计算生物学
酵母
基质金属蛋白酶
表位
定向分子进化
表面蛋白
蛋白质工程
计算机科学
结合选择性
蛋白质-蛋白质相互作用
定向进化
化学
生物
突变体
生物化学
酶
遗传学
基因
抗原
病毒学
作者
Alireza Shoari,Ghazaleh Khalili‐Tanha,Matt Coban,Evette S. Radisky
标识
DOI:10.3389/fmolb.2023.1321956
摘要
The study of protein-protein interactions (PPIs) and the engineering of protein-based inhibitors often employ two distinct strategies. One approach leverages the power of combinatorial libraries, displaying large ensembles of mutant proteins, for example, on the yeast cell surface, to select binders. Another approach harnesses computational modeling, sifting through an astronomically large number of protein sequences and attempting to predict the impact of mutations on PPI binding energy. Individually, each approach has inherent limitations, but when combined, they generate superior outcomes across diverse protein engineering endeavors. This synergistic integration of approaches aids in identifying novel binders and inhibitors, fine-tuning specificity and affinity for known binding partners, and detailed mapping of binding epitopes. It can also provide insight into the specificity profiles of varied PPIs. Here, we outline strategies for directing the evolution of tissue inhibitors of metalloproteinases (TIMPs), which act as natural inhibitors of matrix metalloproteinases (MMPs). We highlight examples wherein design of combinatorial TIMP libraries using structural and computational insights and screening these libraries of variants using yeast surface display (YSD), has successfully optimized for MMP binding and selectivity, and conferred insight into the PPIs involved.
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