ArticleStructure (London, England : 1993)2012
A position-specific distance-dependent statistical potential for protein structure and functional study.
Article in Structure (London, England : 1993), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Journal research data policies in materials science.Digital discovery · 2026Review
- A dual diffusion model-based representation learning framework for antimicrobial peptides classification.Bioinformatics (Oxford, England) · 2026Article
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Structure-conditioned amino-acid couplings: How contact geometry affects pairwise sequence preferences.Protein science : a publication of the Protein Society · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
- Improved protein structure prediction by deep learning irrespective of co-evolution information.Nature machine intelligence · 2021Article
- Deep template-based protein structure prediction.PLoS computational biology · 2021Article
- Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8.Protein science : a publication of the Protein Society · 2021Article
- Representations of protein structure for exploring the conformational space: A speed-accuracy trade-off.Computational and structural biotechnology journal · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
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- AlphaFold at CASP13.Bioinformatics (Oxford, England) · 2019Article
- Distance-based protein folding powered by deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment.BMC bioinformatics · 2019Article
- Protein threading using residue co-variation and deep learning.Bioinformatics (Oxford, England) · 2018Article
- From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction.Molecules (Basel, Switzerland) · 2018Article
- Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations.Scientific reports · 2017Article
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2 authors.
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Abstract
Although studied extensively, designing highly accurate protein energy potential is still challenging. A lot of knowledge-based statistical potentials are derived from the inverse of the Boltzmann law and consist of two major components: observed atomic interacting probability and reference state. These potentials mainly distinguish themselves in the reference state and use a similar simple counting method to estimate the observed probability, which is usually assumed to correlate with only atom types. This article takes a rather different view on the observed probability and parameterizes it by the protein sequence profile context of the atoms and the radius of the gyration, in addition to atom types. Experiments confirm that our position-specific statistical potential outperforms currently the popular ones in several decoy discrimination tests. Our results imply that, in addition to reference state, the observed probability also makes energy potentials different and evolutionary information greatly boost performance of energy potentials.
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