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.Highlights <jats:list list-type="bullet">A 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 abstract <jats:fig id="ufig1" position="float" orientation="portrait" fig-type="figure"><jats:caption>We 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.</jats:caption><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="451480v1_ufig1" position="float" orientation="portrait" /></jats:fig>.

Medienart:	Preprint

Erscheinungsjahr:	2023
Erschienen:	2023

Enthalten in:	bioRxiv.org - (2023) vom: 05. Nov. Zur Gesamtaufnahme - year:2023

Sprache:	Englisch

Beteiligte Personen:

Akbar, Rahmad [VerfasserIn] Robert, Philippe A. [VerfasserIn] Weber, Cédric R. [VerfasserIn] Widrich, Michael [VerfasserIn] Frank, Robert [VerfasserIn] Pavlović, Milena [VerfasserIn] Scheffer, Lonneke [VerfasserIn] Chernigovskaya, Maria [VerfasserIn] Snapkov, Igor [VerfasserIn] Slabodkin, Andrei [VerfasserIn] Mehta, Brij Bhushan [VerfasserIn] Miho, Enkelejda [VerfasserIn] Lund-Johansen, Fridtjof [VerfasserIn] Andersen, Jan Terje [VerfasserIn] Hochreiter, Sepp [VerfasserIn] Haff, Ingrid Hobæk [VerfasserIn] Klambauer, Günter [VerfasserIn] Sandve, Geir Kjetil [VerfasserIn] Greiff, Victor [VerfasserIn]

Links:	Volltext [lizenzpflichtig] Volltext [kostenfrei]

Themen:	570 Biology

doi:	10.1101/2021.07.08.451480

funding:
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PPN (Katalog-ID):	XBI032167830