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

基本信息

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
项目类别：
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

项目摘要

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预测。然而，我们目前的工作将主要集中在基质金属蛋白酶 (MMP)、半胱天冬酶和属于丝氨酸蛋白酶家族的几种蛋白质转化酶 (PC)，因为桑福德-伯纳姆医学研究中心已经提供了有关这些蛋白酶的大量实验信息。研究所。当统计上显着数量的底物可用于导出特异性概况时，我们的方法可以轻松扩展到任何其他蛋白酶。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创建新的可能的切割位点来修改蛋白水解事件。这种修饰可能导致疾病或综合症。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上。收集到的所有信息和开发的工具将在 PMAP 网站 (www.proteothesis.org) 上免费提供，供生物医学研究界使用。由于蛋白酶通常具有十几种底物，并且底物在正常生理学与病理学方面通常有所不同，因此该项目的影响可能是巨大的。我们的预测不是逐一识别蛋白酶底物，而是产生注释良好的底物集，这些底物可能具有生物学意义。公共卫生相关性：蛋白水解是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人类蛋白质组中蛋白酶的底物，该方法考虑了准确的氨基酸序列特异性以及结构和生物学因素。这种计算方法将有助于检测导致疾病或综合症的蛋白质加工、调节和降解过程中的异常。