Transfer learning to leverage larger datasets for improved prediction of protein stability changes
Open accessPublished version of a preprint
Abstract
Amino acid mutations that lower a protein’s thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein’s amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · 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
A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors
Naresh, V., & Lee, N. (2021) · Sensors · doi:10.3390/s21041109 · Cited by 2,132 · see citing works
Robust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107 · see citing works
Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,070 · see citing works
Guerois, R., Nielsen, J. E., & Serrano, L. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00442-4 · Cited by 1,728 · see citing works
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · see citing works
Self-citationPrediction of protein stability changes for single‐site mutations using support vector machines
Cheng, J. J., Randall, A. Z., & Baldi, P. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20810 · Cited by 1,165 · see citing works
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
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
Goldenzweig, A., Goldsmith, M., Hill, S. E., Gertman, O., Laurino, P., Ashani, Y., Dym, O., Unger, T., Albeck, S., Prilusky, J., et al. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.06.012 · Cited by 575 · see citing works
Eris: an automated estimator of protein stability
Yin, S., Ding, F., & Dokholyan, N. V. (2007) · Nature Methods · doi:10.1038/nmeth0607-466 · Cited by 405 · see citing works
Power of Biocatalysis for Organic Synthesis
Winkler, C. K., Schrittwieser, J. H., & Kroutil, W. (2021) · ACS Central Science · doi:10.1021/acscentsci.0c01496 · Cited by 396 · see citing works
Engineering protein-based therapeutics through structural and chemical design
Ebrahimi, S. B., & Samanta, D. (2023) · Nature Communications · doi:10.1038/s41467-023-38039-x · Cited by 389 · see citing works
Performance of protein stability predictors
Khan, S., & Vihinen, M. (2010) · Human Mutation · doi:10.1002/humu.21242 · Cited by 324 · see citing works
Using AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · see citing works
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
FoldX as Protein Engineering Tool: Better Than Random Based Approaches?
Buß, O., Rudat, J., & Ochsenreither, K. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.002 · Cited by 288 · see citing works
Principles of Protein Stability and Their Application in Computational Design
Goldenzweig, A., & Fleishman, S. J. (2018) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-062917-012102 · Cited by 283 · see citing works
FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants
Bednář, D., Beerens, K., Šebestová, E., Bendl, J. M., Khare, S. D., Chaloupková, R., Prokop, Z., Brezovský, J., Baker, D. A., & Damborský, J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004556 · Cited by 201 · see citing works
Rapid protein stability prediction using deep learning representations
Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., & Lindorff-Larsen, K. (2023) · eLife · doi:10.7554/elife.82593 · Cited by 177 · 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
Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis
Nisthal, A., Wang, C. Y., Ary, M. L., & Mayo, S. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903888116 · Cited by 163 · see citing works
Pancotti, C., Benevenuta, S., Birolo, G., Alberini, V., Repetto, V., Sanavia, T., Capriotti, E., & Fariselli, P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab555 · Cited by 144 · see citing works
Li, B., Yang, Y., Capra, J. A., & Gerstein, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008291 · Cited by 142 · see citing works
Light attention predicts protein location from the language of life
Stärk, H., Dallago, C., Heinzinger, M., & Rost, B. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab035 · Cited by 130 · see citing works
FireProtDB: database of manually curated protein stability data
Štourač, J., Dubrava, J., Musil, M., Horackova, J., Damborský, J., Mazurenko, S., & Bednář, D. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa981 · Cited by 129 · see citing works
An antisymmetric neural network to predict free energy changes in protein variants
Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., & Sanavia, T. (2021) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/abedfb · Cited by 70 · see citing works
Diaz, D. J., Gong, C., Ouyang-Zhang, J., Loy, J. M., Wells, J., Yang, D. K., Ellington, A. D., Dimakis, A. G., & Klivans, A. R. (2024) · Nature Communications · doi:10.1038/s41467-024-49780-2 · Cited by 66 · see citing works
