Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

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, $\rho$, 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 ($\rho =$ 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.

KEYWORDS:

• Antibody design
• thermostability prediction
• machine learning
• unsupervised and supervised learning

Acknowledgments

The authors thank Ai Ching Lim for her support of the project.

Disclosure statement

All authors except for AH, RR, TPR, and JJG are current employees of Amgen. AH and RR were interns at Amgen. TPR is a former employee of Amgen.

Data availability statement

The source code for TherML (zero-shot, fine-tuned and supervised models) is available at https://github.com/AmeyaHarmalkar/therML for non-commercial use only. The experimental thermostability data and sequences are from internal antibody engineering studies and cannot be made available as the sequences are an intellectual property of Amgen. Any additional information required to reanalyze the data reported in this paper is available from the lead author upon request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2022.2163584

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

AH and JJG were partially supported by the NIH R35 GM141881.