3D deep convolutional neural networks for amino acid environment similarity analysis
Open accessAbstract
BACKGROUND: Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. RESULTS: Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. CONCLUSIONS: End-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses.
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3DCNNs can extract meaningful features of amino acid microenvironments directly from raw atom distribution.
“We have previously shown that 3DCNNs can extract meaningful features of amino acid microenvironments directly from raw atom distribution 12 .”
From Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (2019) · cites this paper as [12]
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- Authors
- Torng, W., & Altman, R. B.
- Affiliations
- Stanford University (US)
- Corresponding author
- Altman, Russ B.
- Venue
- BMC Bioinformatics
- Published
- 2017-06-14
- Volume
- 18
- Issue
- 1
- Pages
- 302-302
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC5472009
- OpenAlex
- W2625768267
- Cited by
- 142
- References
- 82
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 12 · 2023: 23 · 2022: 21 · 2021: 18 · 2020: 15 · 2019: 20 · 2018: 14
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (GM102365, LM05652, HL117798)
- 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 › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- 3D convolutional neural networks, protein microenvironment, protein structure representation, amino acid substitution matrices, T4 lysozyme, mutation prediction, structure-function relationship, end-to-end deep learning, feature visualization
- MeSH terms
- Amino Acids, Animals, Brain, Nerve Net, ROC Curve, In Situ Hybridization, Area Under Curve, Embryonic Development, Mice, Transcriptome
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