Kepp, K. P. (2015) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2015.06.002 · Cited by 57 · see citing works
A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations
Pancotti, C., Benevenuta, S., Repetto, V., Birolo, G., Capriotti, E., Sanavia, T., & Fariselli, P. (2021) · Genes · doi:10.3390/genes12060911 · Cited by 44 · see citing works
BayeStab : Predicting effects of mutations on protein stability with uncertainty quantification
Wang, S., Tang, H., Zhao, Y., & Zuo, L. (2022) · Protein Science · doi:10.1002/pro.4467 · Cited by 43 · see citing works
ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks
Wang, S., Tang, H., Shan, P., Wu, Z., & Zuo, L. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107952 · Cited by 41 · 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
Predicting protein stability and solubility changes upon mutations: data perspective
Mazurenko, S. (2020) · ChemCatChem · doi:10.1002/cctc.202000933 · Cited by 34 · see citing works
Stabilizing proteins, simplified: A Rosetta‐based webtool for predicting favorable mutations
Thieker, D. F., Maguire, J. B., Kudlacek, S. T., Leaver‐Fay, A., Lyskov, S., & Kuhlman, B. A. (2022) · Protein Science · doi:10.1002/pro.4428 · Cited by 32 · see citing works
Self-citationBoosting protein stability with the computational design of β‐sheet surfaces
Kim, D. N., Jacobs, T., & Kuhlman, B. (2016) · Protein Science · doi:10.1002/pro.2869 · Cited by 29 · see citing works
Self-citationProbabilistic analysis of the frequencies of amino acid pairs within characterized protein sequences
Shen, S., Kai, B., Ruan, J., Huzil, J. T., Carpenter, E. J., & Tuszyński, J. A. (2006) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.03.004 · Cited by 24 · see citing works
Mega-scale experimental analysis of protein folding stability in biology and design
Tsuboyama, K., Dauparas, J., Chen, J., Laine, É., Behbahani, Y. M., Weinstein, J. J., Mangan, N. M., Ovchinnikov, S., & Rocklin, G. J. (2023) · RePEc: Research Papers in Economics · doi:10.5281/zenodo.7992926 · Cited by 10 · see citing works
Pak, M. A., Dovidchenko, N. V., Sharma, S. M., & Ivankov, D. N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.31.522396 · Cited by 10 · see citing works
Stability Oracle: A Structure-Based Graph-Transformer for Identifying Stabilizing MutationsPreprint
Diaz, D. J., Gong, C., Ouyang-Zhang, J., Loy, J. M., Wells, J., Yang, D. K., Ellington, A. D., Dimakis, A. G., & Klivans, A. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.15.540857 · Cited by 9 · see citing works
PROSTATA: Protein Stability Assessment using TransformersPreprint
Umerenkov, D., Shashkova, T., Страшнов, П. В., Nikolaev, F., Sindeeva, M., Ivanisenko, N. V., & Kardymon, O. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.25.521875 · Cited by 7 · see citing works
Mega-scale experimental analysis of protein folding stability in biology and design
Tsuboyama, K., Dauparas, J., Chen, J., Laine, É., Behbahani, Y. M., Weinstein, J. J., Mangan, N. M., Ovchinnikov, S., & Rocklin, G. J. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7401274 · Cited by 3 · see citing works
FireProtDB + PDB Structural Protein Stability Dataset
Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B. A. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8169288 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
De novo protein design—From new structures to programmable functions
Kortemme, T. (2024) · Cell · doi:10.1016/j.cell.2023.12.028 · Cited by 361 · see citing works
Machine learning for functional protein design
Notin, P., Rollins, N. J., Gal, Y., Sander, C., & Marks, D. S. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02127-0 · Cited by 278 · see citing works
Site-saturation mutagenesis of 500 human protein domains
Beltran, A., Jiang, X., Shen, Y., & Lehner, B. (2025) · Nature · doi:10.1038/s41586-024-08370-4 · Cited by 126 · see citing works
Engineering thermostability of industrial enzymes for enhanced application performance
Xu, K., Fu, H., Chen, Q., Sun, R., Li, R., Zhao, X., Zhou, J., & Wang, X. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.139067 · Cited by 88 · see citing works
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Chakravarty, D., Lee, M., & Porter, L. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102973 · Cited by 74 · see citing works
Biophysics-based protein language models for protein engineering
Gelman, S., Johnson, B., Freschlin, C. R., Sharma, A., D'Costa, S., Peters, J., Gitter, A., & Romero, P. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02776-2 · Cited by 70 · see citing works
Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery
