Bell, E. L. et al. Biocatalysis. Nat. Rev. Methods Primers 1, 46 (2021).Mesbahuddin, M. S., Ganesan, A. & Kalyaanamoorthy, S. Engineering stable carbonic anhydrases for CO2 capture: a critical review. Protein Eng. Des. Sel. 34, gzab021 (2021).ArticleÂ
Google ScholarÂ
Stourac, J. et al. FireProtDB: database of manually curated protein stability data. Nucleic Acids Res. 49, D319–D324 (2020).ArticleÂ
Google ScholarÂ
Arnold, F. H. Design by directed evolution. Acc. Chem. Res. 31, 125–131 (1998).ArticleÂ
Google ScholarÂ
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).ArticleÂ
Google ScholarÂ
Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H. Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Natl Acad. Sci. USA 116, 8852–8858 (2019).ArticleÂ
Google ScholarÂ
Wittmann, B. J., Johnston, K. E., Wu, Z. & Arnold, F. H. Advances in machine learning for directed evolution. Curr. Opin. Struct. Biol. 69, 11–18 (2021).ArticleÂ
Google ScholarÂ
Yang, Y. et al. ProTstab—predictor for cellular protein stability. BMC Genomics 20, 804 (2019).ArticleÂ
Google ScholarÂ
Jung, F., Frey, K., Zimmer, D. & Mühlhaus, T. DeepSTABp: a deep learning approach for the prediction of thermal protein stability. Int. J. Mol. Sci. 24, 7444 (2023).ArticleÂ
Google ScholarÂ
Tsuboyama, K. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023).ArticleÂ
Google ScholarÂ
Broom, A., Trainor, K., Jacobi, Z. & Meiering, E. M. Computational modeling of protein stability: quantitative analysis reveals solutions to pervasive problems. Structure 28, 717–726.e3 (2020).ArticleÂ
Google ScholarÂ
Broom, A., Jacobi, Z., Trainor, K. & Meiering, E. M. Computational tools help improve protein stability but with a solubility tradeoff. J. Biol. Chem. 292, 14349–14361 (2017).ArticleÂ
Google ScholarÂ
Frenz, B. et al. Prediction of protein mutational free energy: benchmark and sampling improvements increase classification accuracy. Front. Bioeng. Biotechnol. 8, 55824 (2020).ArticleÂ
Google ScholarÂ
Hernández, I. M., Dehouck, Y., Bastolla, U., López-Blanco, J. R. & Chacón, P. Predicting protein stability changes upon mutation using a simple orientational potential. Bioinformatics 39, btad011 (2023).ArticleÂ
Google ScholarÂ
Fang, J. A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation. Brief. Bioinform. 21, 1285–1292 (2019).ArticleÂ
Google ScholarÂ
Sanavia, T. et al. Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput. Struct. Biotechnol. J. 18, 1968–1979 (2020).ArticleÂ
Google ScholarÂ
Rigoldi, F., Donini, S., Redaelli, A., Parisini, E. & Gautieri, A. Review: Engineering of thermostable enzymes for industrial applications. APL Bioeng. 2, 011501 (2018).ArticleÂ
Google ScholarÂ
Alford, R. F. et al. The rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13, 15499626 (2017).ArticleÂ
Google ScholarÂ
Diaz, D. J. et al. Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations. Nat. Commun. 15, 6170 (2024).ArticleÂ
Google ScholarÂ
Jarzab, A. et al. Meltome atlas-thermal proteome stability across the tree of life. Nat. Methods 17, 495–503 (2020).ArticleÂ
Google ScholarÂ
Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Adv. Neural Inf. Process. Syst. 34, 29287–29303 (2021).
