ArticleJournal of computational chemistry2011
Protein side chain modeling with orientation-dependent atomic force fields derived by series expansions.
Article in Journal of computational chemistry, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- Bioinformatic and computational biophysics tools for nanopore engineering: a review from standard approaches to machine learning advancements.Journal of nanobiotechnology · 2026Review
- To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.Briefings in bioinformatics · 2025Article
- GeoPacker: A novel deep learning framework for protein side-chain modeling.Protein science : a publication of the Protein Society · 2022Article
- OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.Briefings in bioinformatics · 2022Article
- Article
- Computational reconstruction of atomistic protein structures from coarse-grained models.Computational and structural biotechnology journal · 2020Review
- ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment.BMC bioinformatics · 2019Article
- Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics.PLoS computational biology · 2018Article
- Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking.Journal of computational chemistry · 2018Article
- Revisiting antibody modeling assessment for CDR-H3 loop.Protein engineering, design & selection : PEDS · 2016Article
- Protein structure prediction using residue- and fragment-environment potentials in CASP11.Proteins · 2016Article
- Assessment of protein side-chain conformation prediction methods in different residue environments.Proteins · 2014Article
- LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains.Journal of computational chemistry · 2014Article
- OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION.Journal of theoretical & computational chemistry · 2013Article
- Energy functions in de novo protein design: current challenges and future prospects.Annual review of biophysics · 2013Review
- Fast and accurate prediction of protein side-chain conformations.Bioinformatics (Oxford, England) · 2011Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
We describe the development of new force fields for protein side chain modeling called optimized side chain atomic energy (OSCAR). The distance-dependent energy functions (OSCAR-d) and side-chain dihedral angle potential energy functions were represented as power and Fourier series, respectively. The resulting 802 adjustable parameters were optimized by discriminating the native side chain conformations from non-native conformations, using a training set of 12,000 side chains for each residue type. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. Then, the OSCAR-d were multiplied by an orientation-dependent function to yield OSCAR-o. A total of 1087 parameters of the orientation-dependent energy functions (OSCAR-o) were optimized by maximizing the energy gap between the native conformation and subrotamers calculated as low energy by OSCAR-d. When OSCAR-o with optimized parameters were used to model side chain conformations simultaneously for 218 recently released protein structures, the prediction accuracies were 88.8% for χ(1) , 79.7% for χ(1 + 2) , 1.24 Å overall root mean square deviation (RMSD), and 0.62 Å RMSD for core residues, respectively, compared with the next-best performing side-chain modeling program which achieved 86.6% for χ(1) , 75.7% for χ(1 + 2) , 1.40 Å overall RMSD, and 0.86 Å RMSD for core residues, respectively. The continuous energy functions obtained in this study are suitable for gradient-based optimization techniques for protein structure refinement. A program with built-in OSCAR for protein side chain prediction is available for download at http://sysimm.ifrec.osaka-u.ac.jp/OSCAR/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.