In silico proof of principle of machine learning-based antibody design at unconstrained scale

Abstract

Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody binding parameters. The simulation framework enables both the computation of antibody-antigen 3D-structures as well as functions as an oracle for unrestricted prospective evaluation of the antigen specificity of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (1D) data can be used to design native-like conformational (3D) epitope-specific antibodies, matching or exceeding the training dataset in affinity and developability variety. Furthermore, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Finally, we validated that the antibody design insight gained from simulated antibody-antigen binding data is applicable to experimental real-world data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.HighlightsA large-scale dataset of 70M [3 orders of magnitude larger than the current state of the art] synthetic antibody-antigen complexes, that reflect biological complexity, allows the prospective evaluation of antibody generative deep learningCombination of generative learning, synthetic antibody-antigen binding data, and prospective evaluation shows that deep learning driven antibody design and discovery at an unconstrained level is feasibleTransfer learning (low-N learning) coupled to generative learning shows that antibody-binding rules may be transferred across unrelated antibody-antigen complexesExperimental validation of antibody-design conclusions drawn from deep learning on synthetic antibody-antigen binding dataGraphical abstractWe leverage large synthetic ground-truth data to demonstrate the (A,B) unconstrained deep generative learning-based generation of native-like antibody sequences, (C) the prospective evaluation of conformational (3D) affinity, paratope-epitope pairs, and developability. (D) Finally, we show increased generation quality of low-N-based machine learning models via transfer learning.

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).…”

Predictive Profiling of SARS-CoV-2 Variants by Deep Mutational Learning

“…Machine learning models tested included K-nearest neighbor, logistic regression, naive Bayes, support vector machines, and random forests (RF); long-short term memory recurrent neural networks (RNN) were also trained, which are a class of deep learning models that have the ability to learn long-range dependencies in sequential data (38)(39)(40)(41). While all baseline models performed effectively (e.g., accuracy scores between 0.87 -0.94), RF and RNN were selected for further optimization and application since they showed relatively higher performance metrics and could be trained faster (Supplementary Fig.…”

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

“… 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 , 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.…”