An end-to-end framework for the prediction of protein structure and fitness from single sequence

Highly accurate protein structure prediction with AlphaFold

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The Protein Data Bank

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Accurate structure prediction of biomolecular interactions with AlphaFold 3

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ColabFold - Making protein folding accessible to all

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

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Principles that Govern the Folding of Protein Chains

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Accurate prediction of protein structures and interactions using a 3-track network

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Evolutionary-scale prediction of atomic level protein structure with a language model

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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · cited 8× · see citing works

Highly accurate protein structure prediction for the human proteome

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The FoldX web server: an online force field

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Fast and accurate protein structure search with Foldseek

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Fast and accurate protein structure search with Foldseek

van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works

Scoring function for automated assessment of protein structure template quality

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I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure

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UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

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

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Improved protein structure prediction using predicted interresidue orientations

Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works

Deep mutational scanning: a new style of protein science

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The diversity of splicing modifiers acting on A-1 bulged 5′-splice sites reveals rules for rational drug design

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Prediction of protein stability changes for single‐site mutations using support vector machines

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Exploring protein fitness landscapes by directed evolution

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

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DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

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mCSM: Predicting the effects of mutations in proteins using graph-based signatures

Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt691 · Cited by 1,066 · see citing works

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

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MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity

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Proteins: Structure, Function, and Bioinformatics

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DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

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DynaMut2 : Assessing changes in stability and flexibility upon single and multiple point missense mutations

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A structural biology community assessment of AlphaFold2 applications

Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · cited 1× · see citing works

Protein structure prediction from sequence variation

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Emerging methods in protein co-evolution

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AlphaFold2 and its applications in the fields of biology and medicine

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Deep generative models of genetic variation capture the effects of mutations

Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601 · see citing works

SDM--a server for predicting effects of mutations on protein stability and malfunction

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CATH: increased structural coverage of functional space

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Scaffolding protein functional sites using deep learning

Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., et al. (2022) · Science · doi:10.1126/science.abn2100 · Cited by 487 · see citing works

Single-sequence protein structure prediction using a language model and deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works

Single-sequence protein structure prediction using language models from deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439 · see citing works

MSA Transformer

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

Mega-scale experimental analysis of protein folding stability in biology and design

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MAESTRO - multi agent stability prediction upon point mutations

Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S., & Lackner, P. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0548-6 · Cited by 310 · see citing works

Parsimony, Exhaustivity and Balanced Detection in Neocortex

Romagnoni, A., Ribot, J., Bennequin, D., & Touboul, J. D. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004623 · Cited by 296 · see citing works

DDMut: predicting effects of mutations on protein stability using deep learning

Zhou, Y., Pan, Q., Pires, D. E. V., Rodrigues, C. H. M., & Ascher, D. B. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad472 · Cited by 264 · see citing works

PremPS: Predicting the impact of missense mutations on protein stability

Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., & Li, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008543 · Cited by 257 · see citing works

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

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

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

Transfer learning to leverage larger datasets for improved prediction of protein stability changes

Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314853121 · Cited by 205 · see citing works

ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years

Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D., & Gromiha, M. M. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1035 · Cited by 199 · see citing works

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

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Rapid protein stability prediction using deep learning representations

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Embeddings from protein language models predict conservation and variant effects

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SCOPe: improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning

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ECNet is an evolutionary context-integrated deep learning framework for protein engineering

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Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

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DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations

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Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis

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Single-sequence protein structure prediction using supervised transformer protein language models

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INPS: predicting the impact of non-synonymous variations on protein stability from sequence

Fariselli, P., Martelli, P. L., Savojardo, C., & Casadio, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv291 · Cited by 147 · see citing works

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

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Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

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A method for multiple-sequence-alignment-free protein structure prediction using a protein language model

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Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC

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SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Li, G., Panday, S. K., & Alexov, E. G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22020606 · Cited by 114 · see citing works

ThermoMutDB: a thermodynamic database for missense mutations

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CAMEO - Perspectives on the future of fully automated evaluation of structure prediction methods

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An antisymmetric neural network to predict free energy changes in protein variants

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AUTO-MUTE 2.0: A Portable Framework with Enhanced Capabilities for Predicting Protein Functional Consequences upon Mutation

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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

Learning protein fitness landscapes with deep mutational scanning data from multiple sources

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Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy

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Zero‐shot mutation effect prediction on protein stability and function using RoseTTAFold

Mansoor, S., Baek, M., Juergens, D., Watson, J. L., & Baker, D. A. (2023) · Protein Science · doi:10.1002/pro.4780 · Cited by 40 · see citing works

