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: 8333323
• 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/8333323
• 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.

描述（由申请人提供）：该建议将提供信息，新算法和预测蛋白水解事件的计算工具。最终目标是对任何给定蛋白酶的底物进行准确的全蛋白质组预测。但是，我们目前的努力将主要集中在甲状腺蛋白酶家族的基质金属蛋白酶（MMP），caspase和几种属于丝氨酸蛋白酶家族的蛋白转化酶（PC），因为Sanford-Burnham医学研究所已经提供了大量有关这些蛋白酶的实验信息。当统计上大量的底物可用于得出特异性概况时，我们的方法可以很容易地扩展到任何其他蛋白酶。 提出的预测方法的独特特征是将基于序列的预测与其他因素相结合。其中包括：底物的结构特征，合作相互作用以及底物和蛋白酶的共定位和共定位和共定位。我们还将包括有关蛋白质底物裂解位点附近残基的SNP（单核苷酸多态性）和PTM（翻译后修饰）的信息。这两种效果可以通过将其关闭或创建新的可能的切割位点关闭或创建蛋白水解事件来修改蛋白水解事件。这种修饰会导致疾病或综合征。蛋白水解事件，例如蛋白酶 - 基底对，将映射到已知的调节网络上。 收集到的所有信息和开发的工具将在PMAP网站（www.proteolysosis.org）上免费获得，供生物医学研究社区使用。 由于蛋白酶通常具有十几个底物，并且由于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。我们的预测不是一对一地识别蛋白酶底物，而是会产生非常井井有条的底物集，这些底物可能具有生物学意义。