PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context
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Abstract
Abstract Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode’s prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal
Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., et al. (2013) · Science Signaling · doi:10.1126/scisignal.2004088 · Cited by 15,160 · see citing works
A method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194 · see citing works
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 · see citing works
SIFT: predicting amino acid changes that affect protein function
Ng, P. C. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg509 · Cited by 6,601 · see citing works
UniProt: the universal protein knowledgebase in 2021
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372 · see citing works
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
COSMIC: the Catalogue Of Somatic Mutations In Cancer
Tate, J. G., Bamford, S., Jubb, H. C., Sondka, Z., Beare, D. M., Bindal, N., Boutselakis, H., Cole, C. G., Creatore, C., Dawson, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1015 · Cited by 4,867 · see citing works
ClinVar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196 · see citing works
ClinVar: public archive of relationships among sequence variation and human phenotype
Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1113 · Cited by 3,516 · see citing works
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V. M., & Skrzypek, E. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1267 · Cited by 3,243 · see citing works
REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
Ioannidis, N. M., Rothstein, J. H., Pejaver, V., Middha, S., McDonnell, S. K., Baheti, S., Musolf, A., Li, Q., Holzinger, E., Karyadi, D., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.08.016 · Cited by 2,908 · see citing works
Mechanism of Activation of the RAF-ERK Signaling Pathway by Oncogenic Mutations of B-RAF
Wan, P., Garnett, M. J., Roe, S. M., Lee, S., Niculescu‐Duvaz, D., Good, V. M., Cancer Genome Project, Jones, C. M., Marshall, C. J., Springer, C. J., et al. (2004) · Cell · doi:10.1016/s0092-8674(04)00215-6 · Cited by 2,800 · see citing works
The FoldX web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki387 · Cited by 2,759 · see citing works
Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321 · see citing works
Accurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · see citing works
Stenson, P. D., Mort, M. E., Ball, E. V., Evans, K., Hayden, M., Heywood, S., Hussain, M., Phillips, A. D., & Cooper, D. N. (2017) · Human Genetics · doi:10.1007/s00439-017-1779-6 · Cited by 1,348 · see citing works
A Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169 · see citing works
Exploring protein fitness landscapes by directed evolution
Romero, P. A., & Arnold, F. H. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2805 · Cited by 1,163 · see citing works
Machine-learning-guided directed evolution for protein engineering
Yang, K., Wu, Z., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0496-6 · Cited by 1,082 · see citing works
A series of PDB related databases for everyday needs
Joosten, R., Beek, T. T. T., Krieger, E., Hekkelman, M., Hooft, R., Schneider, R., Sander, C., & Vriend, G. (2010) · Nucleic Acids Res. · doi:10.1093/nar/gkq1105 · Cited by 1,050 · see citing works
ProteinBERT: a universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac020 · Cited by 1,009 · see citing works
Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands
Jin, S. C., Homsy, J., Zaidi, S., Lu, Q., Morton, S., DePalma, S. R., Zeng, X., Qi, H., Chang, W., Sierant, M. C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3970 · Cited by 875 · see citing works
Self-citationPathogenic Germline Variants in 10,389 Adult Cancers
Huang, K., Mashl, R. J., Wu, Y., Ritter, D., Wang, J., Oh, C., Paczkowska, M., Reynolds, S. M., Wyczalkowski, M. A., Oak, N., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.039 · Cited by 838 · see citing works
De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies
Homsy, J. G., Zaidi, S., Shen, Y., Ware, J. S., Samocha, K. E., Karczewski, K. J., DePalma, S. R., McKean, D. M., Wakimoto, H., Gorham, J. M., et al. (2015) · Science · doi:10.1126/science.aac9396 · Cited by 838 · see citing works
Self-citationJagadeesh, K. A., Wenger, A. M., Berger, M. J., Guturu, H., Stenson, P. D., Cooper, D. N., Bernstein, J. A., & Bejerano, G. (2016) · Nature Genetics · doi:10.1038/ng.3703 · Cited by 831 · see citing works
Chang, M. T., Asthana, S., Gao, S. P., Lee, B. H., Chapman, J. S., Kandoth, C., Gao, J., Socci, N. D., Solit, D. B., Olshen, A. B., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3391 · Cited by 808 · see citing works
Disease variant prediction with deep generative models of evolutionary data
Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791 · see citing works
Harrison, P. W., Amode, M. R., Austine-Orimoloye, O., Azov, A. G., Barba, M., Barnes, I., Becker, A., Bennett, R., Berry, A., Bhai, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1049 · Cited by 742 · see citing works
Evaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719 · see citing works
Directed Evolution: Methodologies and Applications
Wang, Y., Xue, P., Cao, M., Yu, T., Lane, S., & Zhao, H. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00260 · Cited by 719 · see citing works
Liu, X., Li, C., Mou, C., Dong, Y., & Tu, Y.-C. (2020) · Genome Medicine · doi:10.1186/s13073-020-00803-9 · Cited by 652 · see citing works
AlphaFold and Implications for Intrinsically Disordered Proteins
Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · see citing works
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.07.09.450648 · Cited by 635 · see citing works
Valdar, W. S. J. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10146 · Cited by 633 · see citing works
Further studies on the nature and causes of gene mutations.
