Protein Structure Prediction in CASP13 Using AWSEM-Suite
Protein Structure Prediction in CASP13 Using AWSEM-Suite
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DOI：
10.1021/acs.jctc.0c00188
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发表时间：
2020-06-09
影响因子：
5.5
通讯作者：
Wolynes, Peter G.
中科院分区：
文献类型：
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作者：
Jin, Shikai;Chen, Mingchen;Wolynes, Peter G.
最近出现了几种显著提高蛋白质三级结构预测质量的技术。在这项研究中，我们描述了AWSEM- suite的性能，这是一种将基于模板的建模和协同进化约束与现实的粗粒度力场AWSEM相结合的算法。AWSEM植根于神经网络，包含了物理和生物信息能量，并利用能量景观理论进行了优化。AWSEM-Suite作为服务器预测器参与CASP13，并对大多数靶标产生可靠的预测。AWSEM-Suite在自由建模类别和难以建模类别中均排名第八，并且在一个案例中提供了最好的提交预测。在这里，我们使用CASP13中不同类别的几个示例批判性地讨论了AWSEM-Suite的预测性能。对这些选择的目标（其中两个是难以建模的目标）进行的结构预测测试表明，即使在同源性较弱的情况下，AWSEM-Suite在结合模板引导和共同进化约束后也可以实现高分辨率的结构预测。对于具有可靠模板的目标（易于模板的类别），引入共同进化约束有时会损害预测的总体质量。然而，自由能剖面分析表明，这两个进化信息术语的结合有效地增加了景观向原生结构的漏斗，同时仍然允许足够的灵活性来纠正正确的目标结构与提供的指导之间的差异。与其他专门面向结构预测的预测器相比，AWSEM-Suite与统计力学基础以及相关的分子动力学和重要采样模拟的联系使其适合于功能探索。
Recently several techniques have emerged that significantly enhance the quality of predictions of protein tertiary structures. In this study, we describe the performance of AWSEM-Suite, an algorithm that incorporates template-based modeling and coevolutionary restraints with a realistic coarse-grained force field, AWSEM. With its roots in neural networks, AWSEM contains both physical and bioinformatical energies that have been optimized using energy landscape theory. AWSEM-Suite participated in CASP13 as a server predictor and generated reliable predictions for most targets. AWSEM-Suite ranked eighth in both the freemodeling category and the hard-to-model category and in one case provided the best submitted prediction. Here we critically discuss the prediction performance of AWSEM-Suite using several examples from different categories in CASP13. Structure prediction tests on these selected targets, two of them being hard-to-model targets, show that AWSEM-Suite can achieve high-resolution structure prediction after incorporating both template guidances and coevolutionary restraints even when homology is weak. For targets with reliable templates (template-easy category), introducing coevolutionary restraints sometimes damages the overall quality of the predictions. Free energy profile analyses demonstrate, however, that the incorporations of both of these evolutionarily informed terms effectively increase the funneling of the landscape toward native-like structures while still allowing sufficient flexibility to correct for discrepancies between the correct target structure and the provided guidance. In contrast to other predictors that are exclusively oriented toward structure prediction, the connection of AWSEM-Suite to a statistical mechanical basis and affiliated molecular dynamics and importance sampling simulations makes it suitable for functional explorations.