Structure prediction of cyclic peptides by molecular dynamics + machine learning.
Structure prediction of cyclic peptides by molecular dynamics + machine learning.
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DOI：
10.1039/d1sc05562c
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发表时间：
2021-11-17
期刊：
影响因子：
8.4
通讯作者：
Lin YS
中科院分区：
文献类型：
--
作者：
Miao J;Descoteaux ML;Lin YS
最近的计算方法在发现结构良好的环肽方面取得了长足的进步，这些环肽优先占据单一构象。然而，许多成功的环肽治疗剂在溶液中采用多种构象。事实上，一些环肽的变色龙特性可能是它们的高细胞膜渗透性的原因。因此，我们需要能够预测环肽的完整结构系综，包括大多数具有广泛结构系综的环肽，以显着提高我们合理设计环肽治疗剂的能力。在这里，我们介绍了使用分子动力学模拟结果来训练机器学习模型的想法，以实现对环肽的有效结构预测。使用数百个环状五肽的分子动力学模拟结果作为训练数据集，我们开发了机器学习模型，可以为整个序列空间中的所有数十万个序列提供分子动力学模拟质量的结构系综预测。每个单独的环肽的预测可以使用小于1秒的计算时间进行。即使是最具挑战性的类结构不良的环肽与广泛的构象合奏，我们的预测是类似的，通常只有在运行多天的显式溶剂分子动力学模拟后才能获得。由此产生的方法，称为StrEAMM（通过分子动力学和机器学习实现的结构集合），是第一种能够有效预测环肽完整结构集合的技术，而不依赖于额外的分子动力学模拟，构成了七个数量级的速度提高，同时保持与显式溶剂模拟相同的准确性。StrEAMM方法能够预测溶液中采用多种构象的环肽的结构集合。
Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations. The StrEAMM method enables predicting the structural ensembles of cyclic peptides that adopt multiple conformations in solution.