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

Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
mAbs
Themenheft
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
14
Ausgabe / Nummer
1
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Taylor & Francis
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Generative machine learning (ML) has been postulated to become 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 the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
Schlagwörter
Generative machine learning, Antibody design, Paratope, Epitope
Fachgebiet (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Projekt
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1942-0862
1942-0870
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
'https://creativecommons.org/licenses/by-nc/4.0/'
Zitation
AKBAR, Rahmad, Philippe A. ROBERT, Cédric R. WEBER, Michael WIDRICH, Robert FRANK, Milena PAVLOVIĆ, Lonneke SCHEFFER, Maria CHERNIGOVSKAYA, Igor SNAPKOV, Andrei SLABODKIN, Brij Bhushan MEHTA, Enkelejda MIHO, Fridtjof LUND-JOHANSEN, Jan Terje ANDERSEN, Sepp HOCHREITER, Ingrid HOBÆK HAFF, Günter KLAMBAUER, Geir Kjetil SANDVE und Victor GREIFF, 2022. In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs. 4 April 2022. Bd. 14, Nr. 1. DOI 10.1080/19420862.2022.2031482. Verfügbar unter: https://doi.org/10.26041/fhnw-9953