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

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).

De novo design of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike protein

“…The initial antibody variable domain sequences were predicted using de novo antibody design software tool, OptMAVEn-2.0 2 . Using an interatomic clash-cutoff of 1.25 Å, 173 antigen poses were sampled, and each of which yielded a successful (not necessarily unique) antibody design targeted at the seven most solvent accessible ACE2-binding residues of SARS-CoV-2 spike RBD.…”

“…During the Rosetta affinity maturation, only amino acids in the variable region within 5 Å from any epitope residue are allowed to mutate. At the end of these affinity maturation iterations, the entire spike-antibody complex energy was minimized and the Rosetta binding energy was calculated using the InterfaceAnalyzer 2 application. The entire protocol was implemented in RosettaScripts 36 using the REF2015 energy function 28 (see Supp.…”

“…We next used the previously developed OptMAVEn-2.0 2 software to computationally identify the combination of VDJ genes forming the variable region that best binds the desired epitope. OptMAVEn 20 has been used before successfully to design five high affinity CDRs against a FLAG tetrapeptide 21 , three thermally and conformationally stable antibody variable regions (sharing less than 75% sequence similarity to any naturally occurring antibody sequence) against a dodecapeptide mimic of carbohydrates 22 and two thermostable, high affinity variable heavy chain domains (V H H) against α-synuclein peptide responsible for Parkinson’s disease 23 .…”

“…Only 173 antigen-presenting poses out of 3,234 explored, yielded non-clashing antigen-antibody complexes. These 173 poses were ranked on the basis of their Rosetta binding energies with the spike epitope and classified into 27 clusters (using k-means 27 ) in a 19-dimensional space defined by quantitative descriptors of sequence similarity, three-dimensional spatial pose, and the angle at which they bind to the target epitope (see details in original paper 2 ). The top five prototype designs with the highest Rosetta binding energies were present in four clusters and spanned a highly diverse set of choices of MAPs parts (see Table 1) with minimal conservation of the same part among the five prototype designs.…”

“…In an earlier paper 1 , we report on the specific residue interactions underpinning this event. Here we report on the de novo computational design of high affinity antibody variable regions through the recombination of VDJ genes targeting the most solvent-exposed ACE2-binding residues of the SARS-CoV-2 spike protein using the software tool OptMAVEn-2.0 2 . Subsequently, we carry out computational affinity maturation of the designed prototype variable regions through point mutations for improved binding with the target epitope.…”