Transferable deep generative modeling of intrinsically disordered protein conformations Article Swipe
Giacomo Janson
,
Michael Feig
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.08.579522
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.08.579522
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning. AUTHOR SUMMARY Proteins are essential molecules in living organisms and some of them have highly dynamical structures, which makes understanding their biological roles challenging. Disordered proteins can be studied through a combination of computer simulations and experiments. Computer simulations are often resource-intensive. Recently, machine learning has been used to make this process more efficient. The strategy is to learn from previous simulations to model the heterogenous conformations of proteins. However, such methods still suffer from poor transferability, meaning that they tend to make incorrect predictions on proteins not seen in training data. In this study, we present idpSAM, a method based on generative artificial intelligence for modeling the structures of disordered proteins. The model was trained using a vast dataset and, thanks to its architecture and training procedure, it performs well on not just proteins in the training set but achieves high levels transferability to proteins unseen in training. This advancement is a step forward in modeling biologically relevant disordered proteins. It shows how the combination of generative modeling and large training sets and can aid us understand how dynamical proteins behave.
Related Topics
Concepts
Computer science
Artificial intelligence
Intrinsically disordered proteins
Machine learning
Similarity (geometry)
Generative model
Generative grammar
Representation (politics)
Artificial neural network
Set (abstract data type)
Chemistry
Politics
Law
Programming language
Biochemistry
Political science
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.02.08.579522
- https://www.biorxiv.org/content/biorxiv/early/2024/02/08/2024.02.08.579522.full.pdf
- OA Status
- green
- Cited By
- 6
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391684607
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391684607Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.02.08.579522Digital Object Identifier
- Title
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Transferable deep generative modeling of intrinsically disordered protein conformationsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-02-08Full publication date if available
- Authors
-
Giacomo Janson, Michael FeigList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.02.08.579522Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2024/02/08/2024.02.08.579522.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2024/02/08/2024.02.08.579522.full.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Intrinsically disordered proteins, Machine learning, Similarity (geometry), Generative model, Generative grammar, Representation (politics), Artificial neural network, Set (abstract data type), Chemistry, Politics, Law, Programming language, Biochemistry, Political science, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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76Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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