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

Riesselman, A. J., Ingraham, J. B. & Marks, D. S. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15, 816–822 (2018).Article 

Google Scholar 
Dahiyat, B. I. & Mayo, S. L. De novo protein design: fully automated sequence selection. Science 278, 82–87 (1997).Article 

Google Scholar 
Tokuriki, N. & Tawfik, D. S. Stability effects of mutations and protein evolvability. Curr. Opin. Struct. Biol. 19, 596–604 (2009).Article 

Google Scholar 
Pucci, F., Bourgeas, R. & Rooman, M. High-quality thermodynamic data on the stability changes of proteins upon single-site mutations. J. Phys. Chem. Ref. Data 45, 023104 (2016).Article 

Google Scholar 
Yeoman, C. J. et al. in Advances in Applied Microbiology (eds Laskin, A. I. et al.) 1–55 (Elsevier, 2010); https://doi.org/10.1016/s0065-2164(10)70001-0Kopanos, C. et al. VarSome: the human genomic variant search engine. Bioinformatics 35, 1978–1980 (2018).Article 

Google Scholar 
Fowler, D. M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).Article 

Google Scholar 
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).Article 

Google Scholar 
Luo, Y. et al. ECNet is an evolutionary context-integrated deep learning framework for protein engineering. Nat. Commun. 12, 5743 (2021).Article 

Google Scholar 
Li, M. et al. SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering. J. Cheminform 15, 12 (2023).Article 

Google Scholar 
Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. In Proc. Advances in Neural Information Processing Systems Vol. 34 (eds Ranzato, M. et al.) 29287–29303 (Curran Associates, 2021).Rao, R. M. et al. MSA Transformer. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 8844–8856 (PMLR, 2021).Mansoor, S., Baek, M., Juergens, D., Watson, J. L. & Baker, D. Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold. Protein Sci. 32, e4780 (2023).Article 

Google Scholar 
Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).Article 

Google Scholar 
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).Article 
MathSciNet 

Google Scholar 
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).Article 

Google Scholar 
Montanucci, L., Capriotti, E., Frank, Y., Ben-Tal, N. & Fariselli, P. DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC Bioinformatics 20, 335 (2019).Article 

Google Scholar 
Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).Article 

Google Scholar 
Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G. & Sanavia, T. An antisymmetric neural network to predict free energy changes in protein variants. J. Phys. D Appl. Phys. 54, 245403 (2021).Article 

Google Scholar 
Li, B., Yang, Y. T., Capra, J. A. & Gerstein, M. B. Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks. PLoS Comput. Biol. 16, e1008291 (2020).Article 

Google Scholar 
Pancotti, C. et al. A deep-learning sequence-based method to predict protein stability changes upon genetic variations. Genes 12, 911 (2021).Article 

Google Scholar 
Fariselli, P., Martelli, P. L., Savojardo, C. & Casadio, R. INPS: predicting the impact of non-synonymous variations on protein stability from sequence. Bioinformatics 31, 2816–2821 (2015).Article 

Google Scholar 
Capriotti, E., Fariselli, P., Rossi, I. & Casadio, R. A three-state prediction of single point mutations on protein stability changes. BMC Bioinformatics 9, S6 (2008).Article 

Google Scholar 
Chen, Y. et al. PremPS: predicting the impact of missense mutations on protein stability. PLoS Comput. Biol. 16, e1008543 (2020).Article 

Google Scholar 
Zhou, Y., Pan, Q., Pires, D. E. V., Rodrigues, C. H. M. & Ascher, D. B. DDMut: predicting effects of mutations on protein stability using deep learning. Nucleic Acids Res. 51, W122–W128 (2023).Article 

Google Scholar 
Iqbal, S. et al. Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations. Brief. Bioinform. 22, bbab184 (2021).Article 

Google Scholar 
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).Article 

Google Scholar 
Pucci, F., Schwersensky, M. & Rooman, M. Artificial intelligence challenges for predicting the impact of mutations on protein stability. Curr. Opin. Struct. Biol. 72, 161–168 (2022).Article 

Google Scholar 
Masso, M. & Vaisman, I. I. AUTO-MUTE 2.0: a portable framework with enhanced capabilities for predicting protein functional consequences upon mutation. Adv. Bioinform. 2014, 278385 (2014).Article 

Google Scholar 
Pucci, F., Bourgeas, R. & Rooman, M. Predicting protein thermal stability changes upon point mutations using statistical potentials: introducing HoTMuSiC. Sci. Rep. 6, 23257 (2016).Article 

Google Scholar 
Louis, B. B. V. & Abriata, L. A. Reviewing challenges of predicting protein melting temperature change upon mutation through the full analysis of a highly detailed dataset with high-resolution structures. Mol. Biotechnol. 63, 863–884 (2021).Article 

Google Scholar 
Berman, H. M. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).Article 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 

Google Scholar 
Esposito, D. et al. MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect. Genome Biol. 20, 223 (2019).Article 

Google Scholar 
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).Article 

Google Scholar 
Usmanova, D. R. et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics 34, 3653–3658 (2018).Article 

Google Scholar 
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).Article 

Google Scholar 
Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S. & Lackner, P. Maestro—multi agent stability prediction upon point mutations. BMC Bioinformatics 16, 116 (2015).Article 

Google Scholar 
Tsuboyama, K. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023).Article 

Google Scholar 
Rodrigues, C. H., Pires, D. E. & Ascher, D. B. DynaMut2: assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci. 30, 60–69 (2020).Article 

Google Scholar 
Blondel, M., Teboul, O., Berthet, Q. & Djolonga, J. Fast differentiable sorting and ranking. In Proc. 37th International Conference of Machine Learning (eds Daume, H. & Singh, A.) 950–959 (ICML, 2020).Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D. & Gromiha, M. M. ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic Acids Res. 49, D420–D424 (2020).Article 

Google Scholar 
Xavier, J. S. et al. ThermoMutDB: a thermodynamic database for missense mutations. Nucleic Acids Res. 49, D475–D479 (2020).Article 

Google Scholar 
Akdel, M. et al. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 29, 1056–1067 (2022).Article 

Google Scholar 
Buel, G. R. & Walters, K. J. Can AlphaFold2 predict the impact of missense mutations on structure? Nat. Struct. Mol. Biol. 29, 1–2 (2022).Article 

Google Scholar 
Pak, M. A. et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS ONE 18, e0282689 (2023).Article 

Google Scholar 
Kumar, M. D. S. ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions. Nucleic Acids Res. 34, D204–D206 (2006).Article 

Google Scholar 
Nair, P. S. & Vihinen, M. Varibench: a benchmark database for variations. Hum. Mutat. 34, 42–49 (2013).Article 

Google Scholar 
Ingraham, J., Garg, V., Barzilay, R. & Jaakkola, T. Generative models for graph-based protein design. In Proc. Advances in Neural Information Processing Systems Vol. 32 (eds Wallach, H. et al.) 1417 (Curran Associates, 2019).Xu, Y., Liu, D. & Gong, H. Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy. Code Ocean https://doi.org/10.24433/CO.2318813.v1 (2024).

Hot Topics

Related Articles