Residue conservation and solvent accessibility are (almost) all you need for predicting mutational effects in proteins

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

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

Twilight zone of protein sequence alignments

Rost, B. (1999) · Protein Engineering, Design and Selection · doi:10.1093/protein/12.2.85 · Cited by 1,833 · see citing works

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434 · see citing works

Identification of direct residue contacts in protein–protein interaction by message passing

Weigt, M., White, R. A., Szurmant, H., Hoch, J. A., & Hwa, T. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805923106 · Cited by 1,025 · 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

An evolution-based model for designing chorismate mutase enzymes

Russ, W. P., Figliuzzi, M., Stocker, C., Barrat-Charlaix, P., Socolich, M., Kast, P., Hilvert, D., Monasson, R., Cocco, S., Weigt, M., et al. (2020) · Science · doi:10.1126/science.aba3304 · Cited by 313 · see citing works

SaProt: Protein Language Modeling with Structure-aware VocabularyPreprint

Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., & Yuan, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.01.560349 · Cited by 251 · see citing works

Untitled work

(2023) · doi:10.1101/2023.12.07.570727 · Cited by 207 · see citing works

GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects

Laine, É., Karami, Y., & Carbone, A. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz179 · Cited by 197 · see citing works

Quantification of biases in predictions of protein stability changes upon mutations

Pucci, F., Bernaerts, K. V., Kwasigroch, J. M., & Rooman, M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty348 · Cited by 169 · see citing works

Updated benchmarking of variant effect predictors using deep mutational scanning

Livesey, B., & Marsh, J. A. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211474 · Cited by 109 · see citing works

An Atlas of Variant Effects to understand the genome at nucleotide resolution

Fowler, D. M., Adams, D. J., Gloyn, A. L., Hahn, W. C., Marks, D. S., Muffley, L. A., Neal, J. T., Roth, F. P., Rubin, A. F., Starita, L. M., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02986-x · Cited by 109 · see citing works

Machine learning to navigate fitness landscapes for protein engineering

Freschlin, C. R., Fahlberg, S. A., & Romero, P. A. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102713 · Cited by 105 · see citing works

Prediction and interpretation of deleterious coding variants in terms of protein structural stability

Ancien, F., Pucci, F., Godfroid, M., & Rooman, M. J. (2018) · Scientific Reports · doi:10.1038/s41598-018-22531-2 · Cited by 93 · see citing works

Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation

Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., Lonishin, L. R., Meister, A. V., Yakupova, A. G., Kondrashov, F. A., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty340 · Cited by 93 · see citing works

Artificial intelligence challenges for predicting the impact of mutations on protein stability

Pucci, F., Schwersensky, M., & Rooman, M. J. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.001 · Cited by 86 · see citing works

TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness PredictionPreprint

Notin, P., van Niekerk, L., Kollasch, A. W., Ritter, D., Gal, Y., & Marks, D. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.07.519495 · Cited by 57 · see citing works

ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention

Li, M., Tan, Y., Ma, X., Zhong, B., Yu, H., Zhou, Z., Ouyang, W., Zhong, B., Hong, L., & Tan, Y. (2024) · neural information processing systems · doi:10.52202/079017-1126 · Cited by 49 · see citing works

Critical assessment of missense variant effect predictors on disease-relevant variant data

Rastogi, R., Chung, R., Li, S., Li, C., Lee, K., Woo, J., Kim, D., Keum, C., Babbi, G., Martelli, P. L., et al. (2025) · Human Genetics · doi:10.1007/s00439-025-02732-2 · Cited by 36 · see citing works

Expert-guided protein language models enable accurate and blazingly fast fitness prediction

Marquet, C., Schlensok, J., Abakarova, M., Rost, B., & Laine, E. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae621 · Cited by 33 · see citing works

Quantification of biases in predictions of protein–protein binding affinity changes upon mutations

Tsishyn, M., Pucci, F., & Rooman, M. J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad491 · Cited by 29 · see citing works

PoET: A generative model of protein families as sequences-of-sequencesPreprint

Truong, T. F., & Bepler, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.06156 · Cited by 28 · see citing works

Prediction of phenotypes of missense mutations in human proteins from biological assemblies

Wei, Q., Xu, Q., & Dunbrack, R. L. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24176 · Cited by 23 · see citing works

Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis

Yang, F., Li, C., Yang, W., He, Y., Wu, L., Jiang, K., & Sun, C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae491 · Cited by 13 · see citing works

