ABI Development: Refinement Algorithms and Server for Protein Docking

蛋白质之间的相互作用几乎是所有生物途径所不可或缺的。预测这些相互作用和蛋白质复合体的功能是理解生物途径如何发挥作用的关键。详细的多阶段对接算法从两个蛋白质的未结合结构开始，可以确定蛋白质复合体的结构。对接服务器ClusPro致力于让研究人员能够访问这些对接算法。然而，当前的精化阶段对计算要求太高，不适合在在线服务器中使用，因此被简单的能量最小化所取代。ClusPro团队将开发在传统刚体对接算法中执行侧链搜索的方法，并通过随机路线图模拟计算每个能量漏斗的逃逸时间。这些方法将提供更有效的改进，并将有助于识别接近本地的模型，从而提高预测的可靠性和准确性。该服务器将在多个平台上实施，包括超级计算机和多核台式机。ClusPro已经拥有超过4500名用户，每月运行超过1000个作业。2011年，164篇论文使用服务器生成的模型来研究生物学、生物化学和生物技术中的问题。升级后的服务器具有简单的用户界面，对于没有丰富计算经验的实验者来说特别有用，他们将能够使用它来生成蛋白质相互作用的模型来解释他们的数据。研究生将接受培训，以便将高性能的结构预测算法与各种低分辨率或非结构技术的实验数据最佳地结合起来。对接方法也被纳入本科课程，教授分子识别的生物物理原理。

Protein-protein interactions are integral to virtually all biological pathways. Predicting these interactions and the function of the protein complex in key to understanding how biological pathways function. Detailed multistage docking algorithms, which starts from the unbound structures of two proteins, can determine the structure of the protein complex. The docking server, ClusPro, strives to make these docking algorithms accessible to researchers. However, the current refinement stage is computationally too demanding for use in an online server, and hence is replaced by simple energy minimization. The ClusPro team will develop methods to perform side chain search within a traditionally rigid body docking algorithm, and to calculate escape times from each energy funnel by stochastic roadmap simulations. These methods will provide more efficient refinement and will help to identify near-native models, thereby improving the reliability and accuracy of predictions. The server will be implemented on a number of platforms, including supercomputers and multi-core desktops.ClusPro already has over 4500 users and runs over 1000 jobs per month. In 2011, 164 papers used models generated using the server to study problems in biology, biochemistry, and biotechnology. The upgraded server, with its simple user interface, will be particularly useful to experimentalist with no extensive computational experience, who will be able use it for generating models of protein interactions to explain their data. Graduate students will be trained to optimally combine high performance structure prediction algorithms with experimental data from a variety of low-resolution or non-structural techniques. Docking methods are also being incorporated into undergraduate courses to teach the biophysical principles of molecular recognition.