Protein p K a Prediction with Machine Learning
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Abstract
Protein pKa prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pKa predictor DeepKa, which is trained and validated with the pKa values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang, Y.; et al.J. Chem. Inf. Model.2018, 58, 1372−1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pKa predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pKa predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein–ligand binding affinity prediction.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
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- Authors
- Cai, Z., Luo, F., Wang, Y., Li, E., & Huang, Y.
- Affiliations
- Jimei University (CN)
- Corresponding author
- Huang, Yandong
- Venue
- ACS Omega
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-12-07
- Volume
- 6
- Issue
- 50
- Pages
- 34823-34831
- ISSN
- 2470-1343 (ISSN-L)
- PMC
- PMC8697405
- OpenAlex
- W4205794662
- Cited by
- 45
- References
- 2
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 7 · 2023: 7 · 2022: 8 · 2021: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ACS Omega (publishedVersion, cc-by-nc-nd)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2018YFD0901004)
- National Natural Science Foundation of China (11804114)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein electrostatics, protein-ligand binding affinity prediction
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