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Association of putative concave protein-binding sites with the fluctuation behavior of residues.

ABSTRACT: Here, we propose a binding site prediction method based on the high frequency end of the spectrum in the native state of the protein structural dynamics. The spectrum is obtained using an elastic network model (GNM). High frequency vibrating (HFV) residues are determined from the fastest modes dynamics. HFV residue clusters and the associated surface patch residues are tested for their likelihood to locate at the binding interfaces using two different data sets, the Benchmark Set of mainly enzymes and antigen/antibodies and the Cluster Set of more diverse structures. The binding interface is identified to be within 7.5 A of the HFV residue clusters in the Benchmark Set and Cluster Set, for 77% and 70% of the structures, respectively. The success rate increases to 88% and 84%, respectively, by using the surface patches. The results suggest that concave binding interfaces, typically those of enzyme-binding sites, are enriched by the HFV residues. Thus, we expect that the association of HFV residues with the interfaces is mostly for enzymes. If, however, a binding region has invaginations and cavities, as in some of the antigen/antibodies and in cases in the Cluster data set, we expect it would be detected there too. This implies that binding sites possess several (inter-related) properties such as cavities, high packing density, conservation, and disposition for hotspots at binding surfaces. It further suggests that the high frequency vibrating residue-based approach is a potential tool for identification of regions likely to serve as protein-binding sites. The software is available at http://www.prc.boun.edu.tr/PRC/software.html.

SUBMITTER: Ertekin A

PROVIDER: S-EPMC2242393 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature

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Association of putative concave protein-binding sites with the fluctuation behavior of residues.

Ertekin Asli A Nussinov Ruth R Haliloglu Turkan T

Protein science : a publication of the Protein Society 20061001 10

Here, we propose a binding site prediction method based on the high frequency end of the spectrum in the native state of the protein structural dynamics. The spectrum is obtained using an elastic network model (GNM). High frequency vibrating (HFV) residues are determined from the fastest modes dynamics. HFV residue clusters and the associated surface patch residues are tested for their likelihood to locate at the binding interfaces using two different data sets, the Benchmark Set of mainly enzym ...[more]

PMID: 17008715

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Project description:BACKGROUND: Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses. RESULTS: PCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors. CONCLUSIONS: This is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.

Project description:BackgroundBark beetles are major pests of conifer forests, and their behavior is primarily mediated via olfaction. Targeting the odorant receptors (ORs) may thus provide avenues towards improved pest control. Such an approach requires information on the function of ORs and their interactions with ligands, which is also essential for understanding the functional evolution of these receptors. Hence, we aimed to identify a high-quality complement of ORs from the destructive spruce bark beetle Ips typographus (Coleoptera, Curculionidae, Scolytinae) and analyze their antennal expression and phylogenetic relationships with ORs from other beetles. Using 68 biologically relevant test compounds, we next aimed to functionally characterize ecologically important ORs, using two systems for heterologous expression. Our final aim was to gain insight into the ligand-OR interaction of the functionally characterized ORs, using a combination of computational and experimental methods.ResultsWe annotated 73 ORs from an antennal transcriptome of I. typographus and report the functional characterization of two ORs (ItypOR46 and ItypOR49), which are responsive to single enantiomers of the common bark beetle pheromone compounds ipsenol and ipsdienol, respectively. Their responses and antennal expression correlate with the specificities, localizations, and/or abundances of olfactory sensory neurons detecting these enantiomers. We use homology modeling and molecular docking to predict their binding sites. Our models reveal a likely binding cleft lined with residues that previously have been shown to affect the responses of insect ORs. Within this cleft, the active ligands are predicted to specifically interact with residues Tyr84 and Thr205 in ItypOR46. The suggested importance of these residues in the activation by ipsenol is experimentally supported through site-directed mutagenesis and functional testing, and hydrogen bonding appears key in pheromone binding.ConclusionsThe emerging insight into ligand binding in the two characterized ItypORs has a general importance for our understanding of the molecular and functional evolution of the insect OR gene family. Due to the ecological importance of the characterized receptors and widespread use of ipsenol and ipsdienol in bark beetle chemical communication, these ORs should be evaluated for their potential use in pest control and biosensors to detect bark beetle infestations.

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Association of putative concave protein-binding sites with the fluctuation behavior of residues.

Publications

Association of putative concave protein-binding sites with the fluctuation behavior of residues.

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