From sequence to function through structure: Deep learning for protein design
The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve condit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022005086 |
| _version_ | 1827577737471066112 |
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| author | Noelia Ferruz Michael Heinzinger Mehmet Akdel Alexander Goncearenco Luca Naef Christian Dallago |
| author_facet | Noelia Ferruz Michael Heinzinger Mehmet Akdel Alexander Goncearenco Luca Naef Christian Dallago |
| author_sort | Noelia Ferruz |
| collection | DOAJ |
| description | The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field. |
| first_indexed | 2024-03-08T21:31:03Z |
| format | Article |
| id | doaj.art-551fa6fbd7244da18697eddc83db91c5 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:03Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-551fa6fbd7244da18697eddc83db91c52023-12-21T07:30:12ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-0121238250From sequence to function through structure: Deep learning for protein designNoelia Ferruz0Michael Heinzinger1Mehmet Akdel2Alexander Goncearenco3Luca Naef4Christian Dallago5Institute of Informatics and Applications, University of Girona, Girona, Spain; Department of Biochemistry, University of Bayreuth, Bayreuth, Germany; Corresponding authors at: Institute of Informatics and Applications, University of Girona, Girona, Spain (N. Ferruz). Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany (C. Dallago).Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, GermanyVantAI, 151 W 42nd Street, New York, NY 10036, United StatesVantAI, 151 W 42nd Street, New York, NY 10036, United StatesVantAI, 151 W 42nd Street, New York, NY 10036, United StatesDepartment of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany; VantAI, 151 W 42nd Street, New York, NY 10036, United States; NVIDIA DE GmbH, Einsteinstraße 172, 81677 München, Germany; Corresponding authors at: Institute of Informatics and Applications, University of Girona, Girona, Spain (N. Ferruz). Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany (C. Dallago).The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.http://www.sciencedirect.com/science/article/pii/S2001037022005086Protein designProtein predictionDrug discoveryDeep learningProtein language models |
| spellingShingle | Noelia Ferruz Michael Heinzinger Mehmet Akdel Alexander Goncearenco Luca Naef Christian Dallago From sequence to function through structure: Deep learning for protein design Computational and Structural Biotechnology Journal Protein design Protein prediction Drug discovery Deep learning Protein language models |
| title | From sequence to function through structure: Deep learning for protein design |
| title_full | From sequence to function through structure: Deep learning for protein design |
| title_fullStr | From sequence to function through structure: Deep learning for protein design |
| title_full_unstemmed | From sequence to function through structure: Deep learning for protein design |
| title_short | From sequence to function through structure: Deep learning for protein design |
| title_sort | from sequence to function through structure deep learning for protein design |
| topic | Protein design Protein prediction Drug discovery Deep learning Protein language models |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022005086 |
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