ME-p K a : A Deep Learning Method with Multimodal Learning for Protein p K a Prediction

Proteins are crucial in biological processes and are important substances that mediate biochemical reactions, regulate cellular processes, and facilitate drug binding through their active sites and surface residues. The pK_a values of proteins determine the protonation state of ionizable amino acids under specific pH conditions, profoundly impacting protein structure, function, and drug design. However, experimental determination of pK_a values is normally laborious and complex. Moreover, existing prediction methods are limited by the data quantity and quality, as well as their inability to address the intricate structural and physicochemical attributes of proteins, thereby hindering accuracy and generalization, especially in predicting pK_a values for buried residues. In this study, we developed a multimodal protein pK_a prediction model named ME-pK_a (Multimodal ESM pK_a), which leverages the multimodal information and employs a multifidelity learning strategy to speedily and accurately predict molecular pK_a values. The ME-pK_a method facilitates data augmentation by integrating the local environmental attributes of amino acids with the FASTA sequence characteristics of proteins. Furthermore, the incorporation of multifidelity learning has addressed the challenge of limited data availability to some extent. Our ME-pK_a model outperforms several state-of-the-art models in predicting protein pK_a values, achieving impressive results with a low RMSE of 0.845 ± 0.09 and MAE of 0.641 ± 0.07, a high R² of 0.921 ± 0.02, and R of 0.959 ± 0.01 on the PE-pK_a data set. Notably, ME-pK_a demonstrated balanced and robust performance across major ionizable residue types (ASP, GLU, HIS, LYS). It demonstrates superior accuracy in predicting pK_a values for buried residues (RSA < 0.2), achieving the lowest MAE values of 0.921 ± 0.05 on the PE-pK_a data set and 0.911 ± 0.06 on the Small Set data set, which collectively excel in capturing complex environmental influences on pK_a. Moreover, our method confirmed pH-dependent binding of PD-L1 antibodies mediated by the protonation state of His-69 in PD-L1, emphasizing the critical role of amino acid protonation states in drug design. The source code of ME-pK_a can be found at https://github.com/yzjyg215/ME-pKa.