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: 8728277
• 负责人: Piotr Cieplak
• 金额: $ 37.05万
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8728277
• 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; Seeds; Serine Protease; Single Nucleotide Polymorphism; Site; Specificity; Structural Chemistry; Syndrome; Testing; Therapeutic Intervention; Time; base; computerized tools; design; enzyme substrate; mathematical methods; protein degradation; proteinase In; research study; screening; 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.

描述（由申请人提供）：该提案将提供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，我们目前的努力将主要集中在基质金属蛋白酶（MMPs），半胱天蛋白酶，和一些蛋白质转化酶（pc）属于丝氨酸蛋白酶家族，因为在斯坦福-伯纳姆医学研究所已经有大量关于这些蛋白酶的实验信息。我们的方法可以很容易地扩展到任何其他蛋白酶，当统计显著数量的底物成为可用于导出特异性谱。该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、底物和蛋白酶的共定位和共表达。我们还将包括关于蛋白质底物切割位点附近残基的snp（单核苷酸多态性）和PTMs（翻译后修饰）的信息。这两种作用可以通过关闭或产生新的可能的裂解位点来改变蛋白水解事件。这种改变可能导致疾病或综合征。蛋白质水解事件，例如蛋白酶-底物对，将被映射到已知的调节网络上。所有收集到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，而且底物在正常生理和病理上往往不同，所以这个项目的影响可能是巨大的。而不是在一个接一个的基础上鉴定蛋白酶底物，我们的预测将产生非常好的底物注释集，可能具有生物学意义。

项目成果

Sequence-derived structural features driving proteolytic processing.

• DOI: 10.1002/pmic.201300416
• 发表时间: 2014-01
• 期刊: PROTEOMICS
• 影响因子: 3.4
• 作者: Belushkin, Alexander A.;Vinogradov, Dmitry V.;Gelfand, Mikhail S.;Osterman, Andrei L.;Cieplak, Piotr;Kazanov, Marat D.
• 通讯作者: Kazanov, Marat D.