Wang, Q., Liu, Y., Li, C., Xu, S., Xu, S., & Liu, B. (2025) · Advanced Science · doi:10.1002/advs.202503138 · Cited by 76 · see citing works
Sun, C., Li, H., Liu, Y., Li, Y., Gao, R. C., Shi, X., Fei, H., Liu, J., Liang, R., & Gao, C. (2025) · Cell · doi:10.1016/j.cell.2025.07.011 · Cited by 63 · see citing works
An end-to-end framework for the prediction of protein structure and fitness from single sequence
Chen, Y., Xu, Y., Liu, D., Xing, Y., & Gong, H. (2024) · Nature Communications · doi:10.1038/s41467-024-51776-x · Cited by 45 · see citing works
AI-driven de novo enzyme design: Strategies, applications, and future prospects
Cui, X.-C., Zheng, Y., Liu, Y., Yuchi, Z., & Yuan, Y. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108603 · Cited by 44 · see citing works
Multiplexed assays of variant effect for clinical variant interpretation
McEwen, A. E., Tejura, M., Fayer, S., Starita, L. M., & Fowler, D. M. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00870-x · Cited by 41 · see citing works
Autonomous biomedical research with an artificial intelligence agent
Huang, K., Zhang, S., Wang, H., Qu, Y., Lu, Y., Li, R., Roohani, Y., Qiu, L., Cao, S., Li, G. L., et al. (2026) · Science · doi:10.1126/science.adz4351 · Cited by 198 · see citing works
Predicting absolute protein folding stability using generative models
Cagiada, M., Ovchinnikov, S., & Lindorff‐Larsen, K. (2024) · Protein Science · doi:10.1002/pro.5233 · Cited by 38 · see citing works
Grigorakis, K., Ferousi, C., & Topakas, E. (2025) · Catalysts · doi:10.3390/catal15020147 · Cited by 37 · see citing works
Aligning protein generative models with experimental fitness via Direct Preference OptimizationPreprint
Widatalla, T., Rafailov, R., & Hie, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.20.595026 · Cited by 35 · see citing works
Language Modeling Materializes a World Model of Protein BiologyPreprint
Candido, S., Hayes, T., Derry, A., Rao, R., Lin, Z., Verkuil, R., Wu, B. Z., Lee, J. S., Bruguera, E. S., Keval, J. A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729735 · Cited by 65 · see citing works
Zhu, C., Mu, J., & Liang, L. (2024) · Journal of Nanobiotechnology · doi:10.1186/s12951-024-02969-5 · Cited by 34 · see citing works
Sun, J., Zhu, T., Cui, Y., & Wu, B. (2025) · The Innovation · doi:10.1016/j.xinn.2024.100750 · Cited by 45 · see citing works
Computational design of conformation-biasing mutations to alter protein functions
Cavanagh, P. E., Xue, A. G., Dai, S. A., Qiang, A., Matsui, T., & Ting, A. Y. (2026) · Science · doi:10.1126/science.adv7953 · Cited by 31 · see citing works
Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies
Zhang, X., Zhang, W., Zhang, H., & Liao, X. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1546474 · Cited by 30 · see citing works
Protein stability models fail to capture epistatic interactions of double point mutations
Dieckhaus, H., & Kuhlman, B. A. (2024) · Protein Science · doi:10.1002/pro.70003 · Cited by 27 · see citing works
Self-citationFrom traditional to AI-driven: The evolution of intelligent enzyme engineering for biocatalysis
Lv, F., Zhang, J., You, S., & Qi, W. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108788 · Cited by 28 · see citing works
SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions
Blaabjerg, L. M., Jonsson, N., Boomsma, W., Stein, A., & Lindorff‐Larsen, K. (2024) · Nature Communications · doi:10.1038/s41467-024-53982-z · Cited by 28 · see citing works
Large-scale discovery, analysis and design of protein energy landscapes
Ferrari, Á. J. R., Dixit, S. M., Thibeault, J., Garcia, M., Houliston, S., Ludwig, R. W., Notin, P., Phoumyvong, C. M., Martell, C. M., Jung, M. D., et al. (2026) · Nature · doi:10.1038/s41586-026-10465-z · Cited by 22 · see citing works
Generalizable and scalable protein stability prediction with rewired protein generative models
Li, Z., & Luo, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-67609-4 · Cited by 22 · see citing works
Zheng, F., Liu, Y., Yang, Y. J., Wen, Y., & Li, M. (2023) · Protein Science · doi:10.1002/pro.4861 · Cited by 22 · see citing works
Savojardo, C., Manfredi, M., Martelli, P. L., & Casadio, R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btaf019 · Cited by 22 · see citing works
Protein Language Models: Applications and Perspectives
Leclercq, M., & Droit, A. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00506 · Cited by 26 · see citing works
<scp>ESM</scp> ‐scan—A tool to guide amino acid substitutions
Totaro, M. G., Vide, U., Zausinger, R., Winkler, A., & Oberdorfer, G. (2024) · Protein Science · doi:10.1002/pro.5221 · Cited by 19 · see citing works
Can protein expression be ‘solved’?
