New Computational Methods for Data-driven Protein Structure Prediction

描述（由申请人提供）：蛋白质在所有生物过程中发挥核心作用。类似于基因组的完整测序，蛋白质结构的完整描述是理解生物生命的基本步骤，并且在治疗和药物的开发中也与医学高度相关。该项目的广泛，长期目标是通过两个独立但互补的策略开发用于数据驱动的蛋白质结构预测的机器学习方法：1）蛋白质数据库中具有远程同源物的蛋白质的更准确的基于模板的建模，以及2）更好的无模板建模方法对于没有可检测模板的蛋白质和改进基于模板的模型。具体目标是：目的1）通过1a）使用基于回归树的非线性评分函数改进蛋白质序列-模板比对，特别是当良好的序列概况不可用时;和1b）使用机器学习方法改进折叠识别以联合收割机组合残基水平和原子水平特征两者，来极大地改进基于模板的建模;目的2）通过三种独立但互补的方法改进连续空间中的蛋白质构象采样，从而改进无模板建模：2a）使用条件（马尔可夫）随机场（CRF）模型对非线性序列-结构关系建模; 2b）同时采样二级和三级结构;以及2c）从模板学习结构信息。该项目的核心是通过从现有的序列/结构数据库中学习蛋白质序列-结构关系，开发用于数据驱动的蛋白质结构预测的各种CRF模型。本研究的产品包括一个基于回归树的CRF模型，用于精确的蛋白质比对，特别是对于PDB中没有紧密同源物或没有非常好的序列谱的蛋白质;用于蛋白质折叠识别的SVM模型;用于连续空间中有效蛋白质构象采样的几个CRF模型;和一个完整的蛋白质结构预测软件包。此外，它还将建立一个网络服务器，供各种学术和生物医学用户公开使用。蛋白质结构预测将导致广泛的生物医学应用，例如开发新的诊断方法，更好地了解疾病过程和改善预防性治疗，从而降低医疗保健成本。蛋白质建模也广泛应用于制药行业，并融入制药研究的大多数阶段。 公共卫生相关性： 新的蛋白质结构预测将导致广泛的生物医学应用，例如开发新的诊断方法，更好地了解疾病过程和改善预防性治疗，从而降低医疗保健成本。蛋白质建模也广泛应用于制药行业，并融入制药研究的大多数阶段。

DESCRIPTION (provided by applicant): Proteins play a central role in all biological processes. Akin to the complete sequencing of genomes, complete description of protein structures is a fundamental step towards understanding biological life, and is also highly relevant medically in the development of therapeutics and drugs. The broad, long-term goal of the project is to develop machine learning methods for data-driven protein structure prediction through two independent but complementary strategies: 1) much more accurate template-based modeling for proteins with remote homologs in the Protein Data Bank and 2) better template-free modeling method for proteins without detectable templates and for improving template-based models. The specific aims are: Aim 1) to greatly improve template-based modeling by 1a) improving protein sequence-template alignment using a regression-tree-based nonlinear scoring function, especially when good sequence profiles are unavailable; and 1b) improving fold recognition using a machine learning method to combine both residue-level and atom-level features; Aim 2) to improve protein conformation sampling in a continuous space and thus template-free modeling by three independent but complementary approaches: 2a) modeling nonlinear sequence- structure relationship using Conditional (Markov) Random Fields (CRF) models; 2b) simultaneously sampling secondary and tertiary structure; and 2c) learning structure information from template. The core of the project is to develop various CRF models for data-driven protein structure prediction, by learning protein sequence-structure relationship from existing sequence/structure databases. The product of this research includes a regression-tree-based CRF model for accurate protein alignment, especially for proteins without close homologs in the PDB or without very good sequence profiles; a SVM model for protein fold recognition; a few CRF models for efficient protein conformation sampling in a continuous space; and a complete protein structure prediction software package. Also, it will produce a web server publicly available for various academic and biomedical users. Protein structure prediction will lead to a broad range of biomedical applications, such as the development of novel diagnostics, better understanding of disease processes and improved preventive therapies leading to reduced health care costs. Protein modeling is also widely applied in the pharmaceutical industry and integrated into most stages of pharmaceutical research. PUBLIC HEALTH RELEVANCE: Novel protein structure prediction will lead to a broad range of biomedical applications, such as the development of novel diagnostics, better understanding of disease processes and improved preventive therapies leading to reduced health care costs. Protein modeling is also widely applied in the pharmaceutical industry and integrated into most stages of pharmaceutical research.