Protein pKa prediction with machine learning
Open accessPreprint — published as 10.1021/acsomega.1c05440 · Paper page
Abstract
Protein pKa prediction is essential for the investigation of 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, Harris, and Shen 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 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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Protein p <i>K</i> <sub>a</sub> Prediction by Tree-Based Machine Learning
Chen, A. Y., Lee, J., Damjanovic, A., & Brooks, B. R. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01257 · Cited by 37 · see citing works
- Authors
- Cai, Z., Luo, F., Wang, Y., Li, E., & Huang, Y.
- Affiliations
- Jimei University (CN)
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-10-03
- OpenAlex
- W3190585344
- Cited by
- 2
- References
- 2
- Citations by year
- 2022: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Funders
- National Natural Science Foundation of China (11804114)
- National Key R&D Program of China (2018YFD0901004)
- 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, machine learning predictors
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