Exploring Evolution to Uncover Insights Into Protein Mutational Stability

Hermans, P., Tsishyn, M., Schwersensky, M., Rooman, M., & Pucci, F. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msae267 · Cited by 6 · see citing works

Protein stability is determined by single-site bias rather than pairwise covariance

Sternke, M., Tripp, K. W., Behera, S. P., Nguyen, J. P., & Barrick, D. (2026) · Nature Chemical Biology · doi:10.1038/s41589-026-02270-6 · Cited by 2 · see citing works

Understanding Language Model Scaling on Protein Fitness PredictionPreprint

Hou, C., Liu, D., Zafar, A., & Shen, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.25.650688 · Cited by 14 · see citing works

Generalist biological artificial intelligence in modeling the language of life

Rao, V. M., Zhang, S., Plosky, B. S., Hsu, P. D., Wang, B., Zou, J., Žitnik, M., Topol, E. J., & Rajpurkar, P. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03064-w · Cited by 12 · see citing works

Compressing the collective knowledge of ESM into a single protein language model

Dinh, T., Jang, S.-K., Zaitlen, N., & Ntranos, V. (2026) · Nature Methods · doi:10.1038/s41592-026-03050-9 · Cited by 7 · see citing works

Dissecting the effects of single amino acid substitutions in SARS ‐ CoV‐2 Mpro

Sreenivasan, S., Fontes, J. D., & Swint‐Kruse, L. (2025) · Protein Science · doi:10.1002/pro.70225 · 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

Genome-wide analysis discovers PoGPAT-10 driving oil accumulation in tree peony seeds and defines its essential residues

Wei, S., Liu, Y., Wang, H., Chen, L., & Hou, X. (2025) · Industrial Crops and Products · doi:10.1016/j.indcrop.2025.122051 · Cited by 2 · see citing works

Amino acid exchangeability and surface accessibility underpin the effects of single substitutions

Alpay, B. A., Nanda, P., Nagy, E., & Desai, M. M. (2026) · Genetics · doi:10.1093/genetics/iyag104 · Cited by 1 · see citing works

Strategies to Screen and Evaluate Brain Targeting Antibodies Using an iPSC-Derived Blood–Brain Barrier Model

Choi, E. S., Sahota, S., Burnham, E., Ding, Y., & Shusta, E. V. (2025) · Antibodies · doi:10.3390/antib14040102 · Cited by 1 · see citing works

ProteoCast: a web server to predict, validate, and interpret missense variant effects

(2026) · doi:10.1016/j.jmb.2026.169657 · Cited by 1 · see citing works

Structural determinants of walnut glutelin solubility for enhancing food industry applications

Li, Y., Hu, J., Zang, J., & Tang, N. (2026) · Food Research International · doi:10.1016/j.foodres.2026.118863 · Cited by 1 · see citing works

Modality-chain reasoning enables multimodal protein modelling and designPreprint

Liu, C., Chao, L., Ji, S., Wang, H., Zhou, G., Zheng, J. X., Hong, D., Guo, Y., Jiang, T., Gao, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.21.665832 · Cited by 1 · see citing works

Complete Mutagenesis of the Genome and Proteome of ΦX174Preprint

Wei, H., Li, X., & Lehner, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.25.740675 · Cited by 1 · see citing works

HAGGNN : Hydration‐Aware Geometric Graph Neural Network for Protein Thermal Stability Prediction

Jiang, Y., Liu, L., & Ding, Y. (2026) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70133

Protein fitness prediction with language models

Wang, T. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01011-y

Dual-graph deconvolution of enzyme fitness: an interpretable framework to identify and explain catalytic modulatorsPreprint

Busto, S. G., Eschbach, E., Hopf, T. A., Sander, C., Bacallado, S., & Marks, D. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.15.759402

Integrating structural and evolutionary information to infer the mutational landscape of proteins

Tsishyn, M. (2026) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles)

Prediction of solvent accessibility and sites of deleterious mutation from protein sequences

Chen, H., & Zhou, H. (2005)

Evolutionary Constraints Improve Protein Large Language Model Predictions for Protein Stability, Binding Regions, and Epistasis

Tzavella, K., Olsen, C., & Vranken, W. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0226

Predicting protein stability changes upon mutation using a simple orientational potential

Hernández, I. M., Dehouck, Y., Bastolla, U., López‐Blanco, J. R., & Chacón, P. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad011 · Cited by 29 · see citing works

PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations

Iqbal, S., Ge, F., Li, F., Akutsu, T., Zheng, Y., Gasser, R. B., Yu, D., Webb, G. I., & Song, J. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00799 · Cited by 40 · see citing works