Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

Abstract

Machine learning (ML) is a key technology to enable accurate prediction of antibody-antigen binding, a prerequisite for in silico vaccine and antibody design. Two orthogonal problems hinder the current application of ML to antibody-specificity prediction and the benchmarking thereof: (i) The lack of a unified formalized mapping of immunological antibody specificity prediction problems into ML notation and (ii) the unavailability of large-scale training datasets. Here, we developed the Absolut! software suite that allows the parameter-based unconstrained generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity. We show that Absolut!-generated datasets recapitulate critical biological sequence and structural features that render antibody-antigen binding prediction challenging. To demonstrate the immediate, high-throughput, and large-scale applicability of Absolut!, we have created an online database of 1 billion antibody-antigen structures, the extension of which is only constrained by moderate computational resources. We translated immunological antibody specificity prediction problems into ML tasks and used our database to investigate paratope-epitope binding prediction accuracy as a function of structural information encoding, dataset size, and ML method, which is unfeasible with existing experimental data. Furthermore, we found that in silico investigated conditions, predicted to increase antibody specificity prediction accuracy, align with and extend conclusions drawn from experimental antibody-antigen structural data. In summary, the Absolut! framework enables the development and benchmarking of ML strategies for biotherapeutics discovery and design.

Individualized VDJ recombination predisposes the available Ig sequence space

“…Machine learning is increasingly used for AIRR classification both on the sequence (Greiff et al 2017b;Isacchini et al 2021;Akbar et al 2021a;Robert et al 2021a) and repertoire level (Emerson et al 2017;Shemesh et al 2021;Sidhom et al 2021), as well as for antibody generation (Friedensohn et al 2020;Akbar et al 2021b). Future studies will need to investigate whether differences in RGM also impact repertoire classification (Greiff et al 2020;Rodriguez et al 2020;Kanduri et al 2021).…”

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

“…So far, rule inference via, for instance, attribution methods, remains a challenge and is poorly standardized. 54 , 89…”

“…The outer red ring represents the number of antibody sequences in the iReceptor database (the largest publicly available sequence data, 53 the outer purple ring the number of synthetic antibody–antigen binding structures from Absolut! (the largest publicly available synthetic antibody-antigen structural dataset), 54 the outer blue ring displays the number of structures from AbDb (curated antibody–antigen structural data 55 obtained from the protein data bank), 56 and the outer grey ring represents developability information. 52 inner rings illustrate information about antibody-antigen complexes, ig repertoire, therapeutic antibodies, and paratope and epitope data.…”

“…Recently, we presented our efforts to increase the amount of 3D-structure data by six orders of magnitude larger than the 1200 structures available experimentally ( Figure 2 ) 55 via simulating virtual coarse-grained docking of billions of antibody-antigen pairs with several layers of biological complexity. 54 We complemented this data with in silico predicted developability parameters to create datasets that encompass the three aforementioned key design parameters: paratope-epitope binding, affinity, and developability. 27 Such efforts have begun to increase the number of datasets to a level where the benchmarking of data-intensive methods, such as deep learning to study antibody-antigen binding at the paratope-epitope level as well as deep learning-based antibody sequence generation, started to become feasible.…”

“… 27 Such efforts have begun to increase the number of datasets to a level where the benchmarking of data-intensive methods, such as deep learning to study antibody-antigen binding at the paratope-epitope level as well as deep learning-based antibody sequence generation, started to become feasible. 27 , 54 More generally, large-scale 3D-atomistic resolution data generation may represent the next major step where abundantly available antibody sequence data will be leveraged to obtain large quantities of antibody-antigen complexes via recent advances in computational structural biology methods such as antibody modeling, 59–63 molecular docking, 64–67 and molecular dynamics. 68 , 69 …”