Mutation effects predicted from sequence co-variation

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

Matplotlib: a 2D graphics environment

Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249

Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega

Sievers, F., Wilm, A., Dineen, D., Gibson, T., Karplus, K., Li, W.-Z., Lopez, R., McWilliam, H., Remmert, M., Söding, J., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.75 · Cited by 15,576

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697

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

Information theory and statistical mechanics

Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931

Analysis of protein-coding genetic variation in 60,706 humans

Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587

The NumPy Array: A Structure for Efficient Numerical Computation

van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381

Epigenetic Regulations of GABAergic Neurotransmission: Relevance for Neurological Disorders and Epigenetic Therapy

Shrestha, S., & Offer, S. M. (2016) · Medical Epigenetics · doi:10.1159/000444713 · Cited by 8,951

Accelerated Profile HMM Searches

Eddy, S. R. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002195 · Cited by 6,975

DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks

Sun, J., & Frishman, D. (2020) · Journal of Structural Biology · doi:10.1016/j.jsb.2020.107574 · Cited by 15

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

Lin, Y.-J., Menon, A. S., Hu, Z., & Brenner, S. E. (2024) · Human Genomics · doi:10.1186/s40246-024-00663-z · Cited by 17

Molecular evolution of HIV-1 integrase during the 20 years prior to the first approval of integrase inhibitors

Meixenberger, K., Yousef, K. P., Smith, M. R., Somogyi, S., Fiedler, S., Gunsenheimer‐Bartmeyer, B., Hamouda, O., Bannert, N., von Kleist, M., & Kücherer, C. (2017) · Virology Journal · doi:10.1186/s12985-017-0887-1 · Cited by 14

PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations

Kuru, N., Dereli, O., Akkoyun, E., Bırcan, A., Taştan, Ö., & Adebalı, O. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac114 · Cited by 15

Understanding structure-guided variant effect predictions using 3D convolutional neural networks

Ramakrishnan, G., Baakman, C. A. B., Heijl, S., Vroling, B., van Horck, R., Hiraki, J., Xue, L. C., & Huynen, M. A. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1204157 · Cited by 14

Molecular Mechanism of Taurocholate Transport by the Bile Salt Export Pump, an ABC Transporter Associated with Intrahepatic Cholestasis

Sohail, M. I., Schmid, D., Wlcek, K., Spork, M., Szakács, G., Trauner, M. A., Stockner, T., & Chiba, P. (2017) · Molecular Pharmacology · doi:10.1124/mol.117.108688 · Cited by 14

Correlation-compressed direct-coupling analysis

Gao, C.-Y., Zhou, H., & Aurell, E. (2018) · Physical review. E · doi:10.1103/physreve.98.032407 · Cited by 14

Deep Mutagenesis of a Transporter for Uptake of a Non-Native Substrate Identifies Conformationally Dynamic RegionsPreprint

Ellis, H. J., Chan, M. C., Selvam, B., Walter, E., Devlin, C. A., Szymanski, S. K., Henry, L. K., Shukla, D., & Procko, E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.19.440442 · Cited by 16

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

Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area

Tafazoli, A., Wawrusiewicz‐Kurylonek, N., Posmyk, R., & Miltyk, W. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm10010034 · Cited by 13

Unique features of different classes of G‐protein‐coupled receptors revealed from sequence coevolutionary and structural analysis

Do, H. N., Haldane, A., Levy, R. M., & Miao, Y. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26256 · Cited by 14

Optimal regularizations for data generation with probabilistic graphical models

Fanthomme, A., Rizzato, F., Cocco, S., & Monasson, R. (2022) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac650c · Cited by 13

Advances in ligand-specific biosensing for structurally similar molecules

Xi, C., Diao, J., & Moon, T. S. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.009 · Cited by 17

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Swint‐Kruse, L., & Fenton, A. W. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.105736 · Cited by 15

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

Marrero, M. C., Jänes, J., Baptista, D., & Beltrão, P. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120622-020615 · Cited by 13

