refineD：基于机器学习的约束松弛改进蛋白质结构精修。

refineD: improved protein structure refinement using machine learning based restrained relaxation.

机构信息

Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA.

出版信息

Bioinformatics. 2019 Sep 15;35(18):3320-3328. doi: 10.1093/bioinformatics/btz101.

DOI:10.1093/bioinformatics/btz101

PMID:30759180

Abstract

MOTIVATION

Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.

RESULTS

Here, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement. We use four restraint resolutions as adopted in GDT-HA (0.5, 1, 2 and 4 Å), centered on the Cα atom of each residue that are predicted by ensemble of four deep discriminative classifiers trained using combinations of sequence and structure-derived features as well as several energy terms from Rosetta centroid scoring function. The proposed method, refineD, has been found to produce consistent and substantial structural refinement through the use of cumulative and non-cumulative restraints on 150 benchmarking targets. refineD outperforms unrestrained relaxation strategy or relaxation that is restrained to starting structures using the FastRelax application of Rosetta or atomic-level energy minimization based ModRefiner method as well as molecular dynamics (MD) simulation based FG-MD protocol. Furthermore, by adjusting restraint resolutions, the method addresses the tradeoff that exists between degree and consistency of refinement. These results demonstrate a promising new avenue for improving accuracy of template-based protein models by effectively guiding conformational sampling during structure refinement through the use of machine learning based restraints.

AVAILABILITY AND IMPLEMENTATION

http://watson.cse.eng.auburn.edu/refineD/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

蛋白质结构精修的目的是通过构象采样，使中等精度的基于模板的蛋白质模型更接近天然状态。然而，通过有效利用约束来引导采样向天然状态仍然是结构精修中的一个主要问题。

结果

在这里，我们开发了一种基于机器学习的受约束松弛协议，该协议使用基于深度判别学习的二进制分类器，从起始结构预测多分辨率概率约束，然后将这些约束转换为在结构精修过程中集成到 Rosetta 全原子能量函数中的附加评分项。我们使用了 GDT-HA（0.5、1、2 和 4Å）中采用的四种约束分辨率，以每个残基的 Cα原子为中心，这些分辨率是通过使用来自 Rosetta 质心评分函数的序列和结构衍生特征以及几个能量项的组合训练的四个深度判别分类器的集合来预测的。在所评估的 150 个基准目标中，该方法 refineD 通过使用累积和非累积约束，产生了一致和显著的结构精修效果。refineD 优于无约束松弛策略或使用 Rosetta 的 FastRelax 应用程序或基于原子水平能量最小化的 ModRefiner 方法约束到起始结构的松弛，也优于基于分子动力学（MD）模拟的 FG-MD 协议。此外，通过调整约束分辨率，可以解决在精修程度和一致性之间存在的权衡问题。这些结果表明，通过在结构精修过程中使用基于机器学习的约束有效地引导构象采样，为提高基于模板的蛋白质模型的准确性提供了一种有前途的新途径。