Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering

Minot, Mason; Reddy, Sai T.

doi:10.1016/j.cels.2023.12.003

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…They have been used to extract protein sequence representations [3,[74][75][76], for finetuning on the low-N function data [76][77][78], and to generate auxiliary training data in more complex models [78][79][80]. Other computational strategies for addressing the low-N problem include Gaussian processes [75,81,82], augmenting regression models with sequence-based [15,83] or structure-based [84] scores, custom protein 10/45 representations that can produce pretraining data [85], representations of proteins' 3D shape [86], meta learning [87], and contrastive finetuning [88].…”

Section: Discussionmentioning

confidence: 99%

Biophysics-based protein language models for protein engineering

Gelman,

Johnson,

Freschlin

et al. 2024

Preprint

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Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose Mutational Effect Transfer Learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure, and energetics. We finetune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity, and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.

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Section: Discussionmentioning

confidence: 99%

Biophysics-based protein language models for protein engineering

Gelman,

Johnson,

Freschlin

et al. 2024

Preprint

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Advancements in mammalian display technology for therapeutic antibody development and beyond: current landscape, challenges, and future prospects

Slavny,

Hegde,

Doerner

et al. 2024

Front. Immunol.

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The evolving development landscape of biotherapeutics and their growing complexity from simple antibodies into bi- and multi-specific molecules necessitates sophisticated discovery and engineering platforms. This review focuses on mammalian display technology as a potential solution to the pressing challenges in biotherapeutic development. We provide a comparative analysis with established methodologies, highlighting key aspects of mammalian display technology, including genetic engineering, construction of display libraries, and its pivotal role in hit selection and/or developability engineering. The review delves into the mechanisms underpinning developability-driven selection via mammalian display and their broader implications. Applications beyond antibody discovery are also explored, alongside advancements towards function-first screening technologies, precision genome engineering and AI/ML-enhanced libraries, situating them in the context of mammalian display. Overall, the review provides a comprehensive overview of the current mammalian display technology landscape, underscores the expansive potential of the technology for biotherapeutic development, addresses the critical challenges for the full realisation of this potential, and examines advances in related disciplines that might impact the future application of mammalian display technologies.

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Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering

Cited by 2 publications

References 40 publications

Biophysics-based protein language models for protein engineering

Biophysics-based protein language models for protein engineering

Advancements in mammalian display technology for therapeutic antibody development and beyond: current landscape, challenges, and future prospects

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