Muller, H. J. (1932) · Medical Entomology and Zoology · Cited by 536 · see citing works
Predicting the clinical impact of human mutation with deep neural networks
Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J. C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0167-z · Cited by 482 · see citing works
Genome-wide prediction of disease variant effects with a deep protein language model
Brandes, N., Goldman, G., Wang, C. H., Ye, C., & Ntranos, V. (2023) · Nature Genetics · doi:10.1038/s41588-023-01465-0 · Cited by 459 · see citing works
Zhou, X., Feliciano, P., Shu, C., Wang, T., Astrovskaya, I., Hall, J. B., Obiajulu, J. U., Wright, J. R., Murali, S. C., Xu, S. X., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01148-2 · Cited by 453 · see citing works
Multiplex assessment of protein variant abundance by massively parallel sequencing
Matreyek, K. A., Starita, L. M., Stephany, J. J., Martin, B., Chiasson, M. A., Gray, V. E., Kircher, M., Khechaduri, A., Dines, J. N., Hause, R. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0122-z · Cited by 436 · see citing works
ColabFold - Making protein folding accessible to allPreprint
Ovchinnikov, S., Steinegger, M., Mirdita, M., konsti, Moriwaki, Y., Kim, R. S., Wang, P., Kosinski, J., Tadej Satler, P., Cha, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.5281/zenodo.5123296 · Cited by 425 · see citing works
Using SIFT and PolyPhen to Predict Loss-of-Function and Gain-of-Function Mutations
Flanagan, S. E., Patch, A., & Ellard, S. (2010) · Genetic Testing and Molecular Biomarkers · doi:10.1089/gtmb.2010.0036 · Cited by 417 · see citing works
Accelerating Discovery of Functional Mutant Alleles in Cancer
Chang, M. T., Shrestha-Bhattarai, T., Schram, A. M., Bielski, C. M., Donoghue, M. T. A., Jonsson, P., Chakravarty, D., Phillips, S. M., Kandoth, C., Penson, A. V., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0321 · Cited by 402 · see citing works
Regional missense constraint improves variant deleteriousness prediction
Samocha, K. E., Kosmicki, J. A., Karczewski, K. J., O’Donnell-Luria, A. H., Pierce-Hoffman, E., MacArthur, D. G., Neale, B. M., & Daly, M. J. (2017) · doi:10.1101/148353 · Cited by 351 · see citing works
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350 · see citing works
Gain-of-function mutant p53 in cancer progression and therapy
Zhang, C., Liu, J., Xu, D., Zhang, T., Hu, W., & Feng, Z. (2020) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjaa040 · Cited by 339 · see citing works
Progress in Understanding and Treating SCN2A-Mediated Disorders
Sanders, S. J., Campbell, A. J., Cottrell, J. R., Moller, R. S., Wagner, F. F., Auldridge, A. L., Bernier, R. A., Catterall, W. A., Chung, W. K., Empfield, J. R., et al. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.03.011 · Cited by 338 · cited 3× · see citing works
“Furthermore, gain/loss of funcQon could be further divided into amorph, hypomorph, hypermorph, anQmorph and neomorph according to muller’s morphs 79 .”
