Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations
Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations
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DOI：
10.1073/pnas.1805256116
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发表时间：
2019-01-02
影响因子：
11.1
通讯作者：
Khare, Sagar D.
中科院分区：
文献类型：
--
作者：
Pethe, Manasi A.;Rubenstein, Aliza B.;Khare, Sagar D.
蛋白质和肽之间的生物物理相互作用是分子识别特异性景观的关键决定因素。然而，如何在蛋白质-肽界面形状这些景观的分子结构和residuelevel能量的理解仍然难以捉摸。我们结合联合收割机的信息，从酵母为基础的文库筛选，下一代测序，并在监督机器学习方法的结构为基础的建模报告全面的序列能量学功能映射的特异性景观的丙型肝炎病毒（HCV）NS 3/4A蛋白酶，其功能位点特异性裂解的病毒多聚蛋白是一个关键的决定因素的病毒健身。我们筛选了一个底物文库，其中5个残基位置被随机化，并使用酵母展示和荧光激活细胞分选，然后进行深度测序，测量了类似于30，000个底物（类似于文库的1%）的可切割性。在监督学习程序中使用实验衍生序列子集的基于结构的模型来训练支持向量机，以预测HCV蛋白酶对320万种底物变体的可切割性。由此产生的景观允许识别以前未识别的HCV蛋白酶底物，图论分析揭示了序列空间中可切割和不可切割基序的广泛聚类。已知耐药变异体的特异性景观类似地聚类。所描述的方法应该能够阐明和重新设计的特异性景观的各种蛋白酶，包括人类起源的酶。我们的研究结果还表明，残留水平的能量在塑造高原状的功能景观预测从病毒准种理论的可能作用。
Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residuelevel energetics at protein-peptide interfaces shape these landscapes remains elusive. We combine information from yeast-based library screening, next-generation sequencing, and structure-based modeling in a supervised machine learning approach to report the comprehensive sequence-energetics-function mapping of the specificity landscape of the hepatitis C virus (HCV) NS3/4A protease, whose function-site-specific cleavages of the viral polyprotein-is a key determinant of viral fitness. We screened a library of substrates in which five residue positions were randomized and measured cleavability of similar to 30,000 substrates (similar to 1% of the library) using yeast display and fluorescence-activated cell sorting followed by deep sequencing. Structure-based models of a subset of experimentally derived sequences were used in a supervised learning procedure to train a support vector machine to predict the cleavability of 3.2 million substrate variants by the HCV protease. The resulting landscape allows identification of previously unidentified HCV protease substrates, and graph-theoretic analyses reveal extensive clustering of cleavable and uncleavable motifs in sequence space. Specificity landscapes of known drug-resistant variants are similarly clustered. The described approach should enable the elucidation and redesign of specificity landscapes of a wide variety of proteases, including human-origin enzymes. Our results also suggest a possible role for residue-level energetics in shaping plateau-like functional landscapes predicted from viral quasispecies theory.