Computational and artificial intelligence-based methods for antibody development

Trends in Pharmacological Sciences, Journal Year: 2023, Volume and Issue: 44(3), P. 175 - 189

Published: Jan. 18, 2023

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature robust, cumbersome has significant limitations. Substantial recent advances in computational artificial intelligence (AI) technologies now starting overcome many these limitations increasingly integrated into pipelines. Here, we provide an overview AI methods relevant for development, including databases, predictors properties structure, design with emphasis on machine learning (ML) models, complementarity-determining region (CDR) loops, structural components critical binding.

Language: Английский

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In vivo hypermutation and continuous evolution

Nature Reviews Methods Primers, Journal Year: 2022, Volume and Issue: 2(1)

Published: May 19, 2022

Directed evolution has revolutionized biomolecular engineering by applying cycles of mutation, amplification and selection to genes interest (GOIs). However, classical directed methods that rely on manually staged evolutionary constrain the scale depth search is possible. We describe genetic systems achieve rapid fully inside living cells, enabling continuous GOIs as cells grow. These advance scale, depth, ease overall power access important new areas protein engineering. In vivo a form takes advantage cells. Molina, Rix et al. discuss best practices for designing conducting experiments drug discovery, enzyme fluorescence-activated cell sorting (FACS)-based evolution.

Language: Английский

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De novo design of high-affinity binders of bioactive helical peptides

Nature, Journal Year: 2023, Volume and Issue: 626(7998), P. 435 - 442

Published: Dec. 18, 2023

Many peptide hormones form an α-helix on binding their receptors

Language: Английский

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Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

mAbs, Journal Year: 2022, Volume and Issue: 14(1)

Published: March 16, 2022

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, design discovery new candidates remain a time cost-intensive endeavor. In this regard, progress in generation data describing antigen binding developability, computational methodology, artificial intelligence may pave way for era silico on-demand immunotherapeutics discovery. Here, we argue that main necessary machine learning (ML) components an mAb sequence generator are: understanding rules mAb-antigen binding, capacity to modularly combine parameters, algorithms unconstrained parameter-driven synthesis. We review current toward realization these discuss challenges must be overcome allow ML-based fit-for-purpose candidates.

Language: Английский

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Game changers in science and technology - now and beyond

Technological Forecasting and Social Change, Journal Year: 2023, Volume and Issue: 193, P. 122588 - 122588

Published: May 4, 2023

The recent devastating pandemic has drastically reminded humanity of the importance constant scientific and technological progress. A strong interdisciplinary dialogue between academic industrial scientists various specialties, entrepreneurs, managers public is paramount in triggering new breakthrough ideas which often emerge at interface disciplines. following sections, compiled by a highly diverse group authors, are summarizing recently achieved game-changing leaps science technology. game-changers range from paradigm shifts theories to make impact over several decades that have potential change our everyday lives tomorrow. paper an relevance for thinkers, large corporations' strategic planners, top executives alike; it provides glimpse into what further breakthroughs future may hold thereby intends spark with its readers.

Language: Английский

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