Self-citationLoss of Tumor Suppressor Gene Function in Human Cancer: An Overview
Wang, L., Wu, C., Rajasekaran, N., & Shin, Y. K. (2018) · Cellular Physiology and Biochemistry · doi:10.1159/000495956 · Cited by 334 · see citing works
Systematic Functional Annotation of Somatic Mutations in Cancer
Ng, P. K., Li, J., Jeong, K. J., Shao, S. H., Chen, H., Tsang, Y. H., Sengupta, S., Wang, Z., Bhavana, V. H., Tran, R., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.01.021 · Cited by 328 · see citing works
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular PotentialsPreprint
Thölke, P., & de Fabritiis, G. (2022) · arXiv.org · doi:10.48550/arxiv.2202.02541 · Cited by 314 · see citing works
Gerasimavicius, L., Livesey, B., & Marsh, J. A. (2022) · Nature Communications · doi:10.1038/s41467-022-31686-6 · Cited by 309 · see citing works
PERCH: A Unified Framework for Disease Gene Prioritization: HUMAN MUTATION
Feng, B.-J. (2017) · Human Mutation · doi:10.1002/humu.23158 · Cited by 289 · see citing works
Cancer-Associated PTEN Mutants Act in a Dominant-Negative Manner to Suppress PTEN Protein Function
Papa, A., Wan, L., Bonora, M., Salmena, L., Song, M. S., Hobbs, R. M., Lunardi, A., Webster, K. A., Ng, C., Newton, R. H., et al. (2014) · Cell · doi:10.1016/j.cell.2014.03.027 · Cited by 277 · see citing works
Esposito, D., Weile, J., Shendure, J., Starita, L. M., Papenfuss, A. T., Roth, F. P., Fowler, D. M., & Rubin, A. F. (2019) · Genome biology · doi:10.1186/s13059-019-1845-6 · Cited by 248 · see citing works
Tran, T. H., Chan, A. H., Young, L. C., Bindu, L. K., Neale, C., Messing, S. A. J., Dharmaiah, S., Taylor, T. E., Denson, J.-P., Esposito, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21422-x · Cited by 245 · see citing works
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D., Cooper, G. M., & Shendure, J. (2014) · F1000Research · doi:10.7490/f1000research.1095265.1 · Cited by 234 · see citing works
Learning protein fitness models from evolutionary and assay-labeled data
Hsu, C., Nisonoff, H. M., Fannjiang, C., & Listgarten, J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01146-5 · Cited by 222 · see citing works
(2023) · doi:10.1101/2023.12.07.570727 · Cited by 207 · see citing works
Mighell, T. L., Evans-Dutson, S., & O’Roak, B. J. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.03.018 · Cited by 204 · see citing works
Whole exome sequencing reveals the major genetic contributors to nonsyndromic tetralogy of Fallot
Page, D. J., Miossec, M. J., Williams, S. G., Monaghan, R. M., Fotiou, E., Cordell, H. J., Sutcliffe, L., Topf, A., Bourgey, M., Bourque, G., et al. (2019) · Circulation Research · doi:10.1161/circresaha.118.313250 · Cited by 189 · see citing works
Differential regulation of p53 and p21 by MKRN1 E3 ligase controls cell cycle arrest and apoptosis
Lee, E., Lee, M.-S., Camus, S., Ghim, J., Yang, M. R., Oh, W., Ha, N., Lane, D. P., & Song, J. (2009) · The EMBO Journal · doi:10.1038/emboj.2009.164 · Cited by 158 · see citing works
Blanchard, M. G., Willemsen, M. H., Walker, J. B., Dib‐Hajj, S. D., Waxman, S. G., Jongmans, M. C. J., Kleefstra, T., van de Warrenburg, B. P. C., Praamstra, P., Nicolai, J., et al. (2015) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2014-102813 · Cited by 145 · see citing works
Predicting functional effects of missense variants in voltage-gated sodium and calcium channels
Heyne, H. O., Baez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., May, P., Collaborative, E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6848 · Cited by 140 · see citing works
Structural insights into the BRAF monomer-to-dimer transition mediated by RAS binding
Fiesco, J. A. M., Durrant, D., Morrison, D. K., & Zhang, P. (2022) · Nature Communications · doi:10.1038/s41467-022-28084-3 · Cited by 130 · see citing works
Protein–protein interactions and genetic diseases: The interactome
Lage, K. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.05.028 · Cited by 124 · see citing works
