Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: April 25, 2023

Abstract Antibodies have the capacity to bind a diverse set of antigens, and they become critical therapeutics diagnostic molecules. The binding antibodies is facilitated by six hypervariable loops that are diversified through genetic recombination mutation. Even with recent advances, accurate structural prediction these remains challenge. Here, we present IgFold, fast deep learning method for antibody structure prediction. IgFold consists pre-trained language model trained on 558 million natural sequences followed graph networks directly predict backbone atom coordinates. predicts structures similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate this timescale makes possible avenues investigation were previously infeasible. As demonstration IgFold’s capabilities, predicted 1.4 paired sequences, providing insights 500-fold more experimentally determined structures.

Language: Английский

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ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins

Communications Biology, Journal Year: 2023, Volume and Issue: 6(1)

Published: May 29, 2023

Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, set deep learning models trained to accurately predict antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state art accuracy while being far faster than AlphaFold2. For example, on benchmark 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops an RMSD 2.81Å, 0.09Å improvement over AlphaFold-Multimer, hundred times faster. Similar results are also achieved nanobodies, (NanoBodyBuilder2 average 2.89Å, 0.55Å AlphaFold2) TCRs. By predicting ensemble structures, gives error estimate every residue its final prediction. made freely available, both download ( https://github.com/oxpig/ImmuneBuilder ) use via our webserver http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred ). make available structural ~150 thousand non-redundant paired antibody sequences https://doi.org/10.5281/zenodo.7258553

Language: Английский

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BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

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

Published: Feb. 8, 2022

Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one the main routes for therapeutic antibody development. Traditionally, is manual, laborious, requires expert knowledge. Although automation efforts are advancing, existing methods either demonstrated on a small scale or entirely proprietary. To predict immunogenicity risk, human-likeness can be evaluated using humanness scores, but these lack diversity, granularity interpretability. Meanwhile, immune repertoire sequencing has generated rich libraries such as Observed Antibody Space (OAS) that offer augmented diversity not yet exploited engineering. Here we present BioPhi, an open-source platform featuring novel (Sapiens) evaluation (OASis). Sapiens deep learning method trained OAS language modeling. Based

Language: Английский

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Deciphering the language of antibodies using self-supervised learning

Patterns, Journal Year: 2022, Volume and Issue: 3(7), P. 100513 - 100513

Published: May 18, 2022

An individual's B cell receptor (BCR) repertoire encodes information about past immune responses and potential for future disease protection. Deciphering the stored in BCR sequence datasets will transform our understanding of enable discovery novel diagnostics antibody therapeutics. A key challenge analysis is prediction properties from their amino acid alone. Here, we present an antibody-specific language model, Antibody-specific Bidirectional Encoder Representation Transformers (AntiBERTa), which provides a contextualized representation sequences. Following pre-training, show that AntiBERTa embeddings capture biologically relevant information, generalizable to range applications. As case study, fine-tune predict paratope positions sequence, outperforming public tools across multiple metrics. To knowledge, deepest protein-family-specific providing rich BCRs. are primed downstream tasks can improve antibodies.

Language: Английский

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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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Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning

Nature Biotechnology, Journal Year: 2023, Volume and Issue: 41(12), P. 1810 - 1819

Published: March 20, 2023

While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or which few homologous sequences are known. Here we introduce AlphaLink, a modified version of algorithm incorporates experimental distance restraint information into its network architecture. By employing sparse contacts as anchor points, AlphaLink improves on performance in predicting challenging targets. We confirm this experimentally by using noncanonical amino acid photo-leucine to obtain residue-residue inside cells crosslinking mass spectrometry. The program distinct conformations basis restraints provided, demonstrating value data driving structure prediction. noise-tolerant framework integrating prediction presented here opens path characterization in-cell data.

Language: Английский

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Development of therapeutic antibodies for the treatment of diseases

Molecular Biomedicine, Journal Year: 2022, Volume and Issue: 3(1)

Published: Nov. 22, 2022

Abstract Since the first monoclonal antibody drug, muromonab-CD3, was approved for marketing in 1986, 165 drugs have been or are under regulatory review worldwide. With approval of new treating a wide range diseases, including cancer and autoimmune metabolic disorders, therapeutic drug market has experienced explosive growth. Monoclonal antibodies sought after by many biopharmaceutical companies scientific research institutes due to their high specificity, strong targeting abilities, low toxicity, side effects, development success rate. The related industries markets growing rapidly, one most important areas field biology medicine. In recent years, great progress made key technologies theoretical innovations provided antibodies, antibody–drug conjugates, antibody-conjugated nuclides, bispecific nanobodies, other analogs. Additionally, can be combined with used fields create cross-fields, such as chimeric antigen receptor T cells (CAR-T), CAR-natural killer (CAR-NK), cell therapy. This summarizes latest that worldwide, well clinical on these approaches development, outlines discovery strategies emerged during hybridoma technology, phage display, preparation fully human from transgenic mice, single B-cell artificial intelligence-assisted discovery.

Language: Английский

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Assessing developability early in the discovery process for novel biologics

mAbs, Journal Year: 2023, Volume and Issue: 15(1)

Published: Feb. 23, 2023

Beyond potency, a good developability profile is key attribute of biological drug. Selecting and screening for such attributes early in the drug development process can save resources avoid costly late-stage failures. Here, we review some most important properties that be assessed on biologics. These include influence source biologic, its biophysical pharmacokinetic properties, how well it expressed recombinantly. We furthermore present silico, vitro, vivo methods techniques exploited at different stages discovery to identify molecules with liabilities thereby facilitate selection optimal leads. Finally, reflect relevant parameters injectable versus orally delivered biologics provide an outlook toward what general trends are expected rise

Language: Английский

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Nanobodies: Robust miniprotein binders in biomedicine

Advanced Drug Delivery Reviews, Journal Year: 2023, Volume and Issue: 195, P. 114726 - 114726

Published: Feb. 7, 2023

Language: Английский

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Development and use of machine learning algorithms in vaccine target selection

npj Vaccines, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 20, 2024

Computer-aided discovery of vaccine targets has become a cornerstone rational design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in design concerned with the identification B T cell epitopes correlates protection. provide examples ML models, as well types data predictions for which they are built. argue that interpretable potential to improve immunogens also tool scientific discovery, by helping elucidate molecular processes underlying vaccine-induced immune responses. outline limitations challenges terms availability method development need be addressed bridge gap between advances their translational application

Language: Английский

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