Interaction Pattern Based Predictor of Protein Structure

描述（由申请人提供）：本项目的长期目标是开发一种基于结构的蛋白质分子功能预测方法，以便更充分地利用基因组测序和结构基因组学提供的信息。为了实现这一总体目标，该提案进一步开发了一种非常有前途的和紧密整合的序列-结构-功能方法，该方法采用蛋白质结构来预测蛋白质-蛋白质相互作用、蛋白质分子功能和配体结合位点。它也为改进配体筛选提供了相当大的希望。具体而言，提出了以下具体目标：（1）将扩展和改进基于单体序列轮廓的线程算法，该算法目前无法在PDB中为约25%的单结构域蛋白质找到良好的模板结构，这些单结构域蛋白质与已解决的蛋白质结构具有非常低的序列同一性。(2)一个纯粹的基于结构的线程版本将被开发，因为最好的当代线程算法具有强大的进化组件，当目标和模板蛋白质在进化上遥远或具有相似的结构时，限制了它们的结构识别能力。在这方面，适用于基于结构的线程的平均力的潜力将来自一个新的琥珀色相关的，基于物理的原子势，显示出显着的能力，以改善结构更接近本地。(3)多聚体结构预测算法m-TASSER将通过提高界面侧链接触预测的准确性和使用基于物理的界面势进行结构细化来增强。此外，通过利用单结构域蛋白质结构的文库可能是完整的这一事实，全对全对接将提供对单结构域蛋白质的可能二聚体复合物的数量的估计。(4)对基于FINDSITE结构的蛋白质分子功能预测算法进行了扩展和改进。包括增强其配体筛选能力的基础上的见解，对于进化上遥远的蛋白质，有保守的锚区的蛋白质结合位点和2个结合的配体，可以利用快速配体结合姿势预测和筛选。(5)EFICAz是一种精确的酶功能推断方法，将与FINDSITE相结合，以开发更强大的配体筛选方法。(6)在目标1-5中开发的整套工具将应用于所有测序的蛋白质组，并将由此产生的序列-结构-功能（SF）数据库提供给学术界。全蛋白质组结构预测将与EFICAz和FINDSITE相结合，以识别小调节分子的可能受体，包括抗癌代谢物的靶点，并提供全蛋白质组筛选的配体库，蛋白质-蛋白质相互作用库，四级结构和分子功能注释。在所有情况下，将进行大规模、仔细的基准测试。因此，该项目有望对广泛的生物学重要问题产生重大影响。 公共卫生相关性：本计画中所描述的紧密整合的蛋白质序列-结构-功能方法的发展与整个蛋白质组的应用，将对广大的研究人员有实用价值。通过在药物发现的早期阶段提供帮助，所提出的算法可能具有显著的治疗效用。此外，人类相互作用组中估计的650，000种蛋白质-蛋白质相互作用中的大多数是未知的;通过提供预测的蛋白质四级结构，将深入了解这些蛋白质如何执行其功能。

DESCRIPTION (provided by applicant): The long-term goal of this project is to develop a structure-based approach for the prediction of protein molecular function so that the information provided by both genome sequencing and structural genomics can be more fully exploited. To achieve this overall objective, this proposal further develops a very promising and tightly integrated, sequence-to-structure-to-function approach that employs protein structure to predict protein- protein interactions, protein molecular function, and ligand binding sites. It also holds considerable promise for improved ligand screening. In particular, the following Specific Aims are proposed: (1) Monomeric sequence profile-based threading algorithms, which currently fail to find the good template structures in the PDB for the ~25% of single domain proteins with very low sequence identity to solved protein structures, will be extended and improved. (2) A purely structure-based version of threading will be developed, as the best contemporary threading algorithms have a strong evolutionary component that limits their structure recognition ability when the target and template proteins are evolutionarily distant or have analogous structures. In that regard, potentials of mean force suitable for structure-based threading will be derived from a new AMBER-related, physics-based atomic potential that shows significant ability to refine structures closer to native. (3) The multimeric structure prediction algorithm, m-TASSER, will be enhanced by improving the accuracy of interfacial side chain contact predictions and the use of physics-based interfacial potentials for structure refinement. In addition, by exploiting the fact that the library of single domain protein structures is likely complete, all-against-all docking will provide an estimate of the number of possible dimer complexes of single domain proteins. (4) The FINDSITE structure-based protein molecular function prediction algorithm will be extended and improved. Included are enhancements of its ligand screening ability based on the insight that for evolutionarily distant proteins, there are conserved anchor regions in both the protein binding site and in the 2 bound ligands that can be exploited for rapid ligand binding pose prediction and screening. (5) EFICAz , a precise enzyme function inference approach, will be combined with FINDSITE to develop a more powerful ligand screening approach. (6) The entire set of tools developed in Aims 1-5 will be applied to all sequenced 2 proteomes and the resulting sequence-to-structure-to-function, S F, database made available to the academic 2 community. Whole proteome structure predictions will be combined with EFICAz and FINDSITE to identify possible receptors of small regulatory molecules including the targets of anticancer metabolites, and to provide whole proteome screened ligand libraries, libraries of protein-protein interactions, quaternary structures and molecular functional annotations. In all cases, large scale, careful benchmarking will be done. Thus, this project holds the promise of making a significant impact across a wide spectrum of biologically important problems. PUBLIC HEALTH RELEVANCE: The development and whole proteome application of the tightly integrated, protein sequence-to-structure- function approach described in this project will be of utility to a broad spectrum of researchers. By assisting in the early stages of drug discovery, the proposed algorithms could have significant therapeutic utility. Also, most of the estimated 650,000 protein-protein interactions in the human interactome are unknown; by providing predicted protein quaternary structures, insights into how these proteins perform their function will result.