Machine learning in biological physics: From biomolecular prediction to design

Martin, J., Mateos, M., Onuchic, J. N., Coluzza, I., & Morcos, F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311807121 · Cited by 14

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting

Dereli, O., Kuru, N., Akkoyun, E., Bırcan, A., Taştan, Ö., & Adebalı, O. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae136 · Cited by 17

Design of a Protein with Improved Thermal Stability by an Evolution‐Based Generative Model

Tian, P., Lemaire, A., Sénéchal, F., Habrylo, O., Antonietti, V., Sonnet, P., Lefebvre, V., Marin, F. I., Best, R. B., Pelloux, J., et al. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202202711 · Cited by 13

Deep representation learning improves prediction of LacI-mediated transcriptional repression

Garruss, A. S., Collins, K. M., & Church, G. M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022838118 · Cited by 14

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features

Luppino, F., Adzhubei, I. A., Cassa, C. A., & Toth-Petroczy, A. (2023) · Nature Communications · doi:10.1038/s41467-023-37661-z · Cited by 12

The application of machine learning in clinical microbiology and infectious diseases

Cheng, X., Zhao, L., Ye, C.-S., Xu, K.-C., & Xu, K. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1545646 · Cited by 18

Evolution of the Immunoglobulin Isotypes—Variations of Biophysical Properties among Animal Classes

Pomarici, N. D., Cacciato, R., Kokot, J., Fernández‐Quintero, M. L., & Liedl, K. R. (2023) · Biomolecules · doi:10.3390/biom13050801 · Cited by 11

A systematic analysis of regression models for protein engineering

Richard, M., Kæstel‐Hansen, J., Groth, P. M., Bartels, S., Salomon, J., Tian, P., Hatzakis, N. S., & Boomsma, W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012061 · Cited by 21

Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information

Cocco, S., Posani, L., & Monasson, R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312335121 · Cited by 14

MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning

Li, P., & Liu, Z. (2024) · Advanced Science · doi:10.1002/advs.202402918 · Cited by 11

Advances in Zero‐Shot Prediction‐Guided Enzyme Engineering Using Machine Learning

Liu, C., Wu, J., Chen, Y., Liu, Y., Zheng, Y., Liu, L., & Zhao, J. B. (2024) · ChemCatChem · doi:10.1002/cctc.202401542 · Cited by 15

Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step

Gelfand, N., Orel, V., Cui, W., Damborský, J., Li, C., Prokop, Z., Xie, W. J., & Warshel, A. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c15551 · Cited by 12

Rational Engineering of Self-Sufficient P450s to Boost Catalytic Efficiency of Carbene-Mediated C–S Bond Formation

Wang, B., Zhou, T., Shen, Y. C., Hu, W. W., Zhou, J., Tang, J., Han, R., Xu, G., Schwaneberg, U., Wang, B., et al. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c01569 · Cited by 13

Refinement of evolutionary medicine predictions based on clinical evidence for the manifestations of Mendelian diseases

Šimčíková, D., & Heneberg, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-54976-4 · Cited by 16

Inferring genetic fitness from genomic data

Zeng, H.-L., & Aurell, E. (2020) · Physical review. E · doi:10.1103/physreve.101.052409 · Cited by 16

Bridging non-overlapping reads illuminates high-order epistasis between distal protein sites in a GPCR

Yoo, J. I., Daugherty, P. S., & O’Malley, M. (2020) · Nature Communications · doi:10.1038/s41467-020-14495-7 · Cited by 12

Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins

Neverov, A. D., Fedonin, G. G., Cheremukhin, E. A., Klink, G. V., Popova, A. V., & Bazykin, G. A. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1008711 · Cited by 13

A Platform for Deep Sequence–Activity Mapping and Engineering Antimicrobial Peptides

DeJong, M. P., Ritter, S. C., Fransen, K. A., Tresnak, D. T., Golinski, A. W., & Hackel, B. J. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00314 · Cited by 12

Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network

Xu, Y., ShangGuan, T.-J., Ding, X., & Cheung, N. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-00477-2 · Cited by 12

