Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks
Open accessAbstract
While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction.
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Machine learning-based structure prediction methods explicitly incorporate constraints on protein geometries.
“significant work has been done in the opposite direction of machine learning-based structure prediction methods that explicitly incorporate constraints on protein geometries 3,5,26,87-89.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [3]
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- Authors
- Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Harvard University (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Berger, Bonnie
- Peng, Jian
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 6
- Issue
- 1
- Pages
- 65-74.e3
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC5808454
- OpenAlex
- W2780845733
- Cited by
- 114
- References
- 63
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 11 · 2023: 9 · 2022: 10 · 2021: 26 · 2020: 15 · 2019: 31 · 2018: 5
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- NCSA
- Sloan Research Fellowship
- NSF (1652815)
- NIH (R01GM081871)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- evolutionary coupling, residue-residue contact prediction, convolutional neural networks, multiple sequence alignment, protein structure prediction, contact probability
- MeSH terms
- Machine Learning, Algorithms, Animals, Forecasting, Humans, Models, Molecular, Probability, Protein Conformation, Proteins, Sequence Alignment, Neural Networks, Computer, Protein Folding, Computational Biology, Databases, Protein
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