DLPacker : Deep learning for prediction of amino acid side chain conformations in proteins
Open accessPublished version of a preprint
Abstract
Prediction of side chain conformations of amino acids in proteins (also termed “packing”) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image‐to‐image transformation and train a U‐net style DNN to solve the problem. We show that our method outperforms other physics‐based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.
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- Authors
- Misiura, M., Shroff, R., Thyer, R., & Kolomeisky, A. B.
- Affiliations
- Center for Theoretical Biological Physics (US)
- United States Army Combat Capabilities Development Command (US)
- Rice University (US)
- Corresponding author
- Misiura, Mikita
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2022-02-22
- Volume
- 90
- Issue
- 6
- Pages
- 1278-1290
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W4220933043
- Cited by
- 51
- References
- 9
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 11 · 2023: 10 · 2022: 13 · 2021: 1
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Rice Research Repository (Rice University) (submittedVersion)
- Funders
- National Science Foundation of Sri Lanka (PHY‐2019745)
- 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
- protein packing, deep neural network, U-Net, image-to-image translation, protein structure prediction, protein design
- MeSH terms
- Deep Learning, Amino Acids, Protein Conformation, Proteins, Neural Networks, Computer
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