A New Approach to Rapid Protein-Protein Docking

描述(申请人提供)：大分子复合体形成生命机器，与了解许多疾病，如癌症和代谢紊乱有关。对这些结构的了解不仅可以为这些复合体如何发挥作用提供机械描述，还可以为开发与疾病相关的治疗干预提供线索。对这些多组分生物分子复合体(“蛋白质-蛋白质对接”)的预测是发展这类知识的关键技术。我们建议在结构分子生物学家、应用数学家和计算机科学家之间进行独特的合作，以开发和优化新的算法，并将它们集成到灵活的对接工作流环境中。我们设想提高预测、可视化和分析蛋白质-蛋白质相互作用的速度、效率、通用性和灵活性。生成的软件应经过校准、验证，并向学术界免费提供。我们对现有方法的分析表明，通过利用和开发最先进的数学模型和算法，我们可以显着提高对蛋白质-蛋白质相互作用的预测。这些改进解决了当前对接方法中的各种限制，包括：1)适用于广泛的生物系统：所提出的分子形状的空间高效、多分辨率、体积表示可用于任何蛋白质拓扑，并且它显著增加了可计算的系统的大小；2)可扩展性：所提出的表示允许我们使用与我们的分子形状表示类似的方法来捕获分子属性，并且可扩展到灵活的蛋白质；3)空间和时间效率：我们的新表示自然地利用新的自适应不规则采样傅立叶计算来进行极快速的空间高效搜索和评分；4)软各向异性对接亲和力函数：我们对蛋白质-蛋白质相互作用的评分是基于在分子界面体积上定义的分析性对接亲和力函数，允许软对接以及例如对界面中的水和其他生物学意义方面进行建模的能力。

DESCRIPTION (provided by applicant): Macromolecular complexes form the machinery of life, and are relevant to understanding many diseases such as cancer and metabolic disorders. Knowledge of these structures can provide not only the mechanistic descriptions for how these complexes function but also clues in developing therapeutic interventions related to disease. Prediction of these multi-component biomolecular complexes ("protein-protein docking") is a key technology in developing such knowledge. We propose a unique collaboration between structural molecular biologists, applied mathematicians and computer scientists to develop and optimize novel algorithms and integrate them into a flexible docking-workflow environment. We envision improving the speed, efficiency, generality and flexibility of predicting, visualizing and analyzing protein-protein interactions. The resulting software shall be calibrated, validated and made freely available to the academic community. Our analysis of current approaches indicates that by utilizing and developing state-of-the-art mathematical models and algorithms we can significantly improve the prediction of protein-protein interactions. These improvements address a variety of limitations in current docking approaches, including: 1) Applicability to a wide range of biological systems: The proposed space-efficient, multi-resolution, volumetric representation of molecular shape is usable for any protein topology and it significantly increases the size of systems that can be computed; 2) Extensibility: The proposed representation allows us to capture molecular properties using methods similar to our representation of molecular shape and is extensible to flexible proteins; 3)Space and Time Efficiency: Our novel representation lends itself naturally to extremely rapid space efficient search and scoring utilizing a novel adaptive irregularly sampled Fourier calculation; 4) Soft Anisotropic Docking Affinity Functions: Our scoring of protein-protein interactions is based on analytic docking affinity functions defined on molecular interface volumes allowing for both soft docking as well as the ability, for instance, to model water in the interface and other aspects of biological significance.