The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
OPUS-Rota4: A Gradient-Based Protein Side-Chain Modeling Framework Assisted by Deep Learning-Based Predictors
Open accessPreprint — published as 10.1093/bib/bbab529 · Paper page
Abstract
Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs; and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. In summary, OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the constraints derived from OPUS-RotaCM. In this case, we convert the protein side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms into its side-chain modeling procedure. In other words, OPUS-Rota4 provides a platform in which the protein side-chain conformation can be dynamically adjusted under the influence of other processes, such as protein-protein interaction. We apply OPUS-Rota4 on 15 FM predictions submitted by Alphafold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than the side chains predicted by Alphafold2.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 36
- 2019, OPUS-Rota2: An Improved Fast and Accurate Side-Chain Modeling Method, Journal of Chemical Theory and Computation, 15, 5154, 10.1021/acs.jctc.9b00309 · DOI
- 10.1110/ps.035022.108 · DOI
- 2020, OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods, J Chem Inf Model, 60, 6691, 10.1021/acs.jcim.0c00951 · DOI
- 10.1093/bioinformatics/btr482 · DOI
- 10.1002/prot.22488 · DOI
- 10.1093/bioinformatics/btr009 · DOI
- 10.1002/jcc.21747 · DOI
- 10.1093/bioinformatics/btr538 · DOI
- 10.1002/prot.23170 · DOI
- 10.1093/bioinformatics/btaa234 · DOI
- Misiura, M. ; Shroff, R. ; Thyer, R. ; Kolomeisky, A. B. , DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins. bioRxiv 2021, 2021.05.23.445347. · DOI
- 2020, OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks, Bioinformatics, 36, 5021, 10.1093/bioinformatics/btaa629 · DOI
- 10.1371/journal.pcbi.1005324 · DOI · Paper page
- 10.1073/pnas.1914677117 · DOI · Paper page
- 2016, Protein side-chain packing problem: is there still room for improvement, Briefings in Bioinformatics, 18, 1033
- 10.1107/S0907444998003254 · DOI
- 10.1016/S0076-6879(04)83004-0 · DOI
- 10.1093/bioinformatics/btq007 · DOI
- Xu, G. ; Wang, Q. ; Ma, J. , OPUS-X: An Open-Source Toolkit for Protein Torsion Angles, Secondary Structure, Solvent Accessibility, Contact Map Predictions, and 3D Folding. bioRxiv 2021, 2021.05.08.443219. · DOI
- 2019, Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks, Bioinformatics, 35, 2403, 10.1093/bioinformatics/bty1006 · DOI
- 10.1016/j.jmb.2007.11.033 · DOI
- 2017, OPUS-DOSP: A Distance- and Orientation-Dependent All-Atom Potential Derived from Side-Chain Packing, J Mol Biol, 429, 3113, 10.1016/j.jmb.2017.08.013 · DOI
- 10.1002/bip.360221211 · DOI · Paper page
- 2018, OPUS-CSF: A C-atom-based scoring function for ranking protein structural models, Protein Sci, 27, 286, 10.1002/pro.3327 · DOI
- Ronneberger, O. ; Fischer, P. ; Brox, T. , U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention 2015, 234–241. · DOI · Paper page
- Vaswani, A. ; Shazeer, N. ; Parmar, N. ; Uszkoreit, J. ; Jones, L. ; Gomez, A. N. ; Kaiser, Ł. ; Polosukhin, I. , Attention is all you need. Advances in neural information processing systems 2017, 5998–6008.
- He, K. M. ; Zhang, X. Y. ; Ren, S. Q. ; Sun, J. , Deep Residual Learning for Image Recognition. Proc Cvpr Ieee 2016, 770–778. · DOI · Paper page
- 10.1162/neco.1997.9.8.1735 · DOI · Paper page
- 10.1110/ps.0217002 · DOI · Paper page
- Abadi, M. ; Barham, P. ; Chen, J. M. ; Chen, Z. F. ; Davis, A. ; Dean, J. ; Devin, M. ; Ghemawat, S. ; Irving, G. ; Isard, M. ; Kudlur, M. ; Levenberg, J. ; Monga, R. ; Moore, S. ; Murray, D. G. ; Steiner, B. ; Tucker, P. ; Vasudevan, V. ; Warden, P. ; Wicke, M. ; Yu, Y. ; Zheng, X. Q. , TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation 2016, 265–283.
- Kingma, D. P. ; Ba, J. , Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations 2015.
- 10.1093/bioinformatics/btg224 · DOI
- Uddin, M. R. ; Mahbub, S. ; Rahman, M. S. ; Bayzid, M. S. , SAINT: Self-Attention Augmented Inception-Inside-Inception Network Improves Protein Secondary Structure Prediction. Bioinformatics 2020. · DOI
- 2018, Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12, Proteins, 86, 387
- 10.1002/prot.20264 · DOI · Paper page
- 10.1093/bioinformatics/btp163 · DOI · Paper page
Metadata
- Authors
- Xu, G., Wang, Q., & Ma, J.
- Affiliations
- Fudan University (CN)
- Baylor College of Medicine (US)
- Corresponding author
- Ma, Jianpeng
- Venue
- Briefings in Bioinformatics
- Publisher
- openRxiv
- Published
- 2021-07-23
- Volume
- 23
- Issue
- 1
- ISSN
- 1467-5463 (ISSN-L)
- PMC
- PMC8769891
- OpenAlex
- W4220891150
- Cited by
- 24
- References
- 1
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 10 · 2022: 4 · 2021: 1
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Welch Foundation (Q‐1512, Q‐1826)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein folding, protein design, CASP14
- MeSH terms
- Deep Learning, Cell Differentiation, Models, Molecular, Protein Conformation, Proteins, Gene Library, Protein Folding, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
