Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries

Li, Lin; Gupta, Esther; Spaeth, John; Shing, Leslie; Jaimes, Rafael; Engelhart, Emily; Lopez, Randolph; Caceres, Rajmonda S.; Bepler, Tristan; Walsh, Matthew E.

doi:10.1038/s41467-023-39022-2

Cited by 23 publications

(12 citation statements)

References 44 publications

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“…Therefore, the algorithm is the key to nanobody library design. Most recently, Li et al have shown the power of artificial intelligence (AI) to learn the features of natural antibodies, which significantly improve the affinity maturation of single‐chain variable fragments in vitro (Li et al, 2023). The uprising of AI is promising to develop sophisticated models for the library design of nanobodies.…”

Section: Discussionmentioning

confidence: 99%

Sequence‐based design and construction of synthetic nanobody library

Liu,

Li,

et al. 2024

Biotech & Bioengineering

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Nanobody (Nb), the smallest antibody fragments known to bind antigens, is now widely applied to various studies, including protein structure analysis, bioassay, diagnosis, and biomedicine. The traditional approach to generating specific nanobodies involves animal immunization which is time‐consuming and expensive. As the understanding of the antibody repertoire accumulation, the synthetic library, which is devoid of animals, has attracted attention widely in recent years. Here, we describe a synthetic phage display library (S‐Library), designed based on the systematic analysis of the next‐generation sequencing (NGS) of nanobody repertoire. The library consists of a single highly conserved scaffold (IGHV3S65*01‐IGHJ4*01) and complementary determining regions of constrained diversity. The S‐Library containing 2.19 × 108 independent clones was constructed by the one‐step assembly and rapid electro‐transformation. The S‐Library was screened against various targets (Nb G8, fusion protein of Nb G8 and green fluorescent protein, bovine serum albumin, ovalbumin, and acetylcholinesterase). In comparison, a naïve library (N‐Library) from the source of 13 healthy animals was constructed and screened against the same targets as the S‐Library. Binders were isolated from both S‐Library and N‐Library. The dynamic affinity was evaluated by the biolayer interferometry. The data confirms that the feature of the Nb repertoire is conducive to reducing the complexity of library design, thus allowing the S‐Library to be built on conventional reagents and primers.

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Section: Discussionmentioning

confidence: 99%

Sequence‐based design and construction of synthetic nanobody library

Liu,

Li,

et al. 2024

Biotech & Bioengineering

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“…coupled LLMs for feature representation with BO to navigate a machine learning-derived fitness landscape, leading to the design of high-affinity scFv libraries. 122 Finally, LaMBO2 integrates two approaches (NOS for the generation of protein sequences and LaMBO for multiple objective BO with edit-based constraints) to improve antibody expression and binding affinity while maintaining developability. 123 , 124 Collectively, these methods demonstrate that BO is a powerful tool for designing optimized building blocks of single-domain antibodies, which can be subsequently employed in the construction of multi-specific VHHs with enhanced therapeutic properties.…”

Section: Computational Approaches To Vhh Lead Optimizationmentioning

confidence: 99%

Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions

Mullin,

McClory,

Haynes

et al. 2024

mAbs

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The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.

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“…Li et al [15] used a pre-trained LLM, Gaussian Processes (GPs), and ensemble regression models to design and screen new high-affinity single-chain fragment variable antibodies (scFvs) against a conserved coronavirus peptide. They trained their models on experimental data (26.5k heavy and 26.2k light chain sequences) that involved up to three random CDR mutations from an initial candidate.…”

Section: Introductionmentioning

confidence: 99%

Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond

Chinery,

Hummer,

Mehta

et al. 2024

Preprint

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There is currently considerable interest in the field of de novo antibody design, and deep learning techniques are now regularly applied to optimise antibody properties such as binding affinity. However, robust baselines within this field have not kept up with recent developments. In this study, we generate a dataset of over 524,000 Trastuzumab variants and use this to show that standard computational methods such as BLOSUM, AbLang, ESM, and ProteinMPNN can be used to design diverse antibody libraries from just a single starting sequence. These novel libraries are predicted to be enriched in binding variants and experimental validation of 700 of these designs is ongoing. We also demonstrate that, even with only a very small number of experimental data points, simple machine learning classifiers can be trained in seconds to accurately pre-screen future designs. This pre-screening maintains library diversity and saves experimental time and money.

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Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries

Cited by 23 publications

References 44 publications

Sequence‐based design and construction of synthetic nanobody library

Sequence‐based design and construction of synthetic nanobody library

Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions

Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond

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