ArticleProceedings of the National Academy of Sciences of the United States of America2024
Transfer learning to leverage larger datasets for improved prediction of protein stability changes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
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111 citing papers in PubMed, 141 citations in OpenAlex.
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- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- The mutational landscape of STING-induced immunity.Nature · 2026Article
- Precision medicine for atherosclerotic cardiovascular disease: Integrative genomics maps risk loci and AI-predicted functional consequences.Clinical and translational medicine · 2026Article
- Allostery is a widespread cause of loss-of-function variant pathogenicity.Nature communications · 2026Article
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- Stabilizing Plasmodium falciparum proteins for small molecule drug discovery.Protein science : a publication of the Protein Society · 2026Article
- Disease associated missense substitutions disrupt structural stability and catalytic function of 4-hydroxyphenylpyruvate dioxygenase.The Journal of biological chemistry · 2026Article
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- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
51 more citing papers are in PubMed but not listed here.
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4 authors at 1 institution in 1 country.
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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.
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