crossbars to the belt. B, E, F , Fig. 17Crossbeams : One pair each of long and short crossbars mount to the BeltTensioners on each of the orthogonal axes. These crossbars are where sample mounting hardware is affixed.ossbeams , Fig. 12Long and Short Sample Mounting Rakes and Jigs : These sample mounting rakes  are constructed by inserting prebent 23 gauge short bevel needles into the alignment holes within the mounting rakes. To align and permanently affix the needle rakes at various lengths, the needle alignment jig is used. The assembled mounting rakes are affixed to the pairs of long and short crossbars. The rakes are aligned at the center of the sample area. These rakes penetrate the sample and couple it to the motion of the long and short crossbars.and Jigs : These s45The majority of the OBS is 3D printed from PLA filament. All STL files have been prepared and saved in their intended printing orientation. During the printing process no supports are required. All parts were printed with 60% infill and 2 perimeters with a layer height of 0.250 mm. Some print settings may vary depending upon the printer used .5.1(1x) BeltTensionerA Pt1(1x) BeltTensionerB Pt1(1x) CrossbeamAB(2x) High-Load Dry-Running Bearing with Steel Shell(6x) 10 mm M3x.5 mm Socket Head Bolt(6x) Metric Medium-Strength Steel Thin Hex NutsBegin assembly of the OBS by constructing the crossbeams, the first crossbeam is the AB crossbeam . For thiPress fit one bearing each into BeltTensionerA Pt1 and BeltTensionerB Pt1 (red arrow). Insert two thin hex nuts into the slots (blue arrow) on each and tighten to matching side of CrossbeamAB using the 10 mm M3 bolts. Finally insert one hex nut into the recess of each BeltTensioner’s clamp (yellow arrow) and thread a 10 mm M3 bolt.5.2(1x) BeltTensionerC Pt1(1x) BeltTensionerC Pt2(1x) BeltTensionerD Pt1(1x) BeltTensionerD Pt2(1x) CrossbeamCD(2x) High-Load Dry-Running Bearing with Steel Shell(4x) 10 mm M3x.5 mm Socket Head Bolt(6x) Metric Medium-Strength Steel Thin Hex Nuts(2x) 25 mm M3x.5 mm Socket Head BoltAssemble the second crossbeam, the CD crossbeam . For thiPress fit one bearing each into BeltTensionerC Pt1 and BeltTensionerD Pt1 (red arrow). Insert two thin hex nuts into the slots (blue arrow) on each and tighten to matching side of CrossbeamCD using 10 mm M3 bolts. Insert one hex nut into the recess of each BeltTensioner’s clamp (yellow arrow). Finally thread a 25 mm M3 bolt (purple arrow) through each BeltTensionerC Pt2 and BeltTensionerD Pt2 to attach to the corresponding Pt1 components.5.3(1x) BeltTensionerE Pt1(1x) BeltTensionerF Pt1(1x) CrossbeamEF(2x) High-Load Dry-Running Bearing with Steel Shell(6x) 10 mm M3x.5 mm Socket Head Bolt(6x) Metric Medium-Strength Steel Thin Hex NutsAssemble the third crossbeam, the EF crossbeam . For thiPress fit one bearing each into BeltTensionerE Pt1 and BeltTensionerF Pt1 (red arrow). Insert two thin hex nuts into the slots (blue arrow) on each and tighten to matching side of CrossbeamEF using the 10 mm M3 bolts. Finally insert one hex nut into the recess of each BeltTensioner’s clamp (yellow arrow) and thread a 10 mm M3 bolt.5.4(1x) BeltTensionerG Pt1(1x) BeltTensionerG Pt2(1x) BeltTensionerH Pt1(1x) BeltTensionerH Pt1(1x) CrossbeamGH(2x) High-Load Dry-Running Bearing with Steel Shell(4x) 10 mm M3x.5 mm Socket Head Bolt(6x) M3 Metric Medium-Strength Steel Thin Hex Nuts(2x) 25 mm M3x.5 mm Socket Head BoltAssemble the fourth crossbeam, the GH crossbeam . For thiPress fit one bearing each into BeltTensionerG Pt1 and BeltTensionerH Pt1 (red arrow). Insert two thin hex nuts into the slots (blue arrow) on each and tighten to matching side of CrossbeamGH using 10 mm M3 bolts. Insert one hex nut into the