Baranowski, C., Martín, H. G., Oyarzún, D. A., Spinner, A., Desai, B. J., Petzold, C. J., Nikolados, E.-M., Kraatz, S. H. W., Gaber, A., Chalkley, R. J., et al. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.04.021 · Cited by 20 · see citing works
How artificial intelligence is reengineering protein engineering
Listgarten, J., & Jiang, H. (2026) · Science · doi:10.1126/science.aec8444 · Cited by 17 · see citing works
Estevam, G. O., Linossi, E., Rao, J., Macdonald, C. B., Ravikumar, A., Chrispens, K. M., Capra, J. A., Coyote-Maestas, W., Pimentel, H., Collisson, E. A., et al. (2025) · eLife · doi:10.7554/elife.101882 · Cited by 17 · see citing works
Gurusinghe, S. N. S., Wu, Y., DeGrado, W. F., & Shifman, J. M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf270 · Cited by 15 · see citing works
Liang, T., Sun, Z., Ishima, R., Xie, X., Xue, Y., Li, W., & Feng, Z. (2025) · Research · doi:10.34133/research.0674 · Cited by 14 · see citing works
Garcia, M., Dixit, S. M., & Rocklin, G. J. (2026) · Protein Science · doi:10.1002/pro.70453 · Cited by 25 · see citing works
Chungyoun, M., & Gray, J. R. (2026) · Patterns · doi:10.64898/2025.12.27.696706 · Cited by 16 · see citing works
Xu, S., Chen, Y.-M., Meng, X., Pan, R., Yan, A.-X., Li, Z., & Li, Z. (2025) · Bioresource Technology · doi:10.1016/j.biortech.2025.133081 · Cited by 13 · see citing works
Cuturello, F., Celoria, M., Ansuini, A., & Cazzaniga, A. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae447 · Cited by 11 · see citing works
Küng, C., Protsenko, O., Vanella, R., & Nash, M. A. (2025) · Protein Science · doi:10.1002/pro.70112 · Cited by 14 · see citing works
A small molecule stabilizer rescues the surface expression of nearly all missense variants in a GPCR
Mighell, T. L., & Lehner, B. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01659-6 · Cited by 13 · see citing works
HyperMPNN – A general strategy to design thermostable proteins learned from hyperthermophilesPreprint
Ertelt, M., Schlegel, P., Beining, M., Kaysser, L., Meiler, J., & Schoeder, C. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.26.625397 · Cited by 12 · see citing works
Structure and immunogenicity of an engineered soluble prefusion-stabilized EBV gB antigen
McCool, R. S., Acreman, C. M., Powell, A. E., Picucci, S. I., Stieh, D. J., Chou, C., Huynh, J., Caruso, H., Park, S., O’Rear, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67969-x · Cited by 11 · see citing works
Reaching New Heights in Genetic Code Manipulation with High Throughput Screening
Lino, B. R., Williams, S. J., Castor, M. E., & Deventer, J. A. V. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00329 · Cited by 11 · see citing works
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 · see citing works
Beltran, A., Jiang, X., Shen, Y., & Lehner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.26.591310 · Cited by 11 · see citing works
Reliable prediction of protein–protein binding affinity changes upon mutations with Pythia-PPI
Tao, F., Sun, J., Gao, P., Gao, G. F., & Wu, B. (2025) · National Science Review · doi:10.1093/nsr/nwaf231 · Cited by 10 · see citing works
Computation of Protein Thermostability and Epistasis
Peccati, F., Segovia, C. M., Núñez‐Franco, R., & Jiménez‐Osés, G. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70045 · Cited by 11 · see citing works
The genetic architecture of an allosteric hormone receptor
Stammnitz, M. R., & Lehner, B. (2026) · Nature Communications · doi:10.1038/s41467-026-70341-2 · Cited by 10 · see citing works
Allostery is a widespread cause of loss-of-function variant pathogenicity
Liao, X., & Lehner, B. (2026) · Nature Communications · doi:10.1038/s41467-026-74517-8 · Cited by 12 · see citing works
