ArticleBMC bioinformatics2019
ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- R3J-AGNN: GNN-Based Prediction of Inter-Branch Angles in RNA Three-Way Junctions from Secondary Structure.Biology · 2026Article
- Making Use of Averaging Methods in MODELLER for Protein Structure Prediction.International journal of molecular sciences · 2024Article
- RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures.Frontiers in bioinformatics · 2021Article
- An information gain-based approach for evaluating protein structure models.Computational and structural biotechnology journal · 2020Article
- Identification of native protein structures captured by principal interactions.BMC bioinformatics · 2019Article
- A simple neural network implementation of generalized solvation free energy for assessment of protein structural models.RSC advances · 2019Article
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4 authors.
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Abstract
backgroundThe knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials.
resultsWe developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, "effective atomic interaction" is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination.
conclusionsDistance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/ .
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