recess of each BeltTensioner’s clamp (yellow arrow). Finally thread a 25 mm M3 bolt (purple arrow) through each BeltTensionerG Pt2 and BeltTensionerH Pt2 to attach to the corresponding Pt1 components.5.5(6x) RodSupport(1x) RodSupport Rotated Feet(1x) RodSupport Rotated Feet Mirror(16x) M5 Metric Medium-Strength Steel Thin Hex Nuts(8x) High-Load Dry-Running Bearing with Steel ShellAssemble the 8 RodSupports . You neePress fit one bearing (red arrow) into each of the RodSupport pieces. Next Fit two M5 thin hex nuts into each RodSupport Piece (blue arrow).5.6(1x) AB Crossbeam Assembly(1x) CD Crossbeam Assembly(2x) RodSupport(1x) RodSupport Rotated Feet(1x) RodSupport Rotated Feet Mirror(4x) 28 Tooth GT2 Pulley(4x) PTFE Washer(8x) 16 mm M5 Low-Profile Socket Head Bolt(2x) 155 mm long 8 mm diameter linear rod(4x) M3 Metric Medium-Strength Steel Thin Hex Nuts(4x) 3 mm M3x.5 mm Cup Point Set ScrewBegin to mount the OBS to the acrylic build platform . For thiFirst, insert the 155 mm long 8 mm diameter linear rods (red arrow) into either side of the AB and CD Crossbeam assemblies. Center the assemblies on the linear rods. Next, on the B and D side add 1 RodSupport assembly to either side of the Crossbeam assemblies. Next, on the C side add the RodSupport Rotated Feet assembly and on the A side add the RodSupport Rotated Feet Mirror assembly. Align these RodSupport assemblies with the corresponding holes in the acrylic build platform. Thread 16 mm M5 Low-Profile socket head bolts through each hole and fasten the assemblies to the acrylic build platform. Next place one PTFE washer (yellow arrow) on the outer sides of the RodSupport Assemblies.Insert one M3 thin hex nut into the backside of the pulley  EF Crossbeam Assembly(1x) GH Crossbeam Assembly(4x) RodSupport(4x) 32 Tooth GT2 Pulley(4x) PTFE Washer(8x) 16 mm M5 Low-Profile Socket Head Bolt(2x) 140 mm long 8 mm diameter linear rod(4x) M3 Metric Medium-Strength Steel Thin Hex Nuts(4x) 3 mm M3x.5 mm Cup Point Set ScrewContinue to mount the OBS to the acrylic build platform . For thiFirst, insert the 140 mm long 8 mm diameter linear rods (red arrow) into either side of the EF and GH Crossbeam assemblies. Center the assemblies on the linear rods. Next, on both sides of the assemblies add 1 RodSupport assembly to either side of the Crossbeam assemblies. Align these RodSupport assemblies with the corresponding holes in the acrylic build platform. Thread 16 mm M5 Low-Profile socket head bolts through each hole and fasten the assemblies to the acrylic build platform. Next place one PTFE washer on the outer sides of the RodSupport Assemblies.Assemble the 32 tooth GT2 Pulleys:Insert one M3 thin hex nut into the backside of the pulley  0.9-degree NEMA 17 Stepper motor(4x) 10 mm M3x.5 mm Socket Head Bolts(1x) MotorMount(4x) M5 Metric Medium-Strength Steel Thin Hex Nuts(2x) 5 mm to 8 mm shaft couplingAssemble the two motor assemblies , you wilFor each assembly mount the stepper motor to the MotorMount using 2 10 mm long M3x.5 mm socket head bolts (red arrow). Next, insert two M5 thin hex nuts into their recesses (blue arrow). Finally, clamp the 5 mm side of the shaft coupling to the stepper motor shaft (yellow arrow).5.9(2x) Motor Assemblies(4x) 16 mm M5 Low-Profile Socket Head BoltContinue to mount the OBS to the acrylic build platform . For thiLoosen the clamping bolts on the shaft couplings to allow them to slide along the motor shaft. For each motor assembly slide the shaft coupling onto the corresponding 8 mm linear rod. Align the holes in the motor mounts with the associated holes in the acrylic build platform. Thread 16 mm M5 Low-Profile socket head bolts through each hole and fasten the assemblies to the acrylic