Sequence-specific targeting of intrinsically disordered protein regions

bioRxiv Pub Date : 2024-07-16 DOI:10.1101/2024.07.15.603480
Kejia Wu, Hanlun Jiang, Derrick R. Hicks, Caixuan Liu, Edin Muratspahić, T. A. Ramelot, Yuexuan Liu, Kerrie E. McNally, Amit Gaur, B. Coventry, Wei Chen, Asim K. Bera, A. Kang, Stacey R Gerben, Mila Lamb, Analisa Murray, Xinting Li, Madison A. Kennedy, Wei Yang, Gudrun Schober, Stuart M. Brierley, Michael H. Gelb, Gaetano T. Montelione, Emmanuel Derivery, David Baker
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Abstract

A general approach to design proteins that bind tightly and specifically to intrinsically disordered regions (IDRs) of proteins and flexible peptides would have wide application in biological research, therapeutics, and diagnosis. However, the lack of defined structures and the high variability in sequence and conformational preferences has complicated such efforts. We sought to develop a method combining biophysical principles with deep learning to readily generate binders for any disordered sequence. Instead of assuming a fixed regular structure for the target, general recognition is achieved by threading the query sequence through diverse extended binding modes in hundreds of templates with varying pocket depths and spacings, followed by RFdiffusion refinement to optimize the binder-target fit. We tested the method by designing binders to 39 highly diverse unstructured targets. Experimental testing of ∼36 designs per target yielded binders with affinities better than 100 nM in 34 cases, and in the pM range in four cases. The co-crystal structure of a designed binder in complex with dynorphin A is closely consistent with the design model. All by all binding experiments for 20 designs binding diverse targets show they are highly specific for the intended targets, with no crosstalk even for the closely related dynorphin A and dynorphin B. Our approach thus could provide a general solution to the intrinsically disordered protein and peptide recognition problem.
内在无序蛋白质区域的序列特异性定位
设计能与蛋白质和柔性肽的内在无序区(IDRs)紧密特异性结合的蛋白质的一般方法将在生物研究、治疗和诊断中得到广泛应用。然而,由于缺乏确定的结构,以及序列和构象偏好的高度可变性,使得这些工作变得更加复杂。我们试图开发一种将生物物理原理与深度学习相结合的方法,以轻松生成任何无序序列的结合体。我们没有假定目标物具有固定的规则结构,而是通过在数百个具有不同口袋深度和间距的模板中穿行不同的扩展结合模式来实现查询序列的一般识别,然后通过射频扩散细化来优化结合体与目标物的匹配。我们通过为 39 个高度多样化的非结构化靶标设计粘合剂对该方法进行了测试。对每个靶标进行了 36 个设计方案的实验测试,结果发现 34 个设计方案的粘合剂亲和力优于 100 nM,4 个设计方案的亲和力在 pM 范围内。设计的粘合剂与达因吗啡肽 A 复合物的共晶体结构与设计模型非常吻合。对 20 种与不同靶标结合的设计方案进行的所有结合实验表明,它们对预期靶标具有高度特异性,即使是与之密切相关的达因吗啡肽 A 和达因吗啡肽 B 也没有串扰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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