Multi-task deep kernel learning for uncertainty-aware prediction of protein stability changes
Abstract
Accurate prediction of mutation-induced stability changes is crucial for interpreting variant effects and guiding protein engineering, but integrating heterogeneous measurements and quantifying uncertainty remain challenging. Here we present STAB-DKL, an uncertainty-aware multi-task deep kernel learning framework that combines curated and megascale folding free-energy changes (ΔΔG) with melting-temperature changes (ΔTm) as auxiliary supervision. Selective representation sharing and dataset-specific Gaussian process (GP) heads enable joint learning across datasets. The framework integrates pretrained sequence and structural representations with StrucMSA, a compact and computationally efficient evolutionary representation incorporating local structural context. Across four held-out benchmarks, STAB-DKL achieved higher average predictive correlations than the evaluated methods and provided informative uncertainty estimates. On a retrospective seven-protein benchmark, it improved ranking of stabilizing mutations (NDCG@5) by 68% over ThermoMPNN, the strongest comparator. Together, these results demonstrate how multi-task fusion and dataset-specific probabilistic modeling of heterogeneous data enable accurate, uncertainty-aware protein stability prediction and stabilizing mutation prioritization.
References
Balchin, D., Hayer-Hartl, M. & Hartl, F. U. In vivo aspects of protein folding and quality control. Science 353, aac4354 (2016).
Gershenson, A., Gierasch, L. M., Pastore, A. & Radford, S. E. Energy landscapes of functional proteins are inherently risky. Nat Chem Biol 10, 884–891 (2014).
Sumida, K. H. et al. Improving Protein Expression, Stability, and Function with ProteinMPNN. J Am Chem Soc 146, 2054–2061 (2024).
Wilson, A. E., Kosater, W. M. & Liberles, D. A. Evolutionary Processes and Biophysical Mechanisms: Revisiting Why Evolved Proteins Are Marginally Stable. J Mol Evol 88, 415–417 (2020).
Stein, A., Fowler, D. M., Hartmann-Petersen, R. & Lindorff-Larsen, K. Biophysical and Mechanistic Models for Disease-Causing Protein Variants. Trends in Biochemical Sciences 44, 575–588 (2019).
Cagiada, M. et al. Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance. Mol Biol Evol 38, 3235–3246 (2021).
Beltran, A., Jiang, X., Shen, Y. & Lehner, B. Site-saturation mutagenesis of 500 human protein domains. Nature 637, 885–894 (2025).
Hoie, 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).
Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nature Reviews Molecular Cell Biology 10, 866–876 (2009).
Tokuriki, N. & Tawfik, D. S. Stability effects of mutations and protein evolvability. Current Opinion in Structural Biology 19, 596–604 (2009).
Goldenzweig, A. & Fleishman, S. J. Principles of Protein Stability and Their Application in Computational Design. Annu Rev Biochem 87, 105–129 (2018).
Listov, D., Goverde, C. A., Correia, B. E. & Fleishman, S. J. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 25, 639–653 (2024).
Marabotti, A., Scafuri, B. & Facchiano, A. Predicting the stability of mutant proteins by computational approaches: an overview. Briefings in Bioinformatics 22, bbaa074 (2021).
Leman, J. K. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nature Methods 17, 665–680 (2020).
Delgado, J., Radusky, L. G., Cianferoni, D. & Serrano, L. FoldX 5.0: working with RNA, small molecules and a new graphical interface. Bioinformatics 35, 4168–4169 (2019).
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 (2022).
Tsuboyama, K. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023).
Dieckhaus, H., Brocidiacono, M., Randolph, N. Z. & Kuhlman, B. Transfer learning to leverage larger datasets for improved prediction of protein stability changes. Proc Natl Acad Sci U S A 121, e2314853121 (2024).
Li, Z. & Luo, Y. Generalizable and scalable protein stability prediction with rewired protein generative models. Nat Commun 17, 891 (2026).
Diaz, D. J. et al. Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations. Nat Commun 15, 6170 (2024).
Chen, Y., Xu, Y., Liu, D., Xing, Y. & Gong, H. An end-to-end framework for the prediction of protein structure and fitness from single sequence. Nat Commun 15, 7400 (2024).
Nie, Z. et al. Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence. Briefings in Bioinformatics 26, bbaf319 (2025).
Montanucci, L. et al. DDGun: an untrained predictor of protein stability changes upon amino acid variants. Nucleic Acids Res 50, W222–W227 (2022).
Capriotti, E., Fariselli, P. & Casadio, R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33, W306–W310 (2005).
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).
Folkman, L., Stantic, B., Sattar, A. & Zhou, Y. EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models. Journal of Molecular Biology 428, 1394–1405 (2016).
Blaabjerg, L. M. et al. Rapid protein stability prediction using deep learning representations. Elife 12 (2023).
Umerenkov, D. et al. PROSTATA: a framework for protein stability assessment using transformers. Bioinformatics 39 (2023).
Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning. (The MIT Press, 2005).
Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc Natl Acad Sci U S A 110, E193–E201 (2013).
Hie, B., Bryson, B. D. & Berger, B. Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design. Cell Syst 11, 461–477 e469 (2020).
