Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition
Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition
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DOI：
10.3390/biom10030454
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发表时间：
2020-03-01
期刊：
影响因子：
5.5
通讯作者：
Kuhn, Leslie A.
中科院分区：
文献类型：
--
作者：
Bemister-Buffington, Joseph;Wolf, Alex J.;Kuhn, Leslie A.
我们的研究表明，机器学习可以精确定位蛋白质中区分非活性状态和活性状态的特征，特别是识别由生物活性配体触发的gpcr中关键配体结合位点灵活性转变。我们对18个无活性和9个活性的A类G蛋白偶联受体（gpcr）的螺旋片段和环进行了分析。这些三维（3D）结构是在配合体中确定的。然而，考虑到通过图论ProFlex刚度分析识别出的柔性和刚性状态，对去除配体的每个螺旋和环段进行刚度分析，然后进行特征选择和k近邻分类，足以识别出配体结合位点周围的四个片段，其灵活性/刚度准确地预测了GPCR是处于活性状态还是非活性状态。与抑制剂结合的gpcr在其柔性区和刚性区模式上相似，而与激动剂结合的gpcr更灵活和多样化。这种新的配体-近端GPCR活性柔韧性特征是在不知道配体结合模式或先前定义的开关区域的情况下确定的，同时与已知的传输开关相邻。在这一概念证明之后，结合模式识别和活性分类的ProFlex灵活性分析可能有助于预测新设计的配体在蛋白质家族中是作为激活剂还是抑制剂，基于它们在蛋白质中诱导的灵活性模式。
We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loops in 18 inactive and 9 active class A G protein-coupled receptors (GPCRs). These three-dimensional (3D) structures were determined in complex with ligands. However, considering the flexible versus rigid state identified by graph-theoretic ProFlex rigidity analysis for each helix and loop segment with the ligand removed, followed by feature selection and k-nearest neighbor classification, was sufficient to identify four segments surrounding the ligand binding site whose flexibility/rigidity accurately predicts whether a GPCR is in an active or inactive state. GPCRs bound to inhibitors were similar in their pattern of flexible versus rigid regions, whereas agonist-bound GPCRs were more flexible and diverse. This new ligand-proximal flexibility signature of GPCR activity was identified without knowledge of the ligand binding mode or previously defined switch regions, while being adjacent to the known transmission switch. Following this proof of concept, the ProFlex flexibility analysis coupled with pattern recognition and activity classification may be useful for predicting whether newly designed ligands behave as activators or inhibitors in protein families in general, based on the pattern of flexibility they induce in the protein.