Protein p K a 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 p K a predictor DeepKa, which is trained and validated with the p K a 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. Huang Y. 29949356 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 p K a predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein p K a predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein–ligand binding affinity prediction.

No abstract

No abstract

No abstract

The electronic and optical properties of alkali-metal-adsorbed graphene-like gallium nitride (g-GaN) have been investigated using density functional theory. The results denote that alkali-metal-adsorbed g-GaN systems are stable compounds, with the most stable adsorption site being the center of the hexagonal ring. In addition, because of charge transfer from the alkali-metal atom to the host, the g-GaN layer shows clear n-type doping behavior. The adsorption of alkali metal atoms on g-GaN occurs via chemisorption. More importantly, the work function of g-GaN is substantially reduced following the adsorption of alkali-metal atoms. Specifically, the Cs-adsorbed g-GaN system shows an ultralow work function of 0.84 eV, which has great potential application in field-emission devices. In addition, the alkali-metal adsorption can lead to an increase in the static dielectric constant and extend the absorption spectrum of g-GaN.