Proteolysis in silico: statistics, structural chemistry, and biology

计算机蛋白水解：统计学、结构化学和生物学

• 批准号: 8162946
• 负责人: Piotr Cieplak
• 金额: $ 36.29万
• 依托单位: SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8162946
• 关键词: Accounting; Algorithms; Amino Acid Sequence; Base Sequence; Binding; Biological; Biological Factors; Biological Process; Biology; Biomedical Research; Caspase; Cells; Communities; Computer Simulation; Data; Databases; Disease; Eukaryotic Cell; Event; Family; Genetic Polymorphism; Goals; Health; Human; Human Genome; Hydrolysis; Knowledge; Lead; Link; Literature; Maps; Medical Research; Metalloproteases; Methodology; Methods; Modeling; Modification; Monitor; Normal Range; Nucleotides; Pathology; Pathway interactions; Peptide Hydrolases; Peptides; Physiology; Play; Post-Translational Protein Processing; Process; Proteins; Proteolysis; Proteome; Proteomics; Protocols documentation; Regulation; Research Institute; Resources; Role; Screening procedure; Seeds; Serine Protease; Single Nucleotide Polymorphism; Site; Specificity; Structural Chemistry; Syndrome; Testing; Therapeutic Intervention; Time; base; computerized tools; design; enzyme substrate; protein degradation; proteinase In; research study; statistics; tool; web site

DESCRIPTION (provided by applicant): This proposal will provide information, new algorithms, and computational tools for predicting proteolytic events. The ultimate goal is to make accurate proteome-wide predictions of the substrates for any given protease. However, our current effort will focus mainly on matrix metalloproteases (MMPs), caspases, and several protein convertases (PCs) belonging to the serine protease family because a vast amount of experimental information on those proteases is already available at the Sanford-Burnham Medical Research Institute. Our approach can be easily extended to any other proteases when a statistically significant number of substrates become available for deriving a specificity profile. The unique feature of the proposed prediction method is combining sequence-based predictions with other factors. These include: structural features of the substrates, cooperative interactions, and co-localization and co-expression of substrates and proteases. We will also include information about SNPs (single nucleotide polymorphisms) and PTMs (posttranslational modifications) of the residues in the vicinity of the cleavage sites in protein substrates. These two effects can modify the proteolytic event by turning it off or by creating a new possible cleavage site. Such modifications can lead to diseases or syndromes. The proteolytic events, e.g., protease-substrate pairs, will be mapped onto the known regulatory networks. All the information that is collected and tools that are developed will be freely available on the PMAP Web site (www.proteolysis.org) for use by the biomedical research community. Because proteases usually have more than a dozen substrates, and because the substrates often differ in normal physiology vs. pathology, the impact of this project could be immense. Rather than identifying protease substrates on a one-by-one basis, our predictions will produce very-well-annotated sets of substrates that will likely have biological significance. PUBLIC HEALTH RELEVANCE: Proteolysis is a biological process involving hydrolysis of the peptide bonds in proteins. We propose to design a computational approach for predicting substrates for proteinases in human proteome that takes into account accurate amino acid sequence specificity and structural and biological factors. This computational approach will help detect aberrations in the processing, regulation, and degradation of proteins leading to disease or syndromes.

描述（由申请人提供）：该提案将提供用于预测蛋白水解事件的信息、新算法和计算工具。最终目标是对任何给定蛋白酶的底物进行准确的蛋白质组范围的预测。然而，我们目前的努力将主要集中在基质金属蛋白酶（MMP），半胱天冬酶，和几个蛋白转化酶（PC）属于丝氨酸蛋白酶家族，因为大量的实验信息，这些蛋白酶已经在桑福德伯纳姆医学研究所。我们的方法可以很容易地扩展到任何其他蛋白酶时，统计上显着数量的底物变得可用于推导特异性谱。 所提出的预测方法的独特之处在于将基于序列的预测与其他因素相结合。这些包括：底物的结构特征，协同相互作用，以及底物和蛋白酶的共定位和共表达。我们还将包括有关蛋白质底物中切割位点附近残基的SNP（单核苷酸多态性）和PTM（翻译后修饰）的信息。这两种效应可以通过关闭蛋白水解事件或通过产生新的可能的切割位点来修饰蛋白水解事件。这种改变可能导致疾病或综合征。蛋白水解事件，例如，蛋白酶底物对，将被映射到已知的调控网络。 所有收集的信息和开发的工具都将在PMAP网站（www.proteolysis.org）上免费提供，供生物医学研究界使用。 因为蛋白酶通常有十几种底物，而且这些底物在正常生理学和病理学上往往不同，所以这个项目的影响可能是巨大的。我们的预测将产生非常好的注释的底物集，而不是在一个接一个的基础上识别蛋白酶底物，这些底物可能具有生物学意义。 公共卫生相关性：蛋白质水解是一种涉及蛋白质中肽键水解的生物过程。我们建议设计一种计算方法来预测底物的蛋白酶在人类蛋白质组，考虑到准确的氨基酸序列特异性和结构和生物因素。这种计算方法将有助于检测导致疾病或综合征的蛋白质加工、调节和降解中的畸变。