Suiter, C. C., Moriyama, T., Matreyek, K. A., Yang, W., Scaletti, E. R., Nishii, R., Yang, W., Hoshitsuki, K., Singh, M., Trehan, A., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915680117 · Cited by 121 · see citing works
Iqbal, S., Pérez‐Palma, E., Jespersen, J. B., May, P., Hoksza, D., Heyne, H., Ahmed, S. S., Rifat, Z. T., Rahman, M. S., Lage, K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002660117 · Cited by 120 · see citing works
Wolff, M., Brunklaus, A., & Zuberi, S. M. (2019) · Epilepsia · doi:10.1111/epi.14935 · Cited by 105 · see citing works
Predicting functional effect of missense variants using graph attention neural networks
Zhang, H., Xu, M. S., Fan, X., Chung, W. K., & Shen, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00561-w · Cited by 98 · see citing works
Self-citationKakudo, Y., Shibata, H., Otsuka, K., Kato, S., & Ishioka, C. (2005) · Cancer Research · doi:10.1158/0008-5472.can-04-2935 · Cited by 88 · see citing works
Receptor tyrosine kinase mutations in developmental syndromes and cancer: two sides of the same coin
McDonell, L. M., Kernohan, K. D., Boycott, K. M., & Sawyer, S. L. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv254 · Cited by 86 · see citing works
The mechanism of activation of monomeric B-Raf V600E
Maloney, R., Zhang, M., Jang, H., & Nussinov, R. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.007 · Cited by 83 · see citing works
Molecular structure of an open human KATP channel
Zhao, C., & MacKinnon, R. (2021) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2112267118 · Cited by 72 · see citing works
Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology
Li, Y., Zhang, Y., Li, X., Yi, S. S., & Xu, J. (2019) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.03.009 · Cited by 64 · see citing works
Swan, H., Amarouch, M., Leinonen, J. T., Marjamaa, A., Kučera, J., Laitinen-Forsblom, P. J., Lahtinen, A. M., Palotie, A. V., Kontula, K., Toivonen, L. K., et al. (2014) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000703 · Cited by 61 · see citing works
Kagawa, R., Fujiki, R., Tsumura, M., Sakata, S., Nishimura, S., Itan, Y., Kong, X., Kato, Z., Ohnishi, H., Hirata, O., et al. (2016) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2016.09.035 · Cited by 58 · see citing works
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Xu, M., Zhang, Z., Lu, J., Zhu, Z., Zhang, Y., Zhang, Y., Liu, R., & Tang, J. (2022) · neural information processing systems · doi:10.52202/068431-2548 · Cited by 58 · see citing works
Bayrak, Ç. S., Stein, D. F., Jain, A., Chaudhary, K., Nadkarni, G. N., Vleck, T. T. V., Puel, A., Boisson‐Dupuis, S., Okada, S., Stenson, P. D., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.10.007 · Cited by 55 · see citing works
Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53 · see citing works
Utility of the JAX Clinical Knowledgebase in capture and assessment of complex genomic cancer data
Patterson, S. E., Statz, C. M., Yin, T., & Mockus, S. M. (2018) · npj Precision Oncology · doi:10.1038/s41698-018-0073-y · Cited by 49 · see citing works
Matreyek, K. A., Stephany, J. J., Ahler, E., & Fowler, D. M. (2021) · Genome Medicine · doi:10.1186/s13073-021-00984-x · Cited by 48 · see citing works
Is Transfer Learning Necessary for Protein Landscape Prediction?Preprint
Shanehsazzadeh, A., Belanger, D., & Dohan, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03443 · Cited by 43 · see citing works
Lu, H., Villafañe, N., Dogruluk, T., Grzeskowiak, C. L., Kong, K., Tsang, Y. H., Zagorodna, O., Pantazi, A., Yang, L., Neill, N. J., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-2745 · Cited by 40 · see citing works
Gain of Function Mutants: Ion Channels and G Protein-Coupled Receptors
Lester, H. A., & Karschin, A. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.89 · Cited by 38 · see citing works
Kiel, C., Benisty, H., Lloréns‐Rico, V., & Serrano, L. G. (2016) · eLife · doi:10.7554/elife.12814 · Cited by 32 · see citing works
