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Ligand Activity In Surface Similarity Order Of The Stick BelkarProtein Functional Surfaces Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites BMC Structural Biology.It has become apparent that surfaces, comprised of a fraction of the total residues, are the most conserved functional features of proteins.RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/Articleimage/2016/CE/c6ce01358a/c6ce01358a-f5_hi-res.gif' alt='Ligand Activity In Surface Similarity Order Of The Stick Tv' title='Ligand Activity In Surface Similarity Order Of The Stick Tv' />Ligand binding pocket of a novel Allatostatin receptor type C of stick.In order to. A protein kinase is a kinase enzyme that modifies other.Protein kinase. in the dimer