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Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
Open accessPreprint — published as 10.1371/journal.pcbi.1010561 · Paper page
Abstract
Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target molecule of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. With the increasing amount of such high-throughput experimental data available, machine learning techniques have become increasingly popular for molecular datasets analysis. Here, we show that Restricted Boltzmann Machines (RBMs), a two-layer neural network architecture, can successfully be trained on sequence ensembles from SELEX experiments for thrombin aptamers, and used to estimate the fitness of the sequences obtained through the experimental protocol. As a direct consequence, we show that trained RBMs can be exploited to classify as well as generate novel molecules. To confirm our findings, we experimentally verify the generated sequences from RBM.
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Metadata
- Authors
- Di Gioacchino, A., Procyk, J., Molari, M., Schreck, J. S., Zhou, Y., Liu, Y., Monasson, R., Cocco, S., & Šulc, P.
- Affiliations
- Arizona State University (US)
- SIB Swiss Institute of Bioinformatics (CH)
- NSF National Center for Atmospheric Research (US)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Šulc, Petr
- ORCID
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2022-03-14
- Volume
- 18
- Issue
- 9
- Pages
- e1010561-e1010561
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC9553063
- OpenAlex
- W4297990198
- Cited by
- 45
- References
- 1
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 7 · 2023: 8 · 2022: 2
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (TG-BIO210009, 2155095)
- Arizona State University
- European Commission (101026293, HORIZON2020)
- Agence Nationale de la Recherche (ANR-17-CE30-0021, RBMPro CE30-0021-01 and ANR-19 Decrypted CE30-0021-01, ANR-19-CE30-0021)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Mathematics › Theoretical Computer Science › DNA and Biological Computing
- Keywords
- aptamer design, SELEX, restricted Boltzmann machine, thrombin-binding aptamer, interpretability, generative models, disruptive mutations, gel shift assay
- MeSH terms
- Machine Learning, Thrombin, Neural Networks, Computer
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