Protein folding stability estimation with explicit consideration of unfolded states
Lee, H. C., Cho, Y., Yun, J., Steinegger, M., Kim, H. M., & Park, H. (2026) · Nature Communications · doi:10.1038/s41467-026-68637-4 · Cited by 11 · see citing works
Lin, J., Wu, W., Nie, G., Li, J., Fang, X., Sheng, Y.-G., Wang, M., Zheng, Q.-Y., Guo, X., Huang, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64323-z · Cited by 8 · see citing works
Engineering complexity into protein-based biomaterials for biomedical applications
Gregorio, N. E., Haas, C. M., King, N. P., & DeForest, C. A. (2025) · Nature Reviews Materials · doi:10.1038/s41578-025-00861-8 · Cited by 10 · see citing works
Evaluating zero-shot prediction of protein design success by AlphaFold, ESMFold, and ProteinMPNNPreprint
Garcia, M., Dixit, S. M., & Rocklin, G. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.29.667290 · Cited by 8 · see citing works
Nie, Z., Ma, Y., Liu, Y., Huang, X., Liu, Z., Peng, Y., Xu, F., Yin, F., Li, Z., Fu, J., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf319 · Cited by 7 · see citing works
Feldman, J., Brogi, M., & Skolnick, J. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0142 · Cited by 10 · see citing works
When “loss-of-function” means proteostasis burden: Thinking again about coding DNA variants
Shovlin, C. L., & Aldred, M. A. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.12.002 · Cited by 7 · see citing works
Decoding the effects of mutation on protein interactions using machine learning
Wang, X., Li, A., Zhao, Y., & Peng, Y. (2025) · Biophysics Reviews · doi:10.1063/5.0249920 · Cited by 6 · see citing works
Estevam, G. O., Linossi, E., Rao, J., Macdonald, C. B., Ravikumar, A., Chrispens, K. M., Capra, J. A., Coyote-Maestas, W., Pimentel, H., Collisson, E. A., et al. (2025) · eLife · doi:10.7554/elife.101882.3 · Cited by 6 · see citing works
Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNNPreprint
Shuai, R. W., Widatalla, T., Huang, P., & Hie, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.13.637498 · Cited by 7 · see citing works
Intrinsic dataset features drive mutational effect prediction by protein language modelsPreprint
Vieira, L. C., Lin, S., & Wilke, C. O. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.08.710389 · Cited by 10 · see citing works
Tang, J., Gong, D., Li, H., & Li, S. (2026) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2026.01.039 · Cited by 5 · see citing works
Čondić‐Jurkić, K., Alibay, I., Sherman, W., Tollefson, M. R., Walters, W. P., Baker, Z., Chong, L. T., Wei, J. N., Gray, J. J., Weitzner, B. D., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03137 · Cited by 6 · see citing works
Birnbaum, F., & Keating, A. E. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2535494123 · Cited by 6 · see citing works
Ganesan, S., Mittal, N., Bhat, A., Adiga, R. S., Ganesan, A., Nagarajan, D., & Varadarajan, R. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26738 · Cited by 5 · see citing works
Gupta, A. K., Esih, H., Gradišar, H., & Jerala, R. (2025) · Materials Advances · doi:10.1039/d5ma00792e · Cited by 4 · see citing works
CyclicMPNN: stable cyclic peptide sequence generation
Powers, A. C., Janthana, Y., & Hosseinzadeh, P. (2026) · Digital Discovery · doi:10.1039/d6dd00083e · Cited by 5 · see citing works
Predicting the structure-altering mechanisms of disease variants
Arnaudi, M., Utichi, M., Tiberti, M., & Papaleo, E. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102994 · Cited by 4 · see citing works
Energetic and structural control of polyspecificity in a multidrug transporter
Miller, S. T., Henzler‐Wildman, K. A., & Raman, S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2511892122 · Cited by 4 · see citing works
Supervised learning of protein variant effects across large‐scale mutagenesis datasets