build platform. Adjust the shaft couplings so that they capture a sufficient portion of the 5 mm motor shafts and the 8 mm linear rods. Next, tighten the shaft coupling clamping bolts.5.10(2x) Long 490 mm GT2 Belts(2x) Short 360 mm GT2 Belts(4x) Small ziptiesContinue to assemble the OBS . For thiFrom the long roll of GT2 belt, cut belts for each component of the OBS. Loosen the tensioning elements on parts C, D, G, and H (red arrow) and measure the short and long belts such that there will be 1 cm of extra belt on either side of the belt recesses (blue arrow). Insert the belt into the belt loop recesses (blue arrow). The belt teeth will interlock making a secure connection.5.11After cutting the two short and long GT2 belts, wrap them around the pulleys, starting with the two shorter belts . Feed th5.12(32x) 23 g needles(1x) RakeBendingJig(2x) LongSampleMountingRake(2x) ShortSampleMountingRake(4x) RakeMountingJig(8x) M3 Metric Medium-Strength Steel Thin Hex Nuts(8x) 10 mm M3x.5 mm Socket Head BoltsNext, assemble the sample mounting rakes , for thiBend the 32 needles using the rake bending jig. Next, insert 8 bent rakes into each of the long and short sample mounting rake parts. Apply superglue to the rear of the needles and align with the associated rake mounting jigs (red arrow). Clamp and let sit to cure, according to your glue. To finish assembly slot 2 M3 thin nuts into each sample mounting rake (blue arrow), and thread one 10 mm M3 socket head bolt (yellow arrow) into each of those.5.13(2x) Long Sample Mounting Rake(2x) Short Sample Mounting RakeTo finish assembly of the OBS , you wilMount the 2 long sample mounting rakes (red arrow) to the opposing GH and EF crossbeams by gently tightening the 10 mm M3 socket head bolts in each rake. Mount the 2 short sample mounting rakes (blue arrow) to the opposing AB and CD crossbeams by gently tightening the 10 mm M3 socket head bolts in each rake. Mount sample by placing under the sample rakes and pressing upwards into the needle array. Alternative means of sample mounting and grasping can be used and designed to fit onto the crossbeams.5.14(1x) Duet Wifi(1x) PanelDue 5I(1x) DuetCaseBottom(1x) DuetCaseLid(2x) CaseAngles(1x) PanelDue5ICaseRear(1x) PanelDue5ICaseFront(8x) 10 mm M3x.5 mm Socket Head Bolts(8x) 14 mm M3x.5 mm Socket Head Bolts(8x) M3 Metric Medium-Strength Steel Thin Hex Nuts(1x) PowerSupplyMountingPlateTo assemble the Duet WiFi Enclosure with PanelDue 5I , you wilhttps://duet3d.dozuki.com/Bolt the Duet Wifi to the DuetCaseBottom with 4 10 mm M3 socket head bolts A. Next, 6To operate the OBS first power it on. The PanelDue 5i comes preloaded with a graphical user interface to control the Duet WiFi. Select the [Active °C] button on the main screen of the PanelDue 5i to begin warming the heated bed plate to the temperature required for the experiment A. Mount 7For initial validation and characterization, we chose to perform biaxial and uniaxial tensile testing on a thin film of polydimethylsiloxane (PDMS). Fiducial marks were placed ~5 mm apart in a square orientation on the top surface of the PDMS to track displacement during stretching. Time-lapse videos were acquired to visualize the deformation during tensile testing and for quantitative evaluation. Engineering strain  according to previously published protocols The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Eif2b5. We performed sequencing analysis to assess gene correction rates and examined effects on the VWM phenotype, including motor behavior. Sequence analysis demonstrated that over 90% of CRISPR/Cas9-induced edits at the targeted locus are insertion or deletion (indel) mutations, rather than precise corrections from the DNA donor template by homology-directed repair. Around half of the CRISPR/Cas9-treated animals died prematurely. VWM mice showed no improvement in motor skills, weight, or neurological scores at 7 months of age, and CRISPR/Cas9-treated controls displayed an induced VWM phenotype. In conclusion, CRISPR/Cas9-induced DNA double-strand breaks (DSBs) at the Eif2b5 locus did not lead to sufficient correction of the VWM variant. Moreover, indel formation in Eif2b5 induced an exacerbated VWM phenotype. Therefore, DSB-independent strategies like base- or prime editing might better suited for VWM correction.Vanishing white matter (VWM) is a leukodystrophy caused by recessive variants in subunits of eIF2B. At present, no curative treatment is available and patients often die at young age. Due to its monogenic nature, VWM is a promising candidate for the development of CRISPR/Cas9-mediated gene therapy. Here we tested a dual-AAV approach in VWM mice encoding CRISPR/Cas9 and a DNA donor template to correct a pathogenic variant in   Vanishing white matter (VWM) is a devastating leukodystrophy caused by bi-allelic variants in genes related to mRNA translation. No curative treatment is available and patients often die at young age. We tested CRISPR/Cas9-mediated gene therapy using dual AAV. However, low correction rates and indel formation lead to exacerbated VWM phenotypes. VWM is one of the most prevalent leukodystrophies,EIF2B1–EIF2B5). The disease is characterized by chronic and episodic neurological decline, including motor deficits, and can result in early death.,Eif2b5, by transplanting healthy glial cells into newborn pups. However, only partial improvement in the VWM phenotype was observed.in vivo in the brains of newborn pups via CRISPR/Cas9, to assess if this technology can be applied to alleviate the VWM disease phenotype.Here, we aim to employ CRISPR/Cas9 for the treatment of the monogenic neurodegenerative disease vanishing white matter  upstream of the R191H variant, generating a DSB in close proximity (8 bp) to the variant. Two constructs were generated  in the lateral ventricles of neonatal mice. A control group (referred to as control) was injected with only the Cas9-encoding AAV and saline (n = 79). As expected from previous studiesTo correct the pathogenic enerated A and 1C:ectamine B. To tesd saline D, n = 79r = .877; Eif2b5hom and healthy Eif2b5het animals (see r = .870), with an average HDR rate  roughly 17 times lower than that of NHEJ . These results suggest that even if the activity of CRISPR/Cas9 nucleases could be increased, e.g., by using higher viral titers or stronger promoters, correction efficiency would remain low due to the majority of edits at the locus being indel mutations.To discriminate between indel formation and precise correction of the R191H variant by HDR, next generation sequencing (NGS) was performed on amplicons generated by PCR on genomic DNA isolated from brain lysates. To avoid amplification of randomly integrated DNA donor templates, a primer pair binding outside of the HDR template sequence was used. A significantly higher percentage of both NHEJ (T(50.1) = 19.1, p < .001; mals see . Howeverhom and CRISPRhet mice were equally affected  and did not differ in the average age of death. Since no outward phenotype was apparent in the control group receiving AAV-Cas9 alone, it is unlikely that the deterioration seen in the CRISPR animals was due to Cas9 expression.Starting from 5 weeks after treatment, CRISPR animals experienced unexpected premature death C, showinSaCas9 and no sgRNA, died without clear cause  and CRISPRhet (n = 30) animals, nor did the percentages of either NHEJ (s(23) = -.401, p = .058; Eif2b5 locus.As the