Accurate identification and mechanistic evaluation of pathogenic missense variants with Rhapsody-2

Banerjee, A., Bogetti, A. T., & Bahar, I. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2418100122 · Cited by 12

Boosting the predictive power of protein representations with a corpus of text annotations

Duan, H., Skreta, M., Cotta, L., Rajaonson, E. M., Dhawan, N., Aspuru‐Guzik, A., & Maddison, C. J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01088-6 · Cited by 11

Accelerating Enzyme Engineering With Artificial Intelligence in Biocatalysis: Challenges and Opportunities

Lu, X., Cao, M., Ma, M., Wu, Y., Qu, M., Du, F., Ji, R., Duan, M., Dong, L., Liu, K., et al. (2025) · Food Bioengineering · doi:10.1002/fbe2.70029 · Cited by 12

Deep2Full: Evaluating strategies for selecting the minimal mutational experiments for optimal computational predictions of deep mutational scan outcomes

Sruthi, C. K., & Prakash, M. K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227621 · Cited by 13

Undersampling and the inference of coevolution in proteins

Kleeorin, Y., Russ, W. P., Rivoire, O., & Ranganathan, R. (2023) · Cell Systems · doi:10.1016/j.cels.2022.12.013 · Cited by 15

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations

Wang, B., Lei, X., Tian, W., Perez-Rathke, A., Tseng, Y.-Y., & Liang, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad206 · Cited by 11

Ligand distances as key predictors of pathogenicity and function in NMDA receptors

Montanucci, L., Brünger, T., Bhattarai, N., Boßelmann, C. M., Kim, S., Allen, J. P., Zhang, J., Klöckner, C., Krey, I., Fariselli, P., et al. (2024) · Human Molecular Genetics · doi:10.1093/hmg/ddae156 · Cited by 13

Deep learning methods for protein representation and function prediction: A comprehensive overview

Wang, M., Nie, Z., He, Y., Vasilakos, A. V., Cheng, Q. S., & Ren, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110977 · Cited by 11

Predictive and therapeutic applications of protein language models

Furui, K., Sakano, K., & Ohue, M. (2025) · Allergology International · doi:10.1016/j.alit.2025.08.004 · Cited by 12

Functional alignment of protein language models via reinforcement learning

Blalock, N., Seshadri, S., Nakamura, K., Babbar, A., Fahlberg, S. A., Kulkarni, A., & Romero, P. A. (2026) · Nature Communications · doi:10.1038/s41467-026-77557-2 · Cited by 19

Description of rare mutations and a novel variant in Brazilian patients with Cystic Fibrosis: a case series from a referral center in the Bahia State

Mota, L. R., Filho, V. M. D. M., Castro, L., Garcia, D. F., Terse-Ramos, R., Toralles, M. B. P., de Lima, R. L. L. F., & Souza, E. L. (2018) · Molecular Biology Reports · doi:10.1007/s11033-018-4361-y · Cited by 11

Field-theoretic density estimation for biological sequence space with applications to 5′ splice site diversity and aneuploidy in cancer

Chen, W.-C., Zhou, J., Sheltzer, J. M., Kinney, J. B., & McCandlish, D. M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025782118 · Cited by 10

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability

Thakur, A., Gizzio, J., & Levy, R. M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c08097 · Cited by 11

Estimating probabilistic context-free grammars for proteins using contact map constraints

Dyrka, W., Pyzik, M., Coste, F., & Talibart, H. (2019) · PeerJ · doi:10.7717/peerj.6559 · Cited by 11

Mitigation of T-cell dependent immunogenicity by reengineering factor VIIa analogue

Jankowski, W., McGill, J. R., Lagassé, H. A. D., Surov, S. S., Bembridge, G. P., Bunce, C., Cloake, E. A., Fogg, M. H., Jankowska, K. I., Khan, A. R., et al. (2019) · Blood Advances · doi:10.1182/bloodadvances.2019000338 · Cited by 11

