Department of Computer Science, Duke University, Durham, North Carolina, USA.
Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, Austria.
J Comput Biol. 2022 Dec;29(12):1346-1352. doi: 10.1089/cmb.2022.0254. Epub 2022 Sep 13.
Computational, in silico prediction of resistance-conferring escape mutations could accelerate the design of therapeutics less prone to resistance. This article describes how to use the Resistor algorithm to predict escape mutations. Resistor employs Pareto optimization on four resistance-conferring criteria-positive and negative design, mutational probability, and hotspot cardinality-to assign a Pareto rank to each prospective mutant. It also predicts the mechanism of resistance, that is, whether a mutant ablates binding to a drug, strengthens binding to the endogenous ligand, or a combination of these two factors, and provides structural models of the mutants. Resistor is part of the free and open-source computational protein design software OSPREY.
计算,计算机预测耐药性的逃逸突变可以加速设计不易产生耐药性的治疗方法。本文介绍了如何使用 Resistor 算法来预测逃逸突变。Resistor 算法通过对四个耐药性赋予标准(阳性和阴性设计、突变概率和热点基数)进行帕累托优化,为每个潜在的突变体分配帕累托等级。它还预测耐药机制,即突变体是否会破坏与药物的结合,增强与内源性配体的结合,或者这两种因素的结合,并提供突变体的结构模型。Resistor 是免费和开源的计算蛋白质设计软件 OSPREY 的一部分。