animals aged, the number of deaths decreased  weighed significantly less than CRISPRhet (n = 42) mice at the first weighing  = 9.178, p < .001). This weight difference between genotypes was not observed in the control groups until later in life (H(3) = 35.991, p < .001 at 7 months of age, controlhom n = 21, controlhet n = 25), as expected from the VWM phenotype. The Neuroscore of both CRISPRhom and CRISPRhet animals was worsened by CRISPR/Cas9 treatment compared with controlhet animals  = 24.936, p < .005) and was matched by controlhom animals only later in life, when this latter group started to deteriorate due to the VWM genotype -stained brain sections. We observed necrotic tissue in the brainstem of CRISPR mice  becomes more lax during adulthood.,The sharp decline in viability of our CRISPR/Cas9-treated mice started at 5 weeks of age. In mice, myelination starts in the spinal cord at birth and increases rapidly until at least P45.Eif2b5hom VWM animals treated with CRISPR/Cas9 were unimproved on a series of phenotypic assays compared with Eif2b5hom animals in the control group. Moreover, indel formation following CRISPR/Cas9 treatment induced a VWM phenotype in normally healthy Eif2b5het animals: these animals displayed worsened Neuroscores and poorer performance on motor assays when compared with Eif2b5het animals that only received the Cas9-encoding vector. Moreover, involvement of the brainstem was observed in CRISPR/Cas9-treated animals, which is also part of the VWM phenotype. Involvement of the brainstem has been observed in severe patients during rapid decline.,Eif2b5 by indel formation. While co-localization of a necrosis marker with Cas9 and sgRNA would be required to directly attribute the necrosis to CRISPR/Cas9-mediated gene edits, our data suggest that CRISPR/Cas9 treatment resulted in a more severe VWM phenotype, including necrosis in the brain stem.Surviving animals did not show increased correction compared with animals that died prematurely. Instead, Eif2b5 alleles. Despite AAV transduction rates and editing rates comparable with a previous study using ICV injection of AAV-encoding CRISPR-Cas9,Together, our data show that the standard CRISPR/Cas9 nuclease technology could not be used to efficiently correct pathological Eif2b5R191H was used to assess the effects of CRISPR/Cas9 treatment on the VWM phenotype. These mice have a C57Bl/6J and Rag2null background. Homozygous offspring (Eif2b5hom) develop a VWM phenotype, whereas heterozygous littermates do not and were used as healthy controls (Eif2b5het).All mouse experiments conducted in this study were approved by the Animal Approval Committee of the VU University Amsterdam and were carried out following guidelines of the Dutch government. A well-characterized mouse model for VWMa priori power analysis was performed based on previously quantified Eif2b5R191H phenotypic effects  were injected with a 1:1 mix of the virus encoding Cas9 (pMH4) together with the virus encoding the sgRNA, the DNA donor template to correct the R191H variant, and a GFP tag (pMH2). The genomic target site of the 700 bp HDR template was sequenced in the mice we used in this study to exclude the existence of mismatches in the HDR arms. The control group received injections of virus encoding for Cas9 mixed with saline.Vector pMH2_eGFP_sgRNA-EIF2B5 and vector pMH4_SaCas9  were used to produce AAV2/8 virus for ICV injections into homozygous (hom) and heterozygous (het) Eif2b5) SaCas9 on an AAV backbone containing AAV2 ITRs from Addgene plasmid #61591 (a gift from Feng Zhang) has been replaced by eGFP using AgeI and KpnI, and the spacer for targeting Eif2b5R191H (CATGGCAGTGTGTAGGGTGG) has been cloned between the U6 