m+Medelevator.RU
← Поиск публикаций
Biochemistry · 2026

Phage Display Driven Identification and Computational Mapping of Macrocyclic Peptides Targeting RhoA G17V.

СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Abraham Sebin, Zhu Chaoyang, Le Lai Hoang Son, Alugubelli Yugendar R, Nonomura Tatsuki, Huang Yun, Hampton J Trae, Zhou Yubin, Liu Wenshe Ray

Аннотация

Mutant RhoA G17V is a clinically significant yet historically undruggable oncogenic GTPase that drives angioimmunoblastic T-cell lymphoma through a neomorphic interaction with the guanine nucleotide exchange factor Vav1. Its rigid GTPase fold, absence of deep binding pockets, and transient protein-protein interfaces have hindered conventional small-molecule approaches, creating a critical need for alternative therapeutic modalities. Here, we report a systematic strategy to target RhoA G17V using macrocyclic peptides. Two complementary phage-displayed cyclic peptide libraries, an AcrK-mediated 10-mer cyclic library and a CAmCBT-cyclized 12-mer library, were subjected to high-stringency biopanning against recombinant RhoA G17V. While the 10-mer library yielded moderate-affinity binders, the 12-mer library enabled the discovery of Z1, a macrocyclic peptide with submicromolar affinity (KD = 136 nM), representing the highest-affinity peptide reported for RhoA G17V to date. Computational docking combined with long-timescale molecular dynamics simulations revealed a stable peptide-protein interaction governed by cooperative hydrophobic and electrostatic interactions. Systematic alanine scanning mutagenesis experimentally validated the predicted binding determinants, confirming the key residues within the macrocycle. Collectively, this work establishes macrocyclic phage display as a powerful and generalizable platform for discovering high-affinity ligands against challenging mutant GTPases and lays a foundation for the development of precision peptide-based therapeutics.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты