Large-Scale Structure-Based Prediction and Identification of Novel Protease Substrates Using Computational Protein Design

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• 作者：Manasi A. Pethe¹ ; ² ; Aliza B. Rubenstein³ ; ⁴ ; Sagar D. Khare¹ ; ² ; ³ ; ⁴ ; ^{sagar.khare@rutgers.edu}
• 关键词：TEV ; tobacco etch virus protease ; HCV ; hepatitis C virus protease ; HIVPR ; human immunodeficiency virus protease 1 ; AUC ; area under curve ; ROC ; receiver operator curve ; SVM ; support vector machine ; YESS ; Yeast Endoplasmic Reticulum Sequestration and Screening ; TI ; tetrahedral intermediate
• 刊名：Journal of Molecular Biology
• 出版年：2017
• 出版时间：20 January 2017
• 年：2017
• 卷：429
• 期：2
• 页码：220-236
• 全文大小：2008 K
• 卷排序：429

文摘

Develop a general, structure-based approach for predicting protease substrate specificity using Rosetta and AMBER MMPBSA. Recapitulate known protease specificity profiles with accuracy comparable to sequence-only methods. Combining sequence and structure energy features using machine learning helps increase discrimination performance. Validated approach experimentally in yeast cells. Discovered novel sequence specificities for HCV NS3 4A protease using our computational approach.