Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning
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Abstract
ABSTRACT Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pre-trained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific p K a values, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction, and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for p K a prediction. Our GNN framework demonstrated robustness across diverse datasets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.
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- Authors
- Wozniak, S., Janson, G., & Feig, M.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Feig, Michael
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2025-04-24
- Volume
- 21
- Issue
- 9
- Pages
- 4830-4845
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC12080100
- OpenAlex
- W4409774501
- Cited by
- 14
- References
- 84
- Citations by year
- 2026: 11 · 2025: 3
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Theory and Computation (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Division of Molecular and Cellular Biosciences (MCB 2210228)
- National Institute of General Medical Sciences (R35 GM126948)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein property prediction, graph neural networks, transfer learning, protein embeddings, solvation free energy, diffusion coefficient, hydrodynamic radius, solvent accessible surface area, pKa prediction, intrinsically disordered peptides
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