García-Cebollada Helena, López Alfonso, Sancho Javier
Department of Biochemistry, Molecular and Cell Biology, Faculty of Science, University of Zaragoza, 50009 Zaragoza, Spain.
Biocomputation and Complex Systems Physics Institute (BIFI), Unit and GBs-CSIC, University of Zaragoza, 50018 Zaragoza, Spain.
Comput Struct Biotechnol J. 2022 May 10;20:2415-2433. doi: 10.1016/j.csbj.2022.05.008. eCollection 2022.
Protein stability is a requisite for most biotechnological and medical applications of proteins. As natural proteins tend to suffer from a low conformational stability , great efforts have been devoted toward increasing their stability through rational design and engineering of appropriate mutations. Unfortunately, even the best currently used predictors fail to compute the stability of protein variants with sufficient accuracy and their usefulness as tools to guide the rational stabilisation of proteins is limited. We present here , a protein stabilising tool based on a different approach. Instead of quantifying changes in stability, uses structure- and sequence-based screening modules to nominate candidate mutations for subsequent evaluation by a logistic regression model, carefully trained to avoid overfitting. Thus, analyses PDB files in search for stabilization opportunities and provides a ranked list of promising mutations with their estimated success rates (eSR), their probabilities of being stabilising by at least 0.5 kcal/mol. The agreement between eSRs and actual positive predictive values (PPV) on external datasets of mutations is excellent. When is used with its Optimal kappa selection threshold, its PPV is above 0.7. Even with less stringent thresholds, largely outperforms FoldX, Rosetta and PoPMusiC. Indicating the PDB file of the protein suffices to obtain a ranked list of mutations, their eSRs and hints on the likely source of the stabilization expected. is a distinct, straightforward and highly successful tool to design protein stabilising mutations, and it is freely available for academic use at http://webapps.bifi.es/the-protposer.
蛋白质稳定性是蛋白质大多数生物技术和医学应用的必要条件。由于天然蛋白质往往构象稳定性较低，人们已付出巨大努力，通过合理设计和引入适当突变来提高其稳定性。不幸的是，即使是目前最好的预测工具也无法足够准确地计算蛋白质变体的稳定性，其作为指导蛋白质合理稳定化工具的作用有限。我们在此介绍一种基于不同方法的蛋白质稳定化工具。该工具不是量化稳定性变化，而是使用基于结构和序列的筛选模块来提名候选突变，随后由经过精心训练以避免过拟合的逻辑回归模型进行评估。因此，该工具分析蛋白质数据银行（PDB）文件以寻找稳定化机会，并提供一份有希望的突变的排名列表，以及它们的估计成功率（eSR），即它们至少稳定0.5千卡/摩尔的概率。在突变的外部数据集上，eSR与实际阳性预测值（PPV）之间的一致性非常好。当该工具与其最优卡帕选择阈值一起使用时，其PPV高于0.7。即使使用不太严格的阈值，该工具在很大程度上也优于FoldX、Rosetta和PoPMusiC。表明提供蛋白质的PDB文件就足以获得突变的排名列表、它们的eSR以及预期稳定化可能来源的提示。该工具是一种独特、直接且非常成功的设计蛋白质稳定化突变的工具，可在http://webapps.bifi.es/the-protposer免费用于学术用途。
