FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution
FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution
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DOI：
10.1021/acs.jpcb.9b11869
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发表时间：
2020-05-07
影响因子：
3.3
通讯作者：
Shukla, Diwakar
中科院分区：
文献类型：
--
作者：
Feng, Jiangyan;Shukla, Diwakar
蛋白质是动态的分子，通过采用不同的三维结构来执行不同的分子功能。残基-残基接触预测的最新进展为从序列信息预测从头开始的蛋白质结构开辟了新的途径。然而，仅从残基-残基接触预测一种以上的构象仍然是困难的。这是由于无法去卷积残基-残基接触的复杂信号，即与蛋白质折叠、构象多样性和配体结合相关的空间接触。在这里，我们介绍了一种基于机器学习的方法，称为指印接触，用于扩展残基-残基接触的能力。该算法充分利用了残基-残基接触的特点，即(1)单个构象在结构预测中优于其他构象，以所有排名靠前的残基-残基接触为结构约束；(2)特定构象的接触排名较低，只占残基-残基接触的一小部分。我们展示了Fingerprint Contact在八种不同构象运动的配体结合蛋白上的能力。此外，Fingerprint接触识别优先位于动态波动区域的残基-残基接触的小簇。随着蛋白质序列信息的快速增长，我们预计Fingerprint Contact将成为了解蛋白质功能机制的结构方面强有力的第一步。
Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e., spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.