promoter and sgRNA scaffold using BsaI digest. The DNA donor template comprised the Eif2b5 locus sequence 350 bp upstream and downstream of the R191H variant, and was cloned into the plasmid downstream of the sgRNA scaffold using NotI digest. The donor template contained an additional silent T to A mutation five bases upstream of the R191H variant to exclude cutting of the donor template by the sgRNA. For pMH4 (SaCas9), the U6 promotor and the sgRNA scaffold from Addgene plasmid #61591 were removed and replaced by a small multiple cloning site using a KpnI and NotI digest.For pMH2  and cloned into the pPB-Zeocin backbone using HiFi DNA assembly MasterMix (New England Biolabs [NEB]). The piggyBac disease reporter plasmids and helper plasmid were transfected using Lipofectamine 2000 according to the manufacturer's instructions. For sgRNA testing, pMH2 and pMH4 were co-transfected at a 1:1 ratio using Lipofectamine 2000 according to the manufacturer's instructions. DNA was isolated using direct lysis buffer  and incubated at 60°C for 60 min and 95°C for 10 min. Targeted amplicon sequencing was performed as described below using the following forward primer CTTTCCCTACACGACGCTCTTCCGATCTNNNNNNGATCACAGAGTTGGGCAGAC and reverse primer GGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNCACTTCCAGAAGACCCAAGG.Cutting efficiency of the sgRNA was assessed −1) were determined using a Qubit 3.0 Fluorometer. In brief, the Qubit Fluorometer 3.0 (Life Technologies) was used to measure the concentrations (ng/mL) of the extracted genomes by denaturation at 95°C for 5 min, after which the readings were converted to genome copies (gc)/μL using the genome's molecular mass and Avogadro's constant. Identity of the packaged genomes of each AAV vector was confirmed by Sanger DNA sequencing by Mycrosynth AG , testing 500 ng of denatured AAV using the sequencing primer CGCAAATGGGCGGTAGGCGTG. AAV2/8 viruses were stored at −80°C until use.All pseudotyped AAV8 vectors (AAV2/8) were produced by the Viral Vector Facility of the Neuroscience Center Zurich. AAV vectors were ultracentrifuged and diafiltered. Physical titers  if deteriorating rapidly or at planned time points of 12 weeks of age or 5, 8, or 9 months of age . When unNGS was performed on brain lysates to assess the percentages of NHEJ and HDR in edited cells. DNA was isolated with UltraPure phenol:chloroform:isoamyl alcohol (Thermo Fisher) according to the manufacturers' instructions. Briefly, lysates were spun down at 13,300 RPM for 5 min to pellet the lysate. Supernatant was pipetted into a new Eppendorf tube and phenol:chloroform:isoamyl alcohol was added to lysates 1:1. Samples were vortexed vigorously for at least 20 sec before spinning down for 15 min at 13,300 RPM. After successful phase separation, the aqueous top layer supernatant was pipetted into a clean Eppendorf with care not to disturb the protein or lipid layers. Half the amount of supernatant volume of 3 M NaAc pH 4.5 was added, as well as twice the volume of 100% EtOH.Eif2b5 locus was amplified from 100 ng of genomic DNA using the primer pair (5′-CCAGGAAAGCCACAGAGGAG-3′ and 5′-CCCTAGATTTGGTTCCCAGCA-3′) in a 50-μL PCR reaction using the NEBNext High-Fidelity 2× PCR Master Mix , annealing at 64°C, for 30–35 cycles. The PCR product was purified with magnetic beads  and used as a template for a second PCR with primer pair 2 (5′-CTTTCCCTACACGACGCTCTTCCGATCTNNNNNNGACTAACTGTGCCTCTGGTTCT-3′ and 5′-GGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNGGAAGTGAAGAACCCTGTTGG-3′). In the second PCR, a smaller DNA region within the first PCR product was amplified and Illumina adaptors were added in a 25-μL PCR reaction using NEBNext High-Fidelity 2x PCR Master Mix , annealing at 55°C, for eight cycles. The PCR product from the second PCR was again purified with magnetic beads and used as a template for a third PCR reaction. In the third PCR reaction, index barcodes were added to each sample in a 25-μL PCR reaction using NEBNext High-Fidelity 2x PCR Master Mix , annealing at 55°C, for eight cycles. PCR products were validated using 2% agarose gels. Amplified libraries were pooled, gel purified, and normalized to approximately equal molar ratio using a Qubit 3.0 fluorometer. Samples were sequenced using the MiSeq Micro v2 Illumina or the MiSeq Nano v2 Illumina kit . Results were analyzed with CRISPResso2.The region of interest within the ,Animals were tested on various VWM phenotypes, including bodyweight, Neuroscores, and motor skills. Assessments of weight and Neuroscore were performed weekly from 1 month of age onward. Neuroscore was adapted from Nakamura et al.,2O) at 90°C and were left to cool down for 40 min. Slides were rinsed once in PBS and incubated with blocking buffer  for 1 h at room temperature (RT). Tissue was then incubated with primary antibodies  diluted in blocking buffer for another hour at RT prior to overnight incubation at 4°C. The next day, slides were washed in PBS and subsequently incubated in secondary antibodies diluted in blocking buffer for 2 h at RT. Slides were washed in PBS three times for 10 min once more and finally incubated with 4′,6-diamidino-2-phenylindole  for 2 min. After a final wash in PBS, slides were covered with Fluoromount  and cover slips and stored at 4°C.For immunohistochemistry, slides were washed in PBS three times in 10 min. Slides then received heat-mediated antigen retrieval treatment in citrate buffer  for 1.5 min. Slides were then rinsed under running tap water and differentiated in 1% acid alcohol (HCl in 70% EtOH) for 30 sec. Slides were rinsed once more in running tap water and subsequently blued in 0.1% sodium bicarbonate (in MilliQ H2O). Slides were stained with eosin Y (Sigma) for 1 min, rinsed in running tap water, kept in MilliQ for 10 min, and rinsed once more under running tap water. Slides were dehydrated in  EtOH, cleared in Xylene, and mounted in Depex (Serva).H&E histology was performed to analyze tissue morphology. Microscopy slides with tissue sections were washed 6 times for 5 min in PBS to remove OCT. Tissue was then briefly rinsed in MilliQ H13 vector genomes (vg)/mL, 1.3 × 1013 vg/mL, 5.2 × 1012 vg/mL, respectively. ICV injections of 2 × 1010 vg of virus per animal were performed as described in the section “ICV injections” in the “To compare the tropism and spread of three different AAV serotypes, a CMV-GFP construct (hCMV-chI-EGFP-WPRE-SV40p(A)) was used to produce AAV2/5, AAV2/8, and AAV2/9 virus as described above with titers of 1.4 × 10+ cells were determined in the cortex, ventral areas, olfactory bulb, cerebellum, hippocampus, white matter tracts , and brainstem in animals treated with serotype AAV2/5, 2/8, or 2/9.Slices were photographed with a Leica DM6000B microscope (Leica Microsystems). Cell counts were performed in ImageJ by a researcher blind to the genotype and the treatment of analyzed animals. The number of GFPa priori and performed in SPSS 26.0. Normality of the data was checked by Shapiro-Wilk test. If test assumptions were met, independent-samples t tests or one-way ANOVAs were performed to compare groups. Tukey's b was used as a post hoc. In case of non-homogeneity of variances between groups in an ANOVA, determined with a Levene's test, a Welch test with Games-Howell post hoc was used. In case of non-parametric data, Mann-Whitney U tests or Kruskal-Wallis tests were performed.Statistical tests to be used were determined