Prediction of Crystallization Propensity of Proteins from <i>Bacillus haloduran</i>Using Various Amino Acid and Protein Features

作     者：Shaomin Yan Guang Wu 

作者机构：State Key Laboratory of Non-Food Biomass and Enzyme Technology Guangxi Key Laboratory of Bio-Refinery Guangxi Biomass Engineering Technology Research Center National Engineering Research Center for Non-Food Bio-Refinery Guangxi Academy of Sciences Guangxi China 

出 版 物：《Journal of Biomedical Science and Engineering》 (生物医学工程（英文）)

年 卷 期：2019年第12卷第12期

页      面：487-499页

学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 070303[理学-有机化学] 0703[理学-化学] 

主　　题：Protein Feature Bacillus haloduran Protein Crystallization 

摘      要：Correct prediction of propensity of crystallization of proteins is important for cost- and time-saving in determination of 3-demensional structures because one can focus to crystallize the proteins whose propensity is high through predictions instead of choosing proteins randomly. However, so far this job has yet to accomplish although huge efforts have been made over years, because it is still extremely hard to find an intrinsic feature in a protein to directly relate to the propensity of crystallization of the given protein. Despite of this difficulty, efforts are never stopped in testing of known features in amino acids and proteins versus the propensity of crystallization of proteins from various sources. In this study, the comparison of the features, which were developed by us, with the features from well-known resource for the prediction of propensity of crystallization of proteins from Bacillus haloduran was conducted. In particular, the propensity of crystallization of proteins is considered as a yes-no event, so 185 crystallized proteins and 270 uncrystallized proteins from B. haloduran were classified as yes-no events. Each of 540 amino-acid features including the features developed by us was coupled with these yes-no events using logistic regression and neural network. The results once again demonstrated that the predictions using the features developed by us are relatively better than the predictions using any of 540 amino-acid features.