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Publication Abstract from PubMed

An increasing number of protein interaction domains and their targets are being found to be intrinsically disordered proteins (IDPs). The corresponding target recognition mechanisms are mostly elusive because of challenges in performing detailed structural analysis of highly dynamic IDP-IDP complexes. Here, we show that by combining recently developed computational approaches with experiments, the structure of the complex between the intrinsically disordered C-terminal domain (CTD) of protein 4.1G and its target IDP region in NuMA can be dissected at high resolution. First, we carry out systematic mutational scanning using dihydrofolate reductase-based protein complementarity analysis to identify essential interaction regions and key residues. The results are found to be highly consistent with an alpha/beta-type complex structure predicted by AlphaFold2 (AF2). We then design mutants based on the predicted structure using a deep learning protein sequence design method. The solved crystal structure of one mutant presents the same core structure as predicted by AF2. Further computational prediction and experimental assessment indicate that the well-defined core structure is conserved across complexes of 4.1G CTD with other potential targets. Thus, we reveal that an intrinsically disordered protein interaction domain uses an alpha/beta-type structure module formed through synergistic folding to recognize broad IDP targets. Moreover, we show that computational prediction and experiment can be jointly applied to segregate true IDP regions from the core structural domains of IDP-IDP complexes and to uncover the structure-dependent mechanisms of some otherwise elusive IDP-IDP interactions.

Combined prediction and design reveals the target recognition mechanism of an intrinsically disordered protein interaction domain.,Hu X, Xu Y, Wang C, Liu Y, Zhang L, Zhang J, Wang W, Chen Q, Liu H Proc Natl Acad Sci U S A. 2023 Sep 26;120(39):e2305603120. doi: , 10.1073/pnas.2305603120. Epub 2023 Sep 18. PMID:37722056^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.