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Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

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Abstract

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

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Abbreviations

Abs:

Antibodies

AI:

Artificial intelligence

NGS:

Next-generation sequencing

CDR:

Complementary determining region

BCR:

B-cell receptor

HER2:

Human epidermal growth factor receptor 2

AbLang:

Antibody language

AbRep:

Antibody representation

AbHead:

Antibody head model

AntBO:

Antibody design based on a Bayesian Optimization framework

AbMap:

Antibody binding epitope mapping

AntiBERTa:

Antibody-specific Bidirectional Encoder Representation from Transformers

AbMap:

Antibody binding epitope Mapping

OptMAVEn:

Optimal Method for Antibody Variable region Engineering

OptCDR:

Optimal CDR

AbDesign:

Antibody design

RAbD:

Rosetta Ab design

2D:

Two-dimensional

3D:

Three-dimensional

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Acknowledgements

The author would like to thank Peter Olson and Janet Stewart for reviewing of this article and providing detailed suggestions.

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This work was partially supported by RevivAb Educational Advancement Grant MT-021.

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  1. Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada

    Eugenio Gallo

  2. RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil

    Eugenio Gallo

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Gallo, E. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-024-01064-2

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