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

Li, M., Kang, L., Xiong, Y., Wang, Y. G., Fan, G., Tan, P., & Hong, L. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00688-x · Cited by 38 · see citing works

PROSTATA: a framework for protein stability assessment using transformers

Umerenkov, D., Nikolaev, F., Shashkova, T., Страшнов, П. В., Sindeeva, M., Shevtsov, A., Ivanisenko, N. V., & Kardymon, O. L. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad671 · Cited by 36 · see citing works

AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction

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Predicting a Protein's Stability under a Million Mutations

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ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers

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Predicting protein stability changes upon mutation using a simple orientational potential

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Ultra-fast protein structure prediction to capture effects of sequence variation in mutation moviesPreprint

Weißenow, K., Heinzinger, M., Steinegger, M., & Rost, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.14.516473 · Cited by 28 · see citing works

Protein target highlights in CASP15 : Analysis of models by structure providers

Alexander, L. T., Durairaj, J., Kryshtafovych, A., Abriata, L. A., Bayo, Y., Bhabha, G., Breyton, C., Caulton, S. G., Chen, J., Degroux, S., et al. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26545 · Cited by 27 · see citing works

MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assaysPreprint

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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 · see citing works

Mega-scale experimental analysis of protein folding stability in biology and design

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Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scalePreprint

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Learning protein fitness landscapes with deep mutational scanning data from multiple sources

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<scp>GDFold2</scp> : A fast and parallelizable protein folding environment with freely defined objective functions

Mi, T., Xiao, N., & Gong, H. (2025) · Protein Science · doi:10.1002/pro.70041 · Cited by 1 · see citing works

Biophysics-based protein language models for protein engineering

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New strategies to enhance the efficiency and precision of drug discovery

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Protein stability models fail to capture epistatic interactions of double point mutations

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The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe

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Multidimensional computational strategies enhance the thermostability of alpha-galactosidase

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ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models

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Quantum mechanics paradox in protein structure prediction: Intrinsically linked to sequence yet independent of it

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Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence

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Decoding polyphenol–protein interactions with deep learning: From molecular mechanisms to food applications

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Decoding the effects of mutation on protein interactions using machine learning

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Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins

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DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models

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Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model

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ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding modelsPreprint

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Reshaping Protein‐Based Nanoparticles: Innovative Artificial Intelligence‐Driven Strategies for Structural Design and Applications

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Structure-guided engineering of L-aspartate-α-decarboxylase for enhanced substrate tolerance and β-alanine yield

Xu, L., Yuan, J., Wang, D., Wu, R., Liu, J., Cheng, X., Liang, X., & Li, N. (2025) · Food Bioscience · doi:10.1016/j.fbio.2025.107633 · Cited by 2 · see citing works

MEMO-Stab2: Multi-View Sequence-Based Deep Learning Framework for Predicting Mutation-Induced Stability Changes in Transmembrane Proteins

Bao, Y., Liu, Z., Jin, H. Z., Wang, H., Wang, W., & Lin, G. N. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01774 · Cited by 2 · see citing works

Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models

Deguchi, T., Ab Ghani, N. S., Kurumida, Y., Iida, S., Kobayashi, K., & Saito, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf536 · Cited by 2 · see citing works

BridgeNet: a high-efficiency framework integrating sequence and structure for protein and enzyme function prediction

Ye, Y., Duan, H., Mu, Y., Wu, L., & Guo, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf607 · Cited by 2 · see citing works

Improvement of Activity, Stability, and Soluble Expression of Nitrilase through Stepwise Sequence Optimization for Highly Efficient Synthesis of Nicotinic Acid

Han, L., Li, Z., Qiao, J., Cheng, Z., Pepłowski, Ł., Wang, Q., Luo, J., & Zhou, Z. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c14133 · Cited by 2 · see citing works

Thermal Stability Enhancement of a Carbohydrate-Binding Module for Hyaluronic Acid Based on an Integrated Computational Screening Strategy

Fan, C., Sun, M., Mei, X., Li, X., Su, M., Xue, C., & Chang, Y. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c12089 · Cited by 2 · see citing works

From evolution to rational design: AI-driven engineering of safe and highly efficient food enzymes

Wu, X., Yuan, Y., Wang, F., Lu, F., & Liu, F. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108901 · Cited by 2 · see citing works

Applications and limitations of AI tools in enzyme design

Teijeiro-Juiz, R., Egeler, N., Jamróg, G., Ringel, M., Brück, T., Geronimo, B. D., & Loll, B. (2026) · Protein Science · doi:10.1002/pro.70698 · Cited by 2 · see citing works

Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of MetricsPreprint

Страшнов, П. В., Shevtsov, A., Meshchaninov, V., Ivanova, M., Nikolaev, F., Kardymon, O. L., & Vetrov, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.25.620213 · Cited by 2 · see citing works

Accelerating Virtual Directed Evolution of Proteins via Reinforcement LearningPreprint

Mi, T., Wang, Y., Zhao, J., Wang, W., Shen, Y., Xiao, N., Chen, L., Chen, G., Zhang, S., Zhang, W., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.25.661516 · Cited by 3 · see citing works

Enzymes and Natural Products

Hanley, B. (2025) · Springer eBooks · doi:10.1007/978-3-031-81698-7_7 · Cited by 1 · see citing works

AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representations

Sindeeva, M., Telepov, A., Ivanisenko, N. V., Shashkova, T., Khrabrov, K., Tsypin, A., Kadurin, A., & Kardymon, O. L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf324 · Cited by 1 · see citing works

Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights

Lamy, A., Rajesh, D., & Pageat, P. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146605 · Cited by 1 · see citing works

ProDualNet: dual-target protein sequence design method based on protein language model and structure model

Cheng, L., Wei, T., Cui, X., Chen, H.-F., & Yu, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf391 · Cited by 1 · see citing works

Bridging structure and function: artificial intelligence-based modelling of kidney proteins

Wu, S., Wang, W., Zhou, Z. H., Scalzo, F., & Kurtz, I. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01060-6 · Cited by 1 · see citing works

Deep Learning for Protein Modeling: From Single Structure to Bound Complex and Thermodynamic Ensemble, With a Focus on Architectural Design

Angioletti, D., Carli, M., Nobile, M., & Limongelli, V. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70082 · Cited by 1 · see citing works

Review of major scientific research achievements on the 40th Anniversary of the Re-establishment of Department of Biology at Tsinghua University

Chen, G., Chen, Z., Huang, S., Liu, Y., Lu, Z. J., Ou, G., Qi, Y., Wang, X., Wang, Y., Xie, W., et al. (2024) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2024-0541 · Cited by 1 · see citing works

Genomic Insights Into Convergent Evolution: Adaptation to Rocky Habitats in Rock-Inhabiting Fungi

Fu, R., Yan, L., Wang, S., Wei, D., Wu, Q., Liu, X., & Xiang, M. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf249 · Cited by 1 · see citing works

Self-distillation and antisymmetric constraints enable accurate prediction of protein stability changes from single mutations

Wang, W., Yu, W., Yang, Q., Zheng, R., & Li, M. (2026) · Tsinghua Science & Technology · doi:10.1101/2025.05.18.654422 · Cited by 1 · see citing works

<scp>GDFold2</scp> : A fast and parallelizable protein folding environment with freely defined objective functions

Mi, T., Xiao, N., & Gong, H. (2025) · Protein Science · doi:10.1002/pro.70041 · Cited by 1 · see citing works

Rational design for improved thermostability of methionine adenosyltransferase based on FoldX, Rosetta, and multidimensional virtual screening

Yang, Y., Ma, H., 王东培, Jiang, Z., Wang, X., Zhao, Y., Li, R., & Xu, C. (2026) · Food Research International · doi:10.1016/j.foodres.2026.119535

Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering

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A graph retrieval-augmented generation pipeline for systematic drug target discovery: validation and application to ocular neovascularization

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The Role of Artificial Intelligence and Machine Learning in Predictive Virology: Forecasting, Tracking, and Combating Viral Threats

Farrag, M. A. (2026) · Vector-Borne and Zoonotic Diseases · doi:10.1177/15303667261469037

Thermostability modification and simulated application of acidic catalase

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An ensemble learning framework for protein stability prediction with enhanced recognition of stabilizing mutations

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A unified predictor of protein stability changes across all mutation types via implicit structure learning

Tan, H., Lin, S., & Xiong, Y. (2026) · Chemical Science · doi:10.1039/d6sc02103d

Role of machine learning in drug discovery and therapeutic protein design

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Engineering of an Fe2+/α-Ketoglutarate-Dependent Dioxygenase Enables Stereoselective and Efficient Sulfoxide Synthesis

Song, Z., Liu, D., Bi, F., Gao, Y., 施, 振., Cheng, J., Cao, W., Li, Q., Li, Y., Liu, Y., et al. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.6c07879

SPIRED-Fitness: an end-to-end framework for the prediction of protein structure and fitness from single sequence

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Single-sequence protein structure prediction using supervised transformer protein language models

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Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks

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Exploring zero-shot structure-based protein fitness predictionPreprint

Sharma, A., & Gitter, A. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.16886

Two-dimensional geometric template diffusion for boosting single-sequence protein structure prediction

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