ArticleBiophysical journal2011
GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction.
Article in Biophysical journal, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers.
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149 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- A Step-by-Step Protocol for Efficient Global Accuracy Estimation of Protein Complex Structural Models with MViewEMA.Bio-protocol · 2026Article
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.Journal of chemical information and modeling · 2026Article
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.bioRxiv : the preprint server for biology · 2026Article
- Efficient global accuracy estimation for protein complex structural models using multi-view representation learning.Cell reports methods · 2026Article
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- Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.Journal of chemical information and modeling · 2025Article
- TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.Briefings in bioinformatics · 2025Article
- Genome-Wide Characterization and Expression Profiling of Phytosulfokine Receptor Genes (Genes · 2024Article
- A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.Biomolecules · 2024Review
- Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints.Journal of molecular biology · 2024Article
- Assessment of Protein-Protein Docking Models Using Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2024Review
- DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model.Bioinformatics advances · 2024Article
89 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
An accurate scoring function is a key component for successful protein structure prediction. To address this important unsolved problem, we develop a generalized orientation and distance-dependent all-atom statistical potential. The new statistical potential, generalized orientation-dependent all-atom potential (GOAP), depends on the relative orientation of the planes associated with each heavy atom in interacting pairs. GOAP is a generalization of previous orientation-dependent potentials that consider only representative atoms or blocks of side-chain or polar atoms. GOAP is decomposed into distance- and angle-dependent contributions. The DFIRE distance-scaled finite ideal gas reference state is employed for the distance-dependent component of GOAP. GOAP was tested on 11 commonly used decoy sets containing 278 targets, and recognized 226 native structures as best from the decoys, whereas DFIRE recognized 127 targets. The major improvement comes from decoy sets that have homology-modeled structures that are close to native (all within ∼4.0 Å) or from the ROSETTA ab initio decoy set. For these two kinds of decoys, orientation-independent DFIRE or only side-chain orientation-dependent RWplus performed poorly. Although the OPUS-PSP block-based orientation-dependent, side-chain atom contact potential performs much better (recognizing 196 targets) than DFIRE, RWplus, and dDFIRE, it is still ∼15% worse than GOAP. Thus, GOAP is a promising advance in knowledge-based, all-atom statistical potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP.
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