智能计算与弹性网络模型相结合的蛋白质功能预测与分析

揭示生物体内成千上万种蛋白质的功能机制是蛋白质科学研究的核心内容之一。目前，蛋白质功能研究方法主要包括实验手段、物理模型和机器学习三大类，但各具优缺点。其中，实验手段能够提供真实可靠的数据，物理模型中的弹性网络模型能够提供丰富的柔性动力学知识，而机器学习方法具有成本低、高效、数据处理规模大等特点。优势整合各类方法有助于完整揭示蛋白质的功能机制。. 本项目计划选择几种重要的功能蛋白或功能位点作为研究对象，从蛋白质的序列或结构出发，混合利用多种智能计算方法，结合弹性网络模型的输出结果，构建更加精确的功能蛋白或功能位点预测模型，挖掘各类功能显著性相关的结构性与柔性动力学新特征，并计划建立起各种预测与计算分析模型的web服务，为蛋白质的“序列-结构-柔性-功能”新范式研究提供理论基础，为药物设计、蛋白质结构预测、分子柔性对接等重大研究课题所服务。

Revealing functional mechanisms of thousands of proteins in living organisms is one of the key tasks in area of protein science. At present, research methods on protein functions can be mainly categorized into three groups: experimental techniques, physics models and machine learning. Each group has its own advantages and disadvantages. In brief, the experimental techniques mean to provide reliable data, physics models such as elastic network models can provide knowledge of protein flexibility and dynamics, and machine learning methods have the advantages of low cost, high performance and ability to process large-scale data. Integrating advantages of these method helps to completely reveal the mechanisms of protein functions.. This project is planned to collect several kinds of important functional proteins or function sites, and analyze and predict their functions from sequence or structure by using mixed intelligent computation methods and by combining outputs of elastic network models. The goal of the project is to build more precise models for predictions of protein functions or function sites, to mine various function-specific characteristics in ptrotein structures and dynamics, and to construct web services for the proposed prediction and computational models. The knowledge output via this project can provide theoretical basis for the new paradigm "sequence-structure-flexibility-function" in area of protein science. It will be also helpful for several important subjects including drug design, prediction of protein structure and flexible docking.