Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
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DOI：
10.1371/journal.pone.0082849
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Loving KA
中科院分区：
文献类型：
--
作者：
Beard H;Cholleti A;Pearlman D;Sherman W;Loving KA
预测单个氨基酸突变导致的蛋白质结合亲和力的变化有助于我们更好地了解蛋白质-蛋白质相互作用的驱动力，并设计改进的生物治疗药物。在这里，我们使用 MM-GBSA 方法结合 OPLS2005 力场和 VSGB2.0 溶剂模型来计算野生型和突变蛋白之间的结合自由能差异。至关重要的是，作为蛋白质-蛋白质结合亲和力工作的一部分，我们没有对评分模型进行任何更改——能量模型是为结构预测而开发的，之前仅针对计算小分子结合的能量进行了验证。在这里，我们将 21 个靶标中的一组 418 个单残基突变的预测与实验数据进行比较，发现 MM-GBSA 模型平均而言在对这些单蛋白残基突变进行评分方面表现良好。结合亲和力的预测变化和实验变化之间的相关性具有统计显着性，并且该模型在挑选“热点”或改变结合亲和力超过 1 kcal/mol 的突变方面表现良好。这种基于物理的方法无需调整参数来预测结合能，其良好的性能表明它可以应用于其他蛋白质工程问题。
Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity—the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking “hotspots,” or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.
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影响因子：
56.9
作者：
Korkegian, A;Black, ME;Stoddard, BL
通讯作者：
Stoddard, BL
影响因子：
56.9
作者：
Dahiyat, BI;Mayo, SL
通讯作者：
Mayo, SL
影响因子：
2.9
作者：
Bas, Delphine C.;Rogers, David M.;Jensen, Jan H.
通讯作者：
Jensen, Jan H.
影响因子：
56.9
作者：
DeLano, WL;Ultsch, MH;Wells, JA
通讯作者：
Wells, JA
DOI：
10.1093/protein/11.1.65
发表时间：
1998-01-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Dougan, DA;Malby, RL;Hudson, PJ
通讯作者：
Hudson, PJ