Google ScholarÂ
Hsu, C. et al. Learning inverse folding from millions of predicted structures. In Proc. 39th International Conference on Machine Learning 8946–8970 (PMLR, 2022).Yang, K. K., Zanichelli, N. & Yeh, H. Masked inverse folding with sequence transfer for protein representation learning. Protein Eng. Des. Sel. 36, gzad015 (2023).ArticleÂ
Google ScholarÂ
Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).ArticleÂ
Google ScholarÂ
d’Oelsnitz, S. et al. Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme. Nat. Commun. 15, 2084 (2024).ArticleÂ
Google ScholarÂ
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).ArticleÂ
MathSciNetÂ
Google ScholarÂ
Elnaggar, A. et al. Ankh: optimized protein language model unlocks general-purpose modelling. Preprint at https://arxiv.org/abs/2301.06568 (2023).Rao, R. M. et al. MSA Transformer. In Proc. 38th International Conference on Machine Learning 8844–8856 (PMLR, 2021).Notin, P. et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In Proc. 39th International Conference on Machine Learning 16990–17017 (PMLR, 2022).Pucci, F., Bernaerts, K. V., Kwasigroch, J. M. & Rooman, M. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics 34, 3659–3665 (2018).ArticleÂ
Google ScholarÂ
Caldararu, O., Blundell, T. L. & Kepp, K. P. Three simple properties explain protein stability change upon mutation. J. Chem. Inf. Model. 61, 1981–1988 (2021).ArticleÂ
Google ScholarÂ
Konopka, B. M., Marciniak, M. & Dyrka, W. Quantiprot—a Python package for quantitative analysis of protein sequences. BMC Bioinform. 18, 339 (2017).ArticleÂ
Google ScholarÂ
Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577–2637 (1983).ArticleÂ
Google ScholarÂ
Touw, W. G. et al. A series of PDB-related databanks for everyday needs. Nucleic Acids Res. 43, D364–D368 (2015).ArticleÂ
Google ScholarÂ
Tokuriki, N. & Tawfik, D. S. Stability effects of mutations and protein evolvability. Curr. Opin. Struct. Biol. 19, 596–604 (2009).ArticleÂ
Google ScholarÂ
Hopf, T. A. et al. Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 35, 128–135 (2017).ArticleÂ
Google ScholarÂ
Fersht, A. in Structure and Mechanism in Protein Science 2nd edn 508–536 (W. H. Freeman and Company, 1999).Hsu, C., Nisonoff, H., Fannjiang, C. & Listgarten, J. Learning protein fitness models from evolutionary and assay-labeled data. Nat. Biotechnol. 40, 1114–1122 (2022).ArticleÂ
Google ScholarÂ
Laine, E., Karami, Y. & Carbone, A. GEMME: a simple and fast global epistatic model predicting mutational effects. Mol. Biol. Evol. 36, 2604–2619 (2019).ArticleÂ
Google ScholarÂ
Høie, M. H., Cagiada, M., Beck Frederiksen, A. H., Stein, A. & Lindorff-Larsen, K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Rep. 38, 110207 (2022).ArticleÂ
Google ScholarÂ
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M. & Church, G. M. Low-N protein engineering with data-efficient deep learning. Nat. Methods 18, 389–396 (2021).ArticleÂ
Google ScholarÂ
Wittmann, B. J., Yue, Y. & Arnold, F. H. Informed training set design enables efficient machine learning-assisted directed protein evolution. Cell Syst. 12, 1026-1045.e7 (2021).
Google ScholarÂ
Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl Acad. Sci. USA 114, 3521–3526 (2017).ArticleÂ
MathSciNetÂ
Google ScholarÂ
Eswar, N. et al. Comparative protein structure modeling using modeller. Curr. Protoc. Bioinform. 5, 5–6 (2006).
Google ScholarÂ
PDBe-KB consortium PDBe-KB: collaboratively defining the biological context of structural data. Nucleic Acids Res. 50, D534–D542 (2022).ArticleÂ
Google ScholarÂ
Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinform. 20, 473 (2019).ArticleÂ
Google ScholarÂ
Quan, L., Lv, Q. & Zhang, Y. STRUM: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics 32, 2936–2946 (2016).ArticleÂ
Google ScholarÂ
Pancotti, C. et al. Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset. Brief. Bioinform. 23, bbab555 (2022).ArticleÂ
Google ScholarÂ
Dehouck, Y. et al. Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics 25, 2537–2543 (2009).ArticleÂ
Google ScholarÂ
Ye, Y. & Godzik, A. FATCAT: a web server for flexible structure comparison and structure similarity searching. Nucleic Acids Res. 32, W582–W585 (2004).ArticleÂ
Google ScholarÂ
Reeves, S. & Kalyaanamoorthy, S. skalyaanamoorthy/PSLMs: PSLMs for thermostability prediction full release. Zenodo https://doi.org/10.5281/zenodo.12702047 (2024).Dehouck, Y., Kwasigroch, J. M., Gilis, D. & Rooman, M. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform. 12, 151 (2011).ArticleÂ
Google ScholarÂ