Novel method to identify competing protein-protein binders

识别竞争性蛋白质-蛋白质结合物的新方法

• 批准号: 7362003
• 负责人: BURKHARD ROST
• 金额: $ 8.02万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7362003
• 关键词: Binding; Bioinformatics; Biological; Biology; Communities; Computational Biology; Databases; Development; Distant; Evolution; Family; Goals; Homologous Gene; Hot Spot; Light; Location; Methodology; Methods; Organism; Protein Binding; Proteins; Proteome; Resources; Signal Transduction; Structure; Surface; Testing; Work; base; improved; insight; novel; numb protein; protein function; protein protein interaction; protein structure; protein structure function; research study; success; technique development; tool

DESCRIPTION (provided by applicant): The difference between the number of proteins with known sequence and those with wellstudied function (sequence-function gap) is growing daily. Bioinformatics bridges this gap mostly through homology-based inferences of the type: assume that the function of A is known, and that B is sequence similar to A. The inference then is that A and B both have the same function. Successful homology-based inference requires that function is conserved at the level of divergence between A and B, and that an alignment between the two picks up the correct signal of conservation. Here, we propose the development of a new generalized alignment method that will be tailored specifically to the identification of competitive protein-protein binders, e.g. proteins A and B that bind well to the same protein target. Such a development is particularly needed in the light of the recent finding that protein-protein interactions are poorly conserved between organisms. Our main hypothesis is that competing binders will have similar binding hot spots and that the accuracy of our method for the prediction of hot spots will suffice to profit considerably from focusing exclusively on such hot spots when comparing two proteins. In other words, we will first predict the hot spots for both A and B and then align A and B preferentially through their hot spots. While no such concept has ever been applied to the prediction of binding features, similar concepts have significantly improved the identification of proteins with similar structure.

描述（由申请人提供）： 具有已知序列的蛋白质数量与具有良好功能的蛋白质数量（序列功能差距）之间的差异每天都在增长。生物信息学主要是通过基于同源的类型的推论来桥接这一差距：假设a的函数已知，并且B是序列类似于A的序列。推断是A和B都具有相同的函数。成功的基于同源性的推断要求该功能在A和B之间的差异水平上保留，并且两者之间的一致性接收到正确的保护信号。在这里，我们提出了一种新的广义比对方法的开发，该方法将专门针对竞争性蛋白质 - 蛋白质粘合剂的识别，例如蛋白A和B与同一蛋白质靶标很好地结合。鉴于最近的发现，蛋白质 - 蛋白质相互作用在生物体之间是较差的，因此特别需要这种发展。我们的主要假设是，竞争性的粘合剂将具有相似的结合热点，并且我们对热点预测的方法的准确性足以在比较两种蛋白质时仅专注于此类热点，这足以利用。换句话说，我们将首先预测A和B的热点，然后通过其热点优先对齐A和B。尽管从未将这种概念应用于结合特征的预测，但相似的概念显着改善了具有相似结构的蛋白质的鉴定。