Schulze, T. K., Blaabjerg, L. M., Cagiada, M., & Lindorff‐Larsen, K. (2026) · Protein Science · doi:10.1002/pro.70526 · Cited by 11 · see citing works
Zhang, J., Wang, S., Gao, L., & Li, Q. (2026) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2026.03.012 · Cited by 4 · see citing works
Phosphate-binding proteins and peptides: from molecular mechanisms to potential applications
Mayer, B. K., Hutchison, J. M., McLamore, E. S., Torres, M. J., & Venkiteshwaran, K. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103199 · Cited by 4 · see citing works
Studies of Isonitrile Synthases Help Elucidate the Mechanism of Isonitrile Installation
Chen, T., Kim, W., Guo, M., Yao, A., Zhang, Y., & Chang, W. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c03282 · Cited by 4 · see citing works
Ou, W., Jing, D., Guo, X., Liu, Q., & Tan, L. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00112 · Cited by 4 · see citing works
Qin, W., Deng, Y., Ren, H., Liu, Y., Liu, L., Liu, W., Zhao, Y., Li, C., & Yang, Z. (2024) · Cancer Medicine · doi:10.1002/cam4.70252 · Cited by 3 · see citing works
BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein InteractionsPreprint
Lu, W., Zhang, J., Gu, M., & Zheng, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.03.626712 · Cited by 4 · see citing works
Tan, H., Wei, X., Lin, S., Mao, X., Chen, J., Sun, H., Zhang, Y., Zhou, Z., Wei, D., Lin, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.11.669595 · Cited by 4 · see citing works
Woodard, J., Liu, Z., Chegini, A. M., Tian, J., Bhowmick, R., Pennathur, S., Mashaghi, A., Brender, J. R., & Chandrasekaran, S. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101169 · Cited by 3 · see citing works
Combined computational, rational, and empirical design of boiling-resistant keratinase
Yang, Y., Luo, Y., Ding, Y., Yao, Y., Liu, J., Xu, Z., Li, Y., Li, H., Gan, F., Tang, X., et al. (2025) · Applied and Environmental Microbiology · doi:10.1128/aem.01860-25 · Cited by 3 · see citing works
Barducci, G., Rossi, I., Codicè, F., Rollo, C., Repetto, V., Pancotti, C., Iannibelli, V., Sanavia, T., & Fariselli, P. (2026) · Communications Biology · doi:10.1038/s42003-026-09632-9 · Cited by 3 · see citing works
Using experimental results of protein design to guide biomolecular energy-function development
Haddox, H. K., Rocklin, G. J., Motta, F. C., Strickland, D., Halabiya, S. F., Cordray, C., Park, H., Klavins, E., Baker, D. A., & DiMaio, F. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014215 · Cited by 3 · see citing works
Mechanisms of inflammatory bowel diseases: Insights from human genetics and chemical biology
Graham, D. B., & Xavier, R. J. (2026) · Immunity · doi:10.1016/j.immuni.2026.04.002 · Cited by 3 · see citing works
The mutational landscape of STING-induced immunity
Zhang, B., Xu, P., Meng, Y., Gallay, L., Lestelle, F., Morel, H., Frémond, M., Correia, B. E., & Ablasser, A. (2026) · Nature · doi:10.1038/s41586-026-10685-3 · Cited by 3 · see citing works
Rehmann, C. T., Small, S. T., Ralph, P. L., & Kern, A. D. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf141 · Cited by 3 · see citing works
Zhang, Y., Deng, J., Dong, M., Wu, J., Zhao, Q., Gao, X., & Xiong, D. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107476 · Cited by 3 · see citing works
AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations
Bhargav, P., & Mukherjee, A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01387 · Cited by 2 · see citing works
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
Learnable protein representations in computational biology for predicting drug-target affinity
Kumar, R., Romano, J. D., & Ritchie, M. D. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-025-01145-7 · Cited by 2 · see citing works
Hsu, W.-L., Jiao, Y., Hvasta, M. G., Tran, K. N., Troyer, B. T., Huang, W., Kuhlman, B. A., Obregón‐Henao, A., & Lovell, J. F. (2026) · Vaccine · doi:10.1016/j.vaccine.2026.128281 · Cited by 2 · see citing works
Self-citationComprehensive mutagenesis defines the functional landscape of human α-tubulin
Xu, K., Guo, Z., Chen, Z., Li, M., Chen, Z., Zhang, D., Wang, Y., Chai, Y., Zhang, J., Wang, H., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2600822123 · Cited by 2 · see citing works
Xiao, B., Zhang, S., Ainiwaer, M., Liu, H., Ning, L., Hong, Y., Sun, Y., & Ji, Y. (2025) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2024-001906 · Cited by 2 · see citing works
Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies
Patra, S. K., Randolph, N. Z., Kuhlman, B. A., Dieckhaus, H., Betts, L., Douglas, J., Wills, P. R., & Carter, C. W. (2025) · Structural Dynamics · doi:10.1063/4.0000294 · Cited by 2 · see citing works
Self-citationNumabe, R., Rainish, A., Niv, M. Y., & Imai, H. (2025) · Chemical Senses · doi:10.1093/chemse/bjaf027 · Cited by 2 · see citing works
Modular Engineering of Thermo-Responsive Allosteric ProteinsPreprint
Hoffmann, K. H., Kroell, A.-S. H., Motzkus, N. A., Lemmen, N., Happ, N., Wolf, B., Bachmann, A., Southern, N. T., Vogd, F., Aschenbrenner, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651844 · Cited by 2 · see citing works
Design, Structure, and Immunogenicity of a Soluble Prefusion-stabilized EBV gB AntigenPreprint
McCool, R. S., Acreman, C. M., Powell, A. E., Picucci, S. I., Stieh, D. J., Huynh, J., Caruso, H., Park, S., O’Rear, J., Chen, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.19.654955 · Cited by 2 · see citing works
Learning Dynamic Protein Representations at Scale with DistogramsPreprint
Portal, N., Karroucha, W., Mallet, V., & Bonomi, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.29.702509 · Cited by 2 · see citing works
Effects of protein interface mutations on protein quality and affinityPreprint
de Kanter, J. K., Smorodina, E., Minnegalieva, A. R., Arts, M., Blaabjerg, L. M., Frolenkova, M., Rawat, P., Wolfram, L., Britze, H., Wilke, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.24.713863 · Cited by 2 · see citing works
Determinants of metal import and specificity in a bacterial transporterPreprint
Berry, S. P., Freedman, C. B., Marks, D. S., & Gaudet, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.30.714904 · Cited by 2 · see citing works
Li, G., Yao, S., & Fan, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf190 · Cited by 1 · see citing works
Yu, Y., Jiang, F., Ma, X., Zhang, L., Zhong, B., Ouyang, W., Fan, G., Yu, H., Hong, L., & Li, M. (2025) · neural information processing systems · doi:10.52202/085713-4581 · Cited by 1 · see citing works
- Authors
- Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Kuhlman, Brian
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2024-01-29
- Volume
- 121
- Issue
- 6
- Pages
- e2314853121-e2314853121
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC10861915
- OpenAlex
- W4391308485
- Cited by
- 205
- References
- 47
- Citations by year
- 2026: 104 · 2025: 74 · 2024: 25 · 2023: 2
- FWCI
- 27.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nd)
- Funders
- HHS | NIH | National Institute of General Medical Sciences (R35GM131923)
- NSF | EDU | Division of Graduate Education (DGE-2040435)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein stability prediction, transfer learning, ProteinMPNN, point mutations, protein thermodynamic stability, deep neural network, protein design, learned features, engineered proteins
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 ›
Are you an author, or do you see a problem with this page? Request a review
