Assessment of Solvated Interaction Energy Function for Ranking Antibody–Antigen Binding Affinities

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.

治疗性抗体及其片段的亲和力调节对于提升其临床疗效至关重要。虚拟亲和力成熟化技术具有快速聚焦于关键热点残基的优势，且避免了传统展示和文库方法中的组合爆炸问题。然而，这需要一种能够对起始抗体的单点突变进行排序的亲和力评分函数。本研究专注于评估一种最初为蛋白质-配体结合亲和力评分而开发并广泛应用的溶剂化相互作用能（SIE）函数。为此，我们构建了一个名为“单点突变抗体结合”（SiPMAB）的结构-功能数据集，该数据集包含多个适合此评估的抗体-抗原系统，即基于母体抗体的分辨率高的晶体结构，并结合每个系统中针对单点抗体突变的高质量亲和力测量。利用此数据集，我们基于流行的SCWRL、Rosetta和FoldX方法测试了SIE函数的突变协议。我们发现，SIE函数与仅采样突变侧链的协议相结合，可以合理地预测相对结合亲和力，其Spearman秩相关系数约为0.6，优于更具侵略性的采样协议。重要的是，这种性能在七个系统特定组件子集以及其他相关子集（包括非丙氨酸和电荷改变突变）中也得到了保持。通过在包括SIE、Talaris和FOLDEF能量函数在内的多种方法中进行一致性排名，可以进一步提高转移性和亲和力提升突变体的富集。本研究获得的知识可指导虚拟亲和力成熟化的成功前瞻性应用。