OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes.

Ratul Chowdhury,Matthew F. Allan,Costas D. Maranas

doi:10.3390/antib7030023

Ratul Chowdhury, Matthew F. Allan + Show 1 more

Open Access

https://doi.org/10.3390/antib7030023

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Abstract

Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section).

Highlights

Antibodies are versatile molecules produced in B-cells and have become the basis of many therapeutics [1,2,3] and diagnostics [4,5,6] for cancers [6,7,8], infectious diseases [9], and autoimmune disorders [10]
We first benchmarked OptMAVEn-2.0 against OptMAVEn with a set of 10 antigens and subsequently used 54 additional antigens to assess the performance of the current algorithm
OptMAVEn-2.0 to design antibody variable fragments against two sets of Zika envelope proteins reported by Wang et al [45] (PDB: 5GZN) and Zhao et al [46] (5KVD, 5KVE, 5KVF, and 5KVG)

Summary

Introduction

Antibodies are versatile molecules produced in B-cells and have become the basis of many therapeutics [1,2,3] and diagnostics [4,5,6] for cancers [6,7,8], infectious diseases [9], and autoimmune disorders [10]. They are affinity proteins that are crucial for humoral immunity and are able to bind to foreign proteins with high specificity [11]. Chimeras exhibit less immunogenicity relative to Antibodies 2018, 7, 23; doi:10.3390/antib7030023 www.mdpi.com/journal/antibodies

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Discussion

Conclusion