PROTEIN-PROTEIN DOCKING USING LOCAL SHAPE INVARIANTS

使用局部形状不变量进行蛋白质-蛋白质对接

• 批准号: 8171888
• 负责人: Daisuke Kihara
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171888
• 关键词: Accounting; Adopted; Algorithms; Bioinformatics; Biological Process; Complex; Computer Retrieval of Information on Scientific Projects Database; Descriptor; Development; Docking; Funding; Grant; Institution; Modeling; Molecular Conformation; Neighborhoods; Noise; Process; Property; Proteins; Research; Research Personnel; Resources; Rotation; Sampling; Shapes; Source; Structure; Time; United States National Institutes of Health; design; flexibility; interest; molecular dynamics; protein folding; protein protein interaction; protein structure; three dimensional structure

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The aim of the project is to design and develop algorithms to predict protein-protein interactions using docking. The structure of the docked complex is useful in elucidating the biological function of the protein. Since solving a protein structure experimentally is time consuming and technically challenging, development of computational docking has become an urgent task in protein bioinformatics. The starting point in this approach are the crystallized structures of the interacting proteins which are treated as rigid, and the conformational space generated by the two interacting proteins is explored extensively. To help guide the search, a set of moment invariants are used. The underlying idea is to capture local shape properties defined over a small neighborhood of a set of interest points, in terms of a set of numbers called Zernike moments. These local descriptors are invariant to rotation transform and robust to noise. The rotational and translational space (6D space) is sampled using a geometric hashing algorithm. The predicted models are then ranked using a suitable energy function. This step is followed by the subsequent refinement of the top ranking models using molecular dynamics simulations. Proteins are inherently flexible which means that its 3-D structure may change under different conditions. Accounting for this is very challenging as in addition to the sampling of the rigid-body orientations, due consideration needs to be given to the folding of the protein. In order to avoid the heavily time consuming search through the entire flexible conformational space of two proteins during the docking or refinement process, an ensemble approach is adopted wherein, a pre-generated set of different feasible conformations are cross-docked.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 该项目的目的是设计和开发使用对接预测蛋白质-蛋白质相互作用的算法。对接复合物的结构有助于阐明蛋白质的生物学功能。由于蛋白质结构的实验求解是一项耗时且技术上具有挑战性的工作，因此开展计算对接已成为蛋白质生物信息学中的一项紧迫任务。这种方法的出发点是相互作用蛋白质的结晶结构，它们被视为刚性的，并广泛探索了由两种相互作用蛋白质产生的构象空间。为了帮助指导搜索，使用了一组不变矩。其基本思想是捕捉局部形状属性定义在一组兴趣点的一个小邻域，在一组数字称为泽尔尼克矩。这些局部描述符对旋转变换具有不变性，对噪声具有鲁棒性。使用几何哈希算法对旋转和平移空间（6D空间）进行采样。然后使用合适的能量函数对预测模型进行排名。该步骤之后是使用分子动力学模拟对排名靠前的模型进行后续细化。蛋白质具有内在的灵活性，这意味着它的三维结构可能会在不同的条件下发生变化。考虑到这一点是非常具有挑战性的，因为除了刚体方向的采样之外，还需要适当考虑蛋白质的折叠。为了避免在对接或细化过程中通过两个蛋白质的整个柔性构象空间进行大量耗时的搜索，采用了一种集成方法，其中，预先生成的一组不同的可行构象被交叉对接。