The Impact of Protein Dynamics on Residue-Residue Coevolution and Contact PredictionPreprint
Fung, A., Koehl, A., Jagota, M., & Song, Y. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.16.512436 · Cited by 4 · see citing works
Zhong, G., & Shen, Y. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15825902 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
4 of the 95 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Protein language models trained on biophysical dynamics inform mutation effects
Hou, C., Zhao, H., & Shen, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2530466123 · Cited by 22 · see citing works
Self-citationGlaser, M., & Brägelmann, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.03.635741 · Cited by 8 · see citing works
Xu, R., Li, X., Sui, J., Wu, L., Ling, C., Zheng, L., & Guo, J. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00755 · Cited by 4 · see citing works
Understanding language model scaling for protein fitness prediction
Hou, C., Лю, Д., Zafar, A., & Shen, Y. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01010-z · Cited by 18 · see citing works
Self-citationZafar, A., Hou, C., Amirani, N., & Shen, Y. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100618 · Cited by 3 · see citing works
Self-citationZhu, Y. L., Wang, S., Han, Y. M., Lu, Y., Xiong, A., Qiu, S., Jin, L. N., & Zhang, W. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c12915 · Cited by 2 · see citing works
Language Modelling Techniques for Analysing the Impact of Human Genetic Variation
Hegde, M., Nebel, J., & Rahman, F. (2025) · Bioinformatics and Biology Insights · doi:10.1177/11779322251358314 · Cited by 5 · see citing works
Lee, D.-B., Kang, H.-U., & Hwang, K. (2026) · BioData Mining · doi:10.1186/s13040-026-00533-5 · Cited by 1 · see citing works
Yao, W., & li, H. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.03.003 · Cited by 1 · see citing works
Actually, what is a gain-of-function mutation?
Warnecke, T. (2026) · Genetics · doi:10.1093/genetics/iyag059 · Cited by 1 · see citing works
Defining the molecular tolerance-to-damage landscape of SMARCA4 helicase genetic alterationsPreprint
Haque, N., Dong, X., Wagenknecht, J. B., & Zimmermann, M. T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.22.733755 · Cited by 1 · see citing works
Hou, C., Liu, D., & Shen, Y.-F. (2025) · bioRxiv · doi:10.1101/2025.11.04.686576 · Cited by 2 · see citing works
Self-citationLiu, X., Zhang, R., Zhang, H., Xin, J., Li, H., Pan, Y., Zhu, L., Kang, M., Jiang, H., Fang, Y., et al. (2026) · Cancer Cell International · doi:10.1186/s12935-025-04128-8
Zhang, H., Zheng, G., Xu, Z., Zhao, H., Cai, S., Huang, Y., Zhou, Z., & Wei, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.13.711612
The GPCRVP score reliably predicts the impact of GLP1R human variants on receptor function
Lopez‐Balastegui, M., Lian, S., Gao, W., Gbahou, F., Stępniewski, T. M., Dam, J., Jockers, R., & Selent, J. (2026) · Diabetologia · doi:10.1007/s00125-026-06859-3
- Authors
- Zhong, G., Zhao, Y., Zhuang, D., Chung, W. K., & Shen, Y.
- Affiliations
- Columbia University Irving Medical Center (US)
- Boston Children's Hospital (US)
- Corresponding author
- Shen, Yufeng
- Venue
- Nature Communications
- Published
- 2025-08-05
- Volume
- 16
- Issue
- 1
- Pages
- 7189-7189
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12325985
- OpenAlex
- W4412958401
- Cited by
- 15
- References
- 95
- Citations by year
- 2026: 10 · 2025: 5
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM149527)
- U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD109879)
- Simons Foundation (SFARI #1019623)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- missense mutations, gain-of-function mutations, loss-of-function mutations, transfer learning, active learning (machine learning)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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