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Harmalkar A, Rao R, Richard Xie Y, Honer J, Deisting W, Anlahr J, Hoenig A, Czwikla J, Sienz-Widmann E, Rau D, Rice AJ, Riley TP, Li D, Catterall HB, Tinberg CE, Gray JJ, Wei KY. Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features. MAbs 2023; 15:2163584. [PMID: 36683173 PMCID: PMC9872953 DOI: 10.1080/19420862.2022.2163584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 01/24/2023] Open
Abstract
Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Roshan Rao
- Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Yuxuan Richard Xie
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonas Honer
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Wibke Deisting
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Jonas Anlahr
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Anja Hoenig
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Julia Czwikla
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Eva Sienz-Widmann
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Doris Rau
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Austin J. Rice
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Timothy P. Riley
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Danqing Li
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | | | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Kathy Y. Wei
- Therapeutic Discovery, Amgen Research, Amgen Inc, South San Francisco, CA, USA
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