PDF(Pubmed)
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) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with 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 to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.
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，它们不提供关于结合区域是什么(例如结合表位)的信息。我们引入了一种基于AlphaFold2（AF）竞争分析（AF-CBA）的新型计算管道，以从下拉实验中鉴定与目标靶标结合的蛋白质。以及结合表位。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。WedescribeaseriesoflimitationstothemethodologybasedonintrensionstoAFandAF-CBA,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，我们希望它通常适用于从高通量蛋白质文库开始进行实验验证的目标选择。
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