Novel method to identify competing protein-protein binders

描述（由申请人提供）： 已知序列的蛋白质和功能研究充分的蛋白质（序列-功能缺口）之间的差异每天都在增加。生物信息学主要通过基于同源性的推断来弥补这一差距：假设A的功能是已知的，并且B是与A相似的序列。由此推论，A和B具有相同的功能。成功的基于同源性的推断要求功能在A和B之间的差异水平上是保守的，并且两者之间的比对拾取了正确的保守信号。在这里，我们提出了一种新的通用的比对方法，将专门针对竞争性蛋白质-蛋白质结合剂，如蛋白质A和B，以及结合到相同的蛋白质目标的识别。鉴于最近发现蛋白质-蛋白质相互作用在生物体之间的保守性很差，这种发展特别需要。我们的主要假设是，竞争的结合剂将有类似的结合热点，我们的方法预测的热点的准确性将足以受益于专门集中在这样的热点比较两种蛋白质。换句话说，我们将首先预测A和B的热点，然后通过它们的热点优先对齐A和B。虽然没有这样的概念已经被应用到结合特征的预测，类似的概念已经显着提高了具有相似结构的蛋白质的识别。

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.