Sulea Traian, Vivcharuk Victor, Corbeil Christopher R, Deprez Christophe, Purisima Enrico O
Human Health Therapeutics, National Research Council Canada , 6100 Royalmount Avenue, Montreal, QC, Canada H4P 2R2.
J Chem Inf Model. 2016 Jul 25;56(7):1292-303. doi: 10.1021/acs.jcim.6b00043. Epub 2016 Jul 14.
Affinity modulation of antibodies and antibody fragments of therapeutic value is often required in order to improve their clinical efficacies. Virtual affinity maturation has the potential to quickly focus on the critical hotspot residues without the combinatorial explosion problem of conventional display and library approaches. However, this requires a binding affinity scoring function that is capable of ranking single-point mutations of a starting antibody. We focus here on assessing the solvated interaction energy (SIE) function that was originally developed for and is widely applied to scoring of protein-ligand binding affinities. To this end, we assembled a structure-function data set called Single-Point Mutant Antibody Binding (SiPMAB) comprising several antibody-antigen systems suitable for this assessment, i.e., based on high-resolution crystal structures for the parent antibodies and coupled with high-quality binding affinity measurements for sets of single-point antibody mutants in each system. Using this data set, we tested the SIE function with several mutation protocols based on the popular methods SCWRL, Rosetta, and FoldX. We found that the SIE function coupled with a protocol limited to sampling only the mutated side chain can reasonably predict relative binding affinities with a Spearman rank-order correlation coefficient of about 0.6, outperforming more aggressive sampling protocols. Importantly, this performance is maintained for each of the seven system-specific component subsets as well as for other relevant subsets including non-alanine and charge-altering mutations. The transferability and enrichment in affinity-improving mutants can be further enhanced using consensus ranking over multiple methods, including the SIE, Talaris, and FOLDEF energy functions. The knowledge gained from this study can lead to successful prospective applications of virtual affinity maturation.
为了提高其临床疗效，通常需要对具有治疗价值的抗体和抗体片段进行亲和力调节。虚拟亲和力成熟有潜力快速聚焦于关键的热点残基，而不存在传统展示和文库方法中的组合爆炸问题。然而，这需要一个能够对起始抗体的单点突变进行排序的结合亲和力评分函数。我们在此专注于评估最初开发并广泛应用于蛋白质 - 配体结合亲和力评分的溶剂化相互作用能（SIE）函数。为此，我们组装了一个名为单点突变抗体结合（SiPMAB）的结构 - 功能数据集，该数据集包含几个适用于此评估的抗体 - 抗原系统，即基于亲本抗体的高分辨率晶体结构，并结合每个系统中单点抗体突变体集的高质量结合亲和力测量。使用这个数据集，我们基于流行的方法SCWRL、Rosetta和FoldX，用几种突变方案测试了SIE函数。我们发现，与仅限于对突变侧链进行采样的方案相结合的SIE函数能够合理地预测相对结合亲和力，斯皮尔曼等级相关系数约为0.6，优于更激进的采样方案。重要的是，对于七个系统特定的组件子集中的每一个以及包括非丙氨酸和电荷改变突变在内的其他相关子集，这种性能都得以保持。使用包括SIE、Talaris和FOLDEF能量函数在内的多种方法进行共识排序，可以进一步提高亲和力改善突变体的可转移性和富集性。从这项研究中获得的知识可以导致虚拟亲和力成熟的成功前瞻性应用。