Wang, C. & Balch, W. E. Bridging Genomics to Phenomics at Atomic Resolution through Variation Spatial Profiling. Cell Rep 24, 2013–2028 e2016 (2018).
Zhao, P., Wang, C., Sun, S., Wang, X. & Balch, W. E. Tracing genetic diversity captures the molecular basis of misfolding disease. Nat Commun 15, 3333 (2024).
Jokinen, E., Heinonen, M. & Lahdesmaki, H. mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinformatics 34, i274–i283 (2018).
Wilson, A. G., Hu, Z., Salakhutdinov, R. & Xing, E. P. Deep Kernel Learning. in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics Vol. 51, 370–378 (PMLR, Proceedings of Machine Learning Research, 2016).
Wilson, A. & Nickisch, H. Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). in Proceedings of the 32nd International Conference on Machine Learning Vol. 37, 1775–1784 (PMLR, Proceedings of Machine Learning Research, 2015).
Bonilla, E. V., Chai, K. M. A. & Williams, C. K. I. Multi-task Gaussian Process prediction. in Proceedings of the 21st International Conference on Neural Information Processing Systems, 153–160 (Curran Associates Inc., Vancouver, British Columbia, Canada, 2007).
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat Methods 16, 687–694 (2019).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
Stark, H., Dallago, C., Heinzinger, M. & Rost, B. Light attention predicts protein location from the language of life. Bioinform Adv 1, vbab035 (2021).
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).
Kepp, K. P. Towards a "Golden Standard" for computing globin stability: Stability and structure sensitivity of myoglobin mutants. Biochim Biophys Acta 1854, 1239–1248 (2015).
Pires, D. E., Ascher, D. B. & Blundell, T. L. mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30, 335–342 (2014).
Xavier, J. S. et al. ThermoMutDB: a thermodynamic database for missense mutations. Nucleic Acids Res 49, D475–D479 (2021).
Bava, K. A., Gromiha, M. M., Uedaira, H., Kitajima, K. & Sarai, A. ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucleic Acids Res 32, D120–D121 (2004).
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).
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).
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).
Stourac, J. et al. FireProtDB: database of manually curated protein stability data. Nucleic Acids Res 49, D319–D324 (2021).
Chen, Y. et al. PremPS: Predicting the impact of missense mutations on protein stability. PLoS Comput Biol 16, e1008543 (2020).
Gal, Y. & Ghahramani, Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. in Proceedings of The 33rd International Conference on Machine Learning Vol. 48, 1050–1059 (PMLR, Proceedings of Machine Learning Research, 2016).
Nix, D. A. & Weigend, A. S. Estimating the mean and variance of the target probability distribution. in Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 55–60 (1994).
Kendall, A. & Gal, Y. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? in Advances in Neural Information Processing Systems, 5574–5584 (Curran Associates, Inc., 2017).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Yang, J., Li, F. Z. & Arnold, F. H. Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering. ACS Cent Sci 10, 226–241 (2024).
Yang, J. et al. Active learning-assisted directed evolution. Nat Commun 16, 714 (2025).
Wang, C., Angles, F. & Balch, W. E. Triangulating variation in the population to define mechanisms for precision management of genetic disease. Structure 30, 1190–1207 e1195 (2022).
Chen, S., Deng, Y., Zhang, X. & Wang, C. Mapping the polymerization landscape of human serpins across genetic variation using Gaussian Processes. LangTaoSha Preprint Server 10.65215/LTSpreprints.2026.08.25.000315 (2026).
Wang, S., Tang, H., Zhao, Y. & Zuo, L. BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification. Protein Sci 31, e4467 (2022).
Hipp, M. S. & Hartl, F. U. Interplay of Proteostasis Capacity and Protein Aggregation: Implications for Cellular Function and Disease. J Mol Biol 436, 168615 (2024).
Balch, W. E., Morimoto, R. I., Dillin, A. & Kelly, J. W. Adapting proteostasis for disease intervention. Science 319, 916–919 (2008).
Thompson, M. et al. Massive experimental quantification allows interpretable deep learning of protein aggregation. Science Advances 11, eadt5111 (2025).
Vanella, R. et al. Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing. Nature Communications 15, 1807 (2024).
Sun, S. et al. Spatial covariance reveals isothiocyanate natural products adjust redox stress to restore function in alpha-1-antitrypsin deficiency. Cell Rep Med 6, 101917 (2025).
Sun, S. et al. Capturing the conversion of the pathogenic alpha-1-antitrypsin fold by ATF6-enhanced proteostasis. Cell Chem Biol 30, 22–42 e25 (2023).
Dieckhaus, H. & Kuhlman, B. Protein stability models fail to capture epistatic interactions of double point mutations. Protein Sci 34, e70003 (2025).
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 Bioinformatics 12, 151 (2011).
Thiltgen, G. & Goldstein, R. A. Assessing predictors of changes in protein stability upon mutation using self-consistency. PLoS One 7, e46084 (2012).
Metrics
DOI:
Submission ID:
Downloads
Posted
How to Cite
Download Citation
Declaration of Competing Interests
Details of all competing interests to be disclosed are as follows:
Copyright
The copyright holder for this preprint is the author/funder.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.