Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.
J Chem Inf Model. 2023 Feb 13;63(3):1058-1065. doi: 10.1021/acs.jcim.2c01235. Epub 2023 Jan 24.
Solvent accessibility has been extensively used to characterize and predict the chemical properties of the surface residues of soluble proteins. However, there is not yet a widely accepted quantity of the same dimension for the study of lipid-accessible residues of membrane proteins. In this study, we propose that , defined in a similar way to solvent accessibility, can be used to characterize the lipid-accessible residues of membrane proteins. Moreover, we developed a deep learning-based method, ProtRAP (Protein Relative Accessibility Predictor), to predict the relative lipid accessibility and relative solvent accessibility of residues from a given protein sequence, which can infer which residues are likely accessible to lipids, accessible to solvent, or buried in the protein interior in one run.
溶剂可及性已被广泛用于描述和预测可溶性蛋白质表面残基的化学性质。然而，目前还没有一个广泛接受的相同维度的量来研究膜蛋白的脂溶性残基。在这项研究中，我们提出，以类似于溶剂可及性的方式定义，可以用来描述膜蛋白的脂溶性残基。此外，我们开发了一种基于深度学习的方法 ProtRAP（蛋白质相对可及性预测器），可以从给定的蛋白质序列中预测残基的相对脂溶性和相对溶剂可及性，一次运行即可推断出哪些残基可能与脂质、溶剂或埋藏在蛋白质内部。
