Lattice protein folding with variational annealing
Understanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental biological processes. Lattice protein folding models offer a simplified yet powerful framework for studying the complexit...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adf376 |
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| _version_ | 1849396926682759168 |
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| author | Shoummo A Khandoker Estelle M Inack Mohamed Hibat-Allah |
| author_facet | Shoummo A Khandoker Estelle M Inack Mohamed Hibat-Allah |
| author_sort | Shoummo A Khandoker |
| collection | DOAJ |
| description | Understanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental biological processes. Lattice protein folding models offer a simplified yet powerful framework for studying the complexities of protein folding, enabling the exploration of energetically optimal folds under constrained conditions. However, finding these optimal folds is a computationally challenging combinatorial optimization problem. In this work, we introduce a novel upper-bound training scheme that employs masking to identify the lowest-energy folds in two-dimensional hydrophobic-polar lattice protein folding. By leveraging dilated recurrent neural networks (RNNs) integrated with an annealing process driven by temperature-like fluctuations, our method accurately predicts optimal folds for benchmark systems of up to 60 beads. Our approach also effectively masks invalid folds from being sampled without compromising the autoregressive sampling properties of RNNs. This scheme is generalizable to three spatial dimensions and can be extended to lattice protein models with larger alphabets. Our findings emphasize the potential of advanced machine learning techniques in tackling complex protein folding problems and a broader class of constrained combinatorial optimization challenges. |
| format | Article |
| id | doaj-art-96a8df40809f411dad78cbdea73a71bd |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-96a8df40809f411dad78cbdea73a71bd2025-08-20T03:39:11ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303502310.1088/2632-2153/adf376Lattice protein folding with variational annealingShoummo A Khandoker0https://orcid.org/0000-0001-7367-4485Estelle M Inack1https://orcid.org/0000-0002-4672-5512Mohamed Hibat-Allah2https://orcid.org/0000-0002-5298-8589Department of Computer Science , Indiana University Bloomington, Bloomington, IN 47405, United States of AmericaPerimeter Institute for Theoretical Physics , Waterloo, Ontario, Canada; yiyaniQ , Toronto, Ontario, Canada; Department of Physics and Astronomy, University of Waterloo , Waterloo, Ontario, CanadaPerimeter Institute for Theoretical Physics , Waterloo, Ontario, Canada; Department of Applied Mathematics, University of Waterloo , Waterloo, Ontario, Canada; Vector Institute , Toronto, Ontario, CanadaUnderstanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental biological processes. Lattice protein folding models offer a simplified yet powerful framework for studying the complexities of protein folding, enabling the exploration of energetically optimal folds under constrained conditions. However, finding these optimal folds is a computationally challenging combinatorial optimization problem. In this work, we introduce a novel upper-bound training scheme that employs masking to identify the lowest-energy folds in two-dimensional hydrophobic-polar lattice protein folding. By leveraging dilated recurrent neural networks (RNNs) integrated with an annealing process driven by temperature-like fluctuations, our method accurately predicts optimal folds for benchmark systems of up to 60 beads. Our approach also effectively masks invalid folds from being sampled without compromising the autoregressive sampling properties of RNNs. This scheme is generalizable to three spatial dimensions and can be extended to lattice protein models with larger alphabets. Our findings emphasize the potential of advanced machine learning techniques in tackling complex protein folding problems and a broader class of constrained combinatorial optimization challenges.https://doi.org/10.1088/2632-2153/adf376lattice protein foldingconstrained combinatorial optimizationrecurrent neural networksautoregressive modelsvariational annealing |
| spellingShingle | Shoummo A Khandoker Estelle M Inack Mohamed Hibat-Allah Lattice protein folding with variational annealing Machine Learning: Science and Technology lattice protein folding constrained combinatorial optimization recurrent neural networks autoregressive models variational annealing |
| title | Lattice protein folding with variational annealing |
| title_full | Lattice protein folding with variational annealing |
| title_fullStr | Lattice protein folding with variational annealing |
| title_full_unstemmed | Lattice protein folding with variational annealing |
| title_short | Lattice protein folding with variational annealing |
| title_sort | lattice protein folding with variational annealing |
| topic | lattice protein folding constrained combinatorial optimization recurrent neural networks autoregressive models variational annealing |
| url | https://doi.org/10.1088/2632-2153/adf376 |
| work_keys_str_mv | AT shoummoakhandoker latticeproteinfoldingwithvariationalannealing AT estelleminack latticeproteinfoldingwithvariationalannealing AT mohamedhibatallah latticeproteinfoldingwithvariationalannealing |