AUTHOR=Huang Pin , Xing Haoming , Zou Xun , Han Qi , Liu Ke , Sun Xiangyan , Wu Junqiu , Fan Jie TITLE=Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding JOURNAL=Frontiers in Molecular Biosciences VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2021.756075 DOI=10.3389/fmolb.2021.756075 ISSN=2296-889X ABSTRACT=

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.