Department of Chemistry, New York University , New York, New York 10003, United States.
NYU-ECNU Center for Computational Chemistry, New York University-Shanghai , Shanghai 200122, China.
J Am Chem Soc. 2017 Nov 8;139(44):15560-15563. doi: 10.1021/jacs.7b05960. Epub 2017 Aug 4.
The use of peptidomimetic scaffolds to target protein-protein interfaces is a promising strategy for inhibitor design. The strategy relies on mimicry of protein motifs that exhibit a concentration of native hot spot residues. To address this constraint, we present a pocket-centric computational design strategy guided by AlphaSpace to identify high-quality pockets near the peptidomimetic motif that are both targetable and unoccupied. Alpha-clusters serve as a spatial representation of pocket space and are used to guide the selection of natural and non-natural amino acid mutations to design inhibitors that optimize pocket occupation across the interface. We tested the strategy against a challenging protein-protein interaction target, KIX/MLL, by optimizing a single helical motif within MLL to compete against the full-length wild-type MLL sequence. Molecular dynamics simulation and experimental fluorescence polarization assays are used to verify the efficacy of the optimized peptide sequence.
使用拟肽支架来靶向蛋白质-蛋白质界面是一种有前途的抑制剂设计策略。该策略依赖于对表现出天然热点残基集中的蛋白质模体的模拟。为了解决这个限制，我们提出了一种基于 AlphaSpace 的以口袋为中心的计算设计策略，以识别靠近拟肽模体的高质量口袋，这些口袋既具有可靶向性又未被占据。Alpha 簇作为口袋空间的空间表示，用于指导选择天然和非天然氨基酸突变，以设计优化界面上口袋占据的抑制剂。我们通过优化 MLL 内的单个螺旋模体来与全长野生型 MLL 序列竞争，针对具有挑战性的蛋白质-蛋白质相互作用靶标 KIX/MLL 测试了该策略。使用分子动力学模拟和实验荧光偏振测定来验证优化肽序列的功效。
