Abstract：
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method\'s speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.
摘要：
由于大量可能的结合物,鉴定感兴趣的蛋白质的相互作用组是具有挑战性的。高通量实验方法缩小可能的结合伴侣，但通常包括假阳性。此外，它们不提供有关结合区域是什么的信息(例如，结合表位)。我们介绍了一种基于AlphaFold2（AF）竞争结合测定（AF-CBA）的新型计算流程，以从下拉实验和结合表位中鉴定结合目标靶标的蛋白质。我们的重点是结合溴和外端结构域（BET）蛋白的外端（ET）结构域的蛋白质，但我们也引入了九个额外的系统，以显示转移到其他肽-蛋白质系统。我们基于AF和AF-CBA的内在缺陷描述了该方法的一系列限制，以帮助用户识别该方法最有用的场景。鉴于该方法的速度和准确性，我们预计其广泛适用于识别潜在伴侣之间的结合表位区域，为实验验证奠定基础。
