Rational Design of Next-Generation Therapeutic Antibodies Using Protein Engineering Tools

Khera, Harvinder Kour; Maity, Koustav

doi:10.1021/bk-2019-1329.ch006

Cited by 2 publications

(2 citation statements)

References 249 publications

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“…The ability to computationally engineer protein sequences has a wide range of applications from therapeutics (Fosgerau & Hoffmann, 2015;Khera & Maity, 2019), to vaccines (Li & Li, 2020;Liu et al, 2020;Malonis et al, 2020;X. Zhou et al, 2020), sensors (Karimzadeh et al, 2018;Merkx et al, 2019) or protein-based materials (Capezza et al, 2019;de la Rica & Matsui, 2010) and beyond that.…”

Section: Introductionmentioning

confidence: 99%

Deep learning of Protein Sequence Design of Protein-protein Interactions

Syrlybaeva

Strauch

2022

Preprint

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MotivationAs more data of experimentally determined protein structures is becoming available, data-driven models to describe protein sequence-structure relationship become more feasible. Within this space, the amino acid sequence design of protein-protein interactions has still been a rather challenging sub-problem with very low success rates - yet it is central for the most biological processes.ResultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments. These interaction fragments are derived from and represent core parts of protein-protein interfaces. Our trained model allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments. Here we demonstrate its potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces capture essential native interactions with high prediction accuracy and have native-like binding affinities. It further does not need precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.AvailabilityThe source code of the method is available at https://github.com/strauchlab/iNNterfaceDesignSupplementary informationSupplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Deep learning of Protein Sequence Design of Protein-protein Interactions

Syrlybaeva

Strauch

2022

Preprint

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“…The ability to computationally engineer protein sequences has a wide range of applications ranging from therapeutics ( Fosgerau and Hoffmann, 2015 ; Khera and Maity, 2019 ), to vaccines ( Li and Li, 2020 ; Liu et al , 2020 ; Malonis et al , 2020 ; Zhou et al , 2020 ), sensors ( Karimzadeh et al , 2018 ; Merkx et al , 2019 ) or protein-based materials ( Capezza et al , 2019 ; de la Rica and Matsui, 2010 ). While there has been progress toward designing protein folds, much improvement is needed for the redesign or de novo design of protein–protein interfaces (PPIFs).…”

Section: Introductionmentioning

confidence: 99%

Deep learning of protein sequence design of protein–protein interactions

Syrlybaeva

Strauch

2022

Bioinformatics

View full text Add to dashboard Cite

Motivation As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates – yet, it is central to most biological processes. Results We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein-interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions. Availability The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign Supplementary information Supplementary data is available at Bioinformatics online.

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Rational Design of Next-Generation Therapeutic Antibodies Using Protein Engineering Tools

Cited by 2 publications

References 249 publications

Deep learning of Protein Sequence Design of Protein-protein Interactions

Deep learning of Protein Sequence Design of Protein-protein Interactions

Deep learning of protein sequence design of protein–protein interactions

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