ArticlePLoS computational biology2020
Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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Who cites it
77 citing papers in PubMed.
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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
- PythiaStudio: a one-stop protein engineering platform powered by Pythia model suite.Nucleic acids research · 2026Article
- OrgNet+: towards robust protein stability prediction with convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- Epistasis among clustered lineage-specific amino acid substitutions in the Drosophila Trio protein.PLoS genetics · 2026Article
- Alfalfa andPhysiology and molecular biology of plants : an international journal of functional plant biology · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Elevating RNA mNational science review · 2026Article
- Dormancy regulon reduction was pivotal to the evolution of Mycobacterium tuberculosis.Nature communications · 2026Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.bioRxiv : the preprint server for biology · 2026Article
- Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- Supervised Learning of Protein Melting Temperature: Cross-Species vs. Species-Specific Prediction.Proteins · 2025Article
- PROPERMAB: an integrative framework formAbs · 2025Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Epistasis among clustered lineage-specific adaptive amino acid substitutions in the Drosophila Trio protein.bioRxiv : the preprint server for biology · 2025Article
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
17 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.
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