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Protein pKa Prediction by Tree-Based Machine Learning : Protein p<i>K</i><sub>a</sub> Prediction by Tree-Based Machine Learning

Cited 12 time in Web of Science Cited 12 time in Scopus

Chen, Ada Y.; Lee, Ju Yong; Damjanovic, Ana; Brooks, Bernard R.

Issue Date
American Chemical Society
Journal of Chemical Theory and Computation, Vol.18 No.4, pp.2673-2686
Protonation states of ionizable protein residuesmodulate many essential biological processes. For correct modelingand understanding of these processes, it is crucial to accuratelydetermine their pKavalues. Here, we present four tree-basedmachine learning models for protein pKaprediction. The fourmodels, Random Forest, Extra Trees, eXtreme Gradient Boosting(XGBoost), and Light Gradient Boosting Machine (LightGBM),were trained on three experimental PDB and pKadatasets, two ofwhich included a notable portion of internal residues. We observedsimilar performance among the four machine learning algorithms.The best model trained on the largest dataset performs 37% betterthan the widely used empirical pKaprediction tool PROPKA and15% better than the published result from the pKapredictionmethod DelPhiPKa. The overall root-mean-square error (RMSE) for this model is 0.69, with surface and buried RMSE values being0.56 and 0.78, respectively, considering six residue types (Asp, Glu, His, Lys, Cys, and Tyr), and 0.63 when considering Asp, Glu,His, and Lys only. We provide pKapredictions for proteins in human proteome from the AlphaFold Protein Structure Database andobserved that 1% of Asp/Glu/Lys residues have highly shifted pKavalues close to the physiological pH.
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산


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