title: Hot spots in a network of functional sites.
abstract: It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.
authors.affiliation: Department of Bioengineering, Marmara University, Goztepe, Istanbul, Turkey.
authors.forename: Pemra
authors.initials: P
authors.lastname: Ozbek
authors.forename: Seren
authors.initials: S
authors.lastname: Soner
authors.forename: Turkan
authors.initials: T
authors.lastname: Haliloglu
mesh_terms: D000818:Animals; D017124:Conserved Sequence; D019143:Evolution, Molecular; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012997:Solvents / Q000737:chemistry
chemical_list: D011506:Proteins; D012997:Solvents
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