Improved prediction of protein side‐chain conformations with SCWRL4

ColabFold - Making protein folding accessible to all

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391

lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests

Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138

A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions

Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000

A Mutation in VPS35, Encoding a Subunit of the Retromer Complex, Causes Late-Onset Parkinson Disease

Zimprich, A., Benet-Pagès, A., Struhal, W., Graf, E., Eck, S. H., Offman, M. N., Haubenberger, D., Spielberger, S., Schulte, E. C., Lichtner, P., et al. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.06.008 · Cited by 816

Advances in protein structure prediction and design

Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806

Crystal structure of the entire respiratory complex I

Baradaran, R., Berrisford, J. M., Minhas, G. S., & Sazanov, L. A. (2013) · Nature · doi:10.1038/nature11871 · Cited by 797

Comparative protein structure modeling using MODELLER

Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730

Sequence co-evolution gives 3D contacts and structures of protein complexes

Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J., & Marks, D. S. (2014) · eLife · doi:10.7554/elife.03430 · Cited by 515

Interferon-induced RIP1/RIP3-mediated necrosis requires PKR and is licensed by FADD and caspases

Thapa, R. J., Nogusa, S., Chen, P., Maki, J. L., Lerro, A., Andrake, M., Rall, G. F., Degterev, A., & Balachandran, S. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301218110 · Cited by 355

Computational design of water-soluble α-helical barrels

Thomson, A. R., Wood, C. W., Burton, A. J., Bartlett, G. J., Sessions, R. B., Brady, R. L., & Woolfson, D. N. (2014) · Science · doi:10.1126/science.1257452 · Cited by 330

ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation

Leem, J., Dunbar, J., Georges, G., Shi, J., & Deane, C. M. (2016) · mAbs · doi:10.1080/19420862.2016.1205773 · Cited by 238

Deep Learning in Protein Structural Modeling and Design

Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227

Computational approaches to therapeutic antibody design: established methods and emerging trends

Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219

Cryo-EM structure of islet amyloid polypeptide fibrils reveals similarities with amyloid-β fibrils

Röder, C., Kupreichyk, T., Gremer, L., Schäfer, L. U., Pothula, K. R., Ravelli, R. B. G., Willbold, D., Hoyer, W., & Schröder, G. F. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-0442-4 · Cited by 180

Efficient flexible backbone protein-protein docking for challenging targets

Marze, N. A., Roy Burman, S. S., Sheffler, W., & Gray, J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty355 · Cited by 173

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140

Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences

Green, A. G., Elhabashy, H., Brock, K. P., Maddamsetti, R., Kohlbacher, O., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-21636-z · Cited by 114

BH3‐in‐groove dimerization initiates and helix 9 dimerization expands Bax pore assembly in membranes

Zhang, Z., Subramaniam, S., Kale, J., Liao, C., Huang, B., Brahmbhatt, H., Condon, S. G., Lapolla, S. M., Hays, F. A., Ding, J., et al. (2016) · The EMBO Journal · doi:10.15252/embj.201591552 · Cited by 93

Rotamer-free protein sequence design based on deep learning and self-consistency

Liu, Y., Zhang, L., Wang, W., Zhu, M., Wang, C., Li, F., Zhang, J., Li, H., Chen, Q., & Liu, H. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00273-6 · Cited by 72

Structural Basis of Membrane Targeting by the Dock180 Family of Rho Family Guanine Exchange Factors (Rho-GEFs)

Premkumar, L., Bobkov, A. A., Patel, M., Jaroszewski, L., Bankston, L. A., Stec, B., Vuori, K., Côté, J.-F., & Liddington, R. C. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.102517 · Cited by 68

Flanking regions determine the structure of the poly-glutamine in Huntingtin through mechanisms common among glutamine-rich human proteins

Urbanek, A., Popovic, M., Morató, A., Estaña, A., Elena-Real, C. A., Mier, P., Fournet, A., Allemand, F., Delbecq, S., Andrade-Navarro, M. A., et al. (2020) · Structure · doi:10.1016/j.str.2020.04.008 · Cited by 66

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction

Marks, C., Nowak, J., Klostermann, S., Georges, G., Dunbar, J., Shi, J., Kelm, S., & Deane, C. M. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw823 · Cited by 56

DLPacker : Deep learning for prediction of amino acid side chain conformations in proteins

Misiura, M., Shroff, R., Thyer, R., & Kolomeisky, A. B. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26311 · Cited by 51

DIP/Dpr interactions and the evolutionary design of specificity in protein families

Sergeeva, A. P., Katsamba, P. S., Cosmanescu, F., Brewer, J. J., Ahlsen, G., Mannepalli, S., Shapiro, L., & Honig, B. (2020) · Nature Communications · doi:10.1038/s41467-020-15981-8 · Cited by 38

Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins

DeBartolo, J., Taipale, M., & Keating, A. E. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003693 · Cited by 39

Structural Insight into How Bacteria Prevent Interference between Multiple Divergent Type IV Secretion Systems

Gillespie, J. J., Phan, I. Q. H., Scheib, H., Subramanian, S., Edwards, T. E., Lehman, S. S., Piitulainen, H., Sayeedur Rahman, M., Rennoll-Bankert, K. E., Staker, B. L., et al. (2015) · mBio · doi:10.1128/mbio.01867-15 · Cited by 36

L-serine treatment in patients with GRIN -related encephalopathy: a phase 2A, non-randomized study

Juliá-Palacios, N., Olivella, M., Sigatullina Bondarenko, M., Ibáñez-Micó, S., Muñoz-Cabello, B., Alonso-Luengo, O., Soto-Insuga, V., García-Navas, D., Cuesta-Herraiz, L., Andreo-Lillo, P., et al. (2024) · Brain · doi:10.1093/brain/awae041 · Cited by 36

ATP-induced asymmetric pre-protein folding as a driver of protein translocation through the Sec machinery.

Corey, R. A., Ahdash, Z., Shah, A., Pyle, E., Allen, W. J., Fessl, T., Lovett, J. E., Politis, A., & Collinson, I. (2019) · eLife · doi:10.7554/elife.41803 · Cited by 30

Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

Anishchenko, I., Kundrotas, P. J., & Vakser, I. A. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.07.035 · Cited by 29

Discovering RNA-Protein Interactome by Using Chemical Context Profiling of the RNA-Protein Interface

Parisien, M., Wang, X., Perdrizet, G., Lamphear, C., Fierke, C. A., Maheshwari, K. C., Wilde, M. J., Sosnick, T. R., & Pan, T. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.04.010 · Cited by 28

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

(2023) · doi:10.3390/biom13010132 · Cited by 28

Involvement of the GABAA receptor α subunit in the mode of action of etifoxine

Mattei, C., Taly, A., Soualah, Z., Saulais, O., Henrion, D., Guérineau, N. C., Verleye, M., & Legros, C. (2019) · Pharmacological Research · doi:10.1016/j.phrs.2019.04.034 · Cited by 26

Antibody affinity maturation by computational design

Kuroda, D., & Tsumoto, K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8648-4_2 · Cited by 26

Evidence for a trap-and-flip mechanism in a proton-dependent lipid transporter

Lambert, E., Mehdipour, A. R., Schmidt, A., Hummer, G., & Perez, C. (2022) · Nature Communications · doi:10.1038/s41467-022-28361-1 · Cited by 24

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

Xu, G., Wang, Q., & Ma, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab529 · Cited by 24

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation

Shao, Q., Jiang, Y., & Yang, Z. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00618 · Cited by 23

Antibody side chain conformations are position-dependent

Leem, J., Georges, G., Shi, J., & Deane, C. M. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25453 · Cited by 22

Simultaneous prediction of antibody backbone and side-chain conformations with deep learning

Akpinaroglu, D., Ruffolo, J. A., Mahajan, S. P., & Gray, J. J. (2022) · PLOS ONE · doi:10.1371/journal.pone.0258173 · Cited by 21

Modeling single nucleotide polymorphisms in the human AKR1C1 and AKR1C2 genes: implications for functional and genotyping analyses

Arthur, J. W., & Reichardt, J. K. V. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015604 · Cited by 18

Computational feasibility of an exhaustive search of side-chain conformations in protein-protein docking

Dauzhenka, T., Kundrotas, P. J., & Vakser, I. A. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25381 · Cited by 17

Exploring Co-occurring POLE Exonuclease and Non-exonuclease Domain Mutations and Their Impact on Tumor Mutagenicity

Shah, S. M., Demidova, E. V., Ringenbach, S., Faezov, B., Andrake, M., Gandhi, A., Mur, P., Viana-Errasti, J., Xiu, J., Swensen, J., et al. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0312 · Cited by 16

Refinement of protein-protein complexes in contact map space with metadynamics simulations

Pfeiffenberger, E., & Bates, P. A. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25612 · Cited by 15

Thermodynamic Stability and Flexibility Characteristics of Antibody Fragment Complexes

Li, T., Verma, D., Tracka, M., Casas-Finet, J., Livesay, D., & Jacobs, D. (2013) · Protein & Peptide Letters · doi:10.2174/09298665113209990051 · Cited by 13

Integration of Nanometer-Range Label-to-Label Distances and Their Distributions into Modelling Approaches

Jeschke, G. (2022) · Biomolecules · doi:10.3390/biom12101369 · Cited by 13

OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling

Xu, G., Wang, Q., & Ma, J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00847 · Cited by 12

Invariant point message passing for protein side chain packing

(2024) · doi:10.1002/prot.26705 · Cited by 10

All‐Atom Protein Sequence Design Based on Geometric Deep Learning

Liu, J., Guo, Z., You, H., Zhang, C., & Lai, L. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202411461 · Cited by 10

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer

Xu, G., Luo, Z., Yan, Y., Wang, Q., & Ma, J. (2024) · Structure · doi:10.1016/j.str.2024.03.015 · Cited by 8

Structural Biology Helps Interpret Variants of Uncertain Significance in Genes Causing Endocrine and Metabolic Disorders

Ittisoponpisan, S., & David, A. (2018) · Journal of the Endocrine Society · doi:10.1210/js.2018-00077 · Cited by 8

Optimizing Epitope Conformational Ensembles Using α-Synuclein Cyclic Peptide “Glycindel” Scaffolds: A Customized Immunogen Method for Generating Oligomer-Selective Antibodies for Parkinson’s Disease

Hsueh, S. C. C., Aina, A., Roman, A. Y., Cashman, N. R., Peng, X., & Plotkin, S. S. (2022) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00567 · Cited by 8

Interfacial residues in protein–protein complexes are in the eyes of the beholder

(2023) · doi:10.1002/prot.26628 · Cited by 7

To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era

Vangaru, S., & Bhattacharya, D. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf297