Development of MUFOLD for Building High-Accuracy Protein Structure Models

开发用于建立高精度蛋白质结构模型的 MUFOLD

• 批准号: 8469528
• 负责人: DONG XU
• 金额: $ 26.94万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8469528
• 关键词: Address; Algorithms; Amino Acid Sequence; Benchmarking; Bioinformatics; Biological Process; Collaborations; Communities; Computational algorithm; Computing Methodologies; DNA Sequence Determination; Databases; Development; Disease; Drug Design; Drug Formulations; Evaluation; Experimental Designs; Funding Opportunities; Generations; Goals; Graph; Homologous Gene; Homologous Protein; Individual; Internet; Knowledge; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Mutation; Network-based; Peptide Sequence Determination; Population; Process; Protein Structure Initiative; Proteins; Protocols documentation; Research; Research Personnel; Resolution; Roentgen Rays; Side; Site; Software Tools; Solutions; Speed; Structural Models; Structure; System; Techniques; Tertiary Protein Structure; Testing; Time; Vertebral column; base; improved; interest; method development; molecular dynamics; next generation sequencing; novel; open source; population based; protein structure; protein structure function; protein structure prediction; research study; restraint; statistics; theories; tool

DESCRIPTION (provided by applicant): The long-term objective of the proposed project is to provide a comprehensive platform, MUFOLD, for efficient and consistently accurate protein tertiary structure prediction. MUFOLD will help experimental biologists understand structures and functions of the proteins of their interest thereby facilitating hypotheses for experimental design. We will focus on the Funding Opportunity Announcement's second objective -- "High- Accuracy Models for Remote Homologs of Known Structures" which states "the quality of these models should be close to X-ray structures or high-resolution NMR structures with less than 2 Angstrom RMSD for backbone and side-chain atoms consistently for all protein targets." Specifically, we will integrate bioinformatics techniques, graph and network theories, computational algorithms, global optimization methods, statistics evaluations, etc. to develop a template-based structure prediction system, in which model generation, model quality assessment (QA), and model refinement will be seamlessly integrated together. At first, we will apply relevant information from the known template database (PDB) in depth as well as multi-layer QA methods to guide an efficient model generation in a small and targeted conformation space, which will facilitate computational efficiency and a limited number of models for QA methods to select. Secondly, we will improve the overall discerning power of QA by integrating various QA scores of a model and its structural relationships to other models generated for the same target protein. Thirdly, we will develop a population-based model refinement protocol, which integrates different levels of QA and efficient model generation techniques to improve the overall quality of models. Our goals are 1) to improve the prediction speed such that the prediction for a target protein with 200~300 residues can be finished in minutes on a multi-core desktop machine; 2) to enhance the QA ability of selecting the best models from the generated candidates, and decrease the current average ~10-point GDT-TS loss from the best available model to <5 points; 3) to achieve the prediction accuracy for remote homolog proteins within 2 Angstrom RMSD for backbone and side-chain atoms on average; and 4) to collaborate with PSI (Protein Structure Initiative) and others for various applications, such as performing homolog modeling for proteins with sequence similarity to newly determined structures, building complete models for incomplete structures, and predicting potential mutation sites to make protein soluble.

描述（由申请人提供）：拟议项目的长期目标是提供一个全面的平台，MUFOLD，用于高效和一致准确的蛋白质三级结构预测。MUFOLD将帮助实验生物学家了解他们感兴趣的蛋白质的结构和功能，从而促进实验设计的假设。我们将专注于资助机会公告的第二个目标-“已知结构的远程同源物的高精度模型”，其中指出“这些模型的质量应该接近X射线结构或高分辨率NMR结构，对于所有蛋白质靶标，骨架和侧链原子的RMSD始终小于2埃。“具体来说，我们将整合生物信息学技术，图形和网络理论，计算算法，全局优化方法，统计评估等，以开发基于模板的结构预测系统，其中模型生成，模型质量评估（QA）和模型改进将无缝集成在一起。首先，我们将深入应用已知模板数据库（PDB）中的相关信息以及多层QA方法，以指导在小而有针对性的构象空间中高效生成模型，这将提高计算效率并限制QA方法选择的模型数量。其次，我们将通过整合模型的各种QA评分及其与针对同一靶蛋白生成的其他模型的结构关系来提高QA的整体识别能力。第三，我们将开发一个基于人口的模型细化协议，它集成了不同层次的QA和有效的模型生成技术，以提高模型的整体质量。 我们的目标是：1）提高预测速度，在多核桌面计算机上，对200~300个残基的目标蛋白质的预测可以在几分钟内完成; 2）增强QA从生成的候选模型中选择最佳模型的能力，将当前最佳模型的平均~10点GDT-TS损失降低到<5点;（3）对于骨架和侧链原子，预测精度平均在2埃RMSD以内;（4）与PSI合作（Protein Structure Initiative）和其他用于各种应用的方法，例如对与新确定的结构具有序列相似性的蛋白质进行同源建模，为不完整的结构构建完整的模型，并预测潜在的突变位点以使蛋白质可溶。