MmCSM-AB: guiding rational antibody engineering through multiple point mutations.

David B Ascher,Douglas E V Pires,Yoochan Myung

doi:10.1093/nar/gkaa389

Abstract

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

Highlights

The ability of antibodies to selectively and bind tightly to targets and sites considered undruggable, has seen them become a major focus of therapeutic and diagnostic applications in a wide range of diseases
We have previously shown that by using graph-based signatures to represent the wild-type residue environment we can accurately predict the effects of mutations on protein folding, stability [14,15,16], dynamics [17], function [18] and interactions [15,19,20,21,22,23,24,25]. These have provided insights into genetic diseases [26,27,28,29,30,31,32], drug resistance [33,34,35,36,37,38,39,40,41,42], pharmacokinetics [43,44,45,46] and rational protein engineering [47]. Extending this to look at antibody engineering, we developed mCSM-AB2 [25], which was able to more accurately predict the effects of single-point missense mutations on antibody binding affinity
W126 Nucleic Acids Research, 2020, Vol 48, Web Server issue using graph-based signatures, sequence- and structurebased information. mmCSM-AB models were trained using single-point mutations and the effects of multiple mutations were assessed, outperforming other available tools across our validation set of experimentally measured changes with double to 14 mutations. mmCSM-AB will help to guide rational antibody engineering by analysing the effects of introducing multiple mutations on binding affinity

Summary

INTRODUCTION

The ability of antibodies to selectively and bind tightly to targets and sites considered undruggable, has seen them become a major focus of therapeutic and diagnostic applications in a wide range of diseases. Initial approaches used a range of techniques, including homology modelling [4], protein–protein docking [5,6,7], energy functions [8,9,10] and more recently machine learning-based approaches [11,12,13] While these have been successfully used in the development of a number of clinical antibodies, they have generally been limited to the analysis of single-point missense mutations, and have been shown to be only weakly correlated with experimentally measured changes. W126 Nucleic Acids Research, 2020, Vol 48, Web Server issue using graph-based signatures, sequence- and structurebased information. mmCSM-AB models were trained using single-point mutations and the effects of multiple mutations were assessed, outperforming other available tools across our validation set of experimentally measured changes with double to 14 mutations. mmCSM-AB will help to guide rational antibody engineering by analysing the effects of introducing multiple mutations on binding affinity

MATERIALS AND METHODS

Method

Findings

CONCLUSION