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data

Sruthi, C. K., Balaram, H., & Prakash, M. K. (2020) · ACS Omega · doi:10.1021/acsomega.0c02402 · Cited by 14

An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks

Kim, H. Y., Jeon, W., & Kim, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-98693-3 · Cited by 12

Computing the Pathogenicity of Wilson’s Disease ATP7B Mutations: Implications for Disease Prevalence

Tang, N., Sandahl, T. D., Ott, P., & Kepp, K. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00852 · Cited by 12

Co‐Evolutionary Fitness Landscapes for Sequence Design

Tian, P., Louis, J. M., Baber, J. L., Aniana, A., & Best, R. B. (2018) · Angewandte Chemie · doi:10.1002/ange.201713220 · Cited by 11

Clustering huge protein sequence sets in linear timePreprint

Steinegger, M., & Söding, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/104034 · Cited by 11

Immunological Diversity with SimilarityPreprint

Arora, R., Burke, H. M., & Arnaout, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/483131 · Cited by 11

PIVOTAL: Prioritizing variants of uncertain significance with spatial genomic patterns in the 3D proteomePreprint

Liang, S., Mort, M. E., Stenson, P. D., Cooper, D. N., & Yu, H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.04.135103 · Cited by 13

Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutationsPreprint

Ursu, O., Neal, J. T., Shea, E. K., Thakore, P. I., Jerby‐Arnon, L., Nguyễn, L., Dionne, D. A., Diaz, C., Bauman, J., Mosaad, M. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.16.383307 · Cited by 11

Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classificationPreprint

Staklinski, S. J., Scheben, A., Siepel, A. C., & Kilberg, M. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.30.564808 · Cited by 11

PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects

Tekpinar, M., David, L., Henry, T., & Carbone, A. (2025) · Genome Biology · doi:10.1186/s13059-025-03581-y · Cited by 18

Deep-Learning-Guided Mining and Clustering of Remote Amino Acid Residues for the Simultaneous Engineering of the Catalytic Activity and Thermostability of a Processive Endoglucanase

Wu, M., Huang, Y., He, X., Chen, K., Wu, B., & Schenk, G. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00454 · Cited by 10

Fitness Landscape Analysis of a tRNA Gene Reveals that the Wild Type Allele is Sub-optimal, Yet Mutationally Robust

Gabzi, T., Pilpel, Y., & Friedlander, T. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac178 · Cited by 13

Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction

Nisonoff, H. M., Wang, Z., & Listgarten, J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00217 · Cited by 10

Optimisation strategies for directed evolution without sequencing

James, J., Towers, S., Foerster, J., & Steel, H. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012695 · Cited by 11

Data-Driven Extraction of Human Kinase-Substrate Relationships From Omics Datasets

Maier, B. D., Pétursson, B., Lussana, A., & Petsalaki, E. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100994 · Cited by 11

Efficient epistasis inference via higher-order covariance matrix factorization

Shimagaki, K. S., & Barton, J. P. (2025) · Genetics · doi:10.1093/genetics/iyaf118 · Cited by 10

A Cyanobacterial Screening Platform for Rubisco Mutant Variants

Hoffmann, U. A., Schuppe, A. Z., Knave, A., Sporre, E., Brismar, H., Englund, E., Syrén, P.-O., & Hudson, E. P. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00065 · Cited by 10

Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure

Corral-Corral, R., Beltrán, J., Brizuela, C. A., & del Rio, G. (2017) · Molecules · doi:10.3390/molecules22101673 · Cited by 10

Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?

Delarue, M. H., & Koehl, P. (2018) · F1000Research · doi:10.12688/f1000research.14870.1 · Cited by 10

Effects of Single Mutations on Protein Stability Are Gaussian Distributed

Razban, R. M., & Shakhnovich, E. I. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.04.027 · Cited by 10

Why Proteins are Big: Length Scale Effects on Equilibria and Kinetics

Rubinson, K. A. (2019) · The Protein Journal · doi:10.1007/s10930-019-09822-x · Cited by 10

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

Feng, J., & Shukla, D. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b11869 · Cited by 9

Limits to detecting epistasis in the fitness landscape of HIV

Biswas, A., Haldane, A., & Levy, R. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0262314 · Cited by 10

Evidence-based tailoring of bioinformatics approaches to optimize methods that predict the effects of nonsynonymous amino acid substitutions in glucokinase

Šimčíková, D., Kocková, L., Vackářová, K., Těšínský, M., & Heneberg, P. (2017) · Scientific Reports · doi:10.1038/s41598-017-09810-0 · Cited by 10

Complementary computational and experimental evaluation of missense variants in the ROMK potassium channel

Ponzoni, L., Nguyen, N. H., Bahar, İ., & Brodsky, J. L. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007749 · Cited by 10

Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language ModelPreprint

He, L., Zhang, S., Wu, L., Xia, H., Ju, F., Zhang, H., Liu, S., Xia, Y., Zhu, J., Deng, P., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15527 · Cited by 35

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineeringPreprint

Illig, A.-M., Siedhoff, N. E., Schwaneberg, U., & Davari, M. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.07.495081 · Cited by 10

Accurate Mutation Effect Prediction using RoseTTAFoldPreprint

Mansoor, S., Baek, M., Juergens, D., Watson, J. L., & Baker, D. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.04.515218 · Cited by 10

Deep learning-enabled design of synthetic orthologs of a signaling proteinPreprint

Lian, X., Praljak, N., Subramanian, S. K., Wasinger, S., Ranganathan, R., & Ferguson, A. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.21.521443 · Cited by 10

Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability

Tan, Y., Zhou, B., Zheng, L., Fan, G., & Hong, L. (2025) · eLife · doi:10.7554/elife.98033.4 · Cited by 20

Statistical genetics in and out of quasi-linkage equilibrium

Dichio, V., Zeng, H.-L., & Aurell, E. (2023) · Reports on Progress in Physics · doi:10.1088/1361-6633/acc5fa · Cited by 13

Engineering TadA ortholog-derived cytosine base editor without motif preference and adenosine activity limitation

Li, G., Dong, X., Luo, J., Yuan, T., Li, T., Zhao, G., Zhang, H., Zhou, J., Zeng, Z., Cui, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52485-1 · Cited by 9

Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep

Tzavella, K., Díaz, A., Olsen, C., & Vranken, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae664 · Cited by 9

Effective Gene Expression Prediction and Optimization from Protein Sequences

Liu, T., Zhang, Y., Li, Y., Xu, G., Gao, H., Wang, P., Tu, T., Luo, H., Wu, N., Yao, B., et al. (2025) · Advanced Science · doi:10.1002/advs.202407664 · Cited by 9

Prediction of Adeno-Associated Virus Fitness with a Protein Language-Based Machine Learning Model

Wu, J., Qiu, Y., Lyashenko, E., Torregrosa, T., Pfister, E. L., Ryan, M. J., Mueller, C., & Choudhury, S. (2025) · Human Gene Therapy · doi:10.1089/hum.2024.227 · Cited by 10

A synthetic cell-free pathway for biocatalytic upgrading of formate from electrochemically reduced CO2

Landwehr, G. M., Vögeli, B., Tian, C., Singal, B., Zolkin, K., Martinez, I., Gupta, A., Lion, R., Sargent, E. H., Karim, A. S., et al. (2025) · Nature Chemical Engineering · doi:10.1038/s44286-025-00315-6 · Cited by 10

Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization

Chan, M. C., Chan, K. K., Procko, E., & Shukla, D. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c00469 · Cited by 9

Designing bacterial signaling interactions with coevolutionary landscapes

Cheng, R. R., Haglund, E., Tiee, N. S., Morcos, F., Levine, H., Adams, J. A., Jennings, P. A., & Onuchic, J. N. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201734 · Cited by 10

Global Dynamics Renders Protein Sites with High Functional Response

Kutlu, Y., Ben‐Tal, N., & Haliloǧlu, T. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c02511 · Cited by 9

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(2017) · Nature Methods · doi:10.1038/nmeth.4205

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