Improving de novo protein binder design with deep learning

Bennett, Nathaniel R.; Coventry, Brian; Goreshnik, Inna; Huang, Buwei; Allen, Aza; Vafeados, Dionne; Peng, Ying Po; Dauparas, Justas; Baek, Minkyung; Stewart, Lance; DiMaio, Frank; Munck, Steven De; Savvides, Savvas N.; Baker, David

doi:10.1038/s41467-023-38328-5

Cited by 122 publications

(73 citation statements)

References 40 publications

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“…On the other hand, using AlphaFold multimer, we performed folding predictions in the trimeric state to assess how strongly each sequence specified the proposed oligomer. We performed folding without MSA features and instead used the AlphaFoldInitialGuess variant to bias folding on the pose coordinates (see Methods, adopted from Bennett et al 2023) 49 . We found many of the AlphaFold predictions recapitulate the intended trimeric forms in a sequence dependent manner.…”

Section: Resultsmentioning

confidence: 99%

“…For all inference performed using the AlphaFoldInitialGuess model, the amino acid sequence was processed into arrays corresponding to AlphaFold sequence features, and structure coordinates were processed into arrays to be input as coordinate positions to the AlphaFoldInitialGuess class, which was derived from descriptions provided in Bennett et al 2023 49 . AlphaFold msa features were provided empty to the AlphaFold feature dictionary as described in 74 .…”

Section: Structure Predictionmentioning

confidence: 99%

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A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Meador,

Castells-Graells,

Aguirre

et al. 2023

Preprint

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Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while forde novointerface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

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Section: Resultsmentioning

confidence: 99%

Section: Structure Predictionmentioning

confidence: 99%

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Meador,

Castells-Graells,

Aguirre

et al. 2023

Preprint

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“…One promising area of research is of course the use of AI to predict structures as well as design and optimize proteins as antigens, antigen-binders, and/or antibodies. , …”

Section: Discussionmentioning

confidence: 99%

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

Guarra,

Colombo

2023

J. Chem. Theory Comput.

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The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

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“…Tasks that were once demanding to machines, e.g. , retrosynthetic planning, 5–7 molecular design, 8–10 prediction of protein structure and function, 11–13 prediction of biological activity 14–16 and others, 17–19 have now become significantly more attainable and generalizable, providing valuable assistance to bench chemists. 20 ML workflows and pipelines are primarily implemented to accelerate chemical research, with the scope of prioritizing experiments with high confidence and likelihood of success, while minimizing exploration towards pre-established objectives.…”

Section: Introductionmentioning

confidence: 99%