LAAS-CNRS, Université de Toulouse, CNRS, Toulouse 31400, France.
Sanofi Recherche & Développement, Integrated Drug Discovery, Molecular Design Sciences, Vitry-sur-Seine Cedex 94403, France.
Bioinformatics. 2020 Feb 15;36(4):1099-1106. doi: 10.1093/bioinformatics/btz684.
Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation.
In this paper, we propose a method to exhaustively sample the conformational space of protein loops. It exploits structural information encoded in a large library of three-residue fragments, and enforces loop-closure using a closed-form inverse kinematics solver. A novel reinforcement-learning-based approach is applied to accelerate sampling while preserving diversity. The performance of our method is showcased on benchmark datasets involving 9-, 12- and 15-residue loops. In addition, more detailed results presented for streptavidin illustrate the ability of the method to exhaustively sample the conformational space of loops presenting several meta-stable conformations.
We are developing a software package called MoMA (for Molecular Motion Algorithms), which includes modeling tools and algorithms to sample conformations and transition paths of biomolecules, including the application described in this work. The binaries can be provided upon request and a web application will also be implemented in the short future.
Supplementary data are available at Bioinformatics online.
蛋白质中的环部分参与许多分子相互作用过程。它们通常表现出高度的灵活性，这对于它们的功能可能是必不可少的。然而，分子建模方法通常使用单个构象来表示环。尽管这种构象可能对应于（亚）稳定状态，但它并不总是提供现实的表示。
在本文中，我们提出了一种方法来彻底采样蛋白质环的构象空间。它利用了在大型三残基片段库中编码的结构信息，并使用闭式逆运动学求解器强制环闭合。应用了一种新的基于强化学习的方法来加速采样，同时保持多样性。我们的方法在涉及 9、12 和 15 残基环的基准数据集上展示了性能。此外，针对链霉亲和素呈现的几个亚稳定构象的详细结果表明了该方法能够彻底采样环的构象空间的能力。
我们正在开发一个名为 MoMA（用于分子运动算法）的软件包，其中包括用于采样生物分子构象和过渡路径的建模工具和算法，包括本文所述的应用。可以根据要求提供二进制文件，并将在不久的将来实现一个网络应用程序。
补充数据可在 Bioinformatics 在线获得。
