ArticlePloS one2011
Identification of ligand binding sites of proteins using the Gaussian Network Model.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- Comparative effects of oncogenic mutations G12C, G12V, G13D, and Q61H on local conformations and dynamics of K-Ras.Computational and structural biotechnology journal · 2020Article
- Interpreting the Dynamics of Binding Interactions of snRNA and U1A Using a Coarse-Grained Model.Biophysical journal · 2019Article
- Gaussian network model can be enhanced by combining solvent accessibility in proteins.Scientific reports · 2017Article
- Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes.BioMed research international · 2016Article
- Article
- Sequence-based Gaussian network model for protein dynamics.Bioinformatics (Oxford, England) · 2014Article
- A quantitative measure of electrostatic perturbation in holo and apo enzymes induced by structural changes.PloS one · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem. Comparison with known structures shows that the predictive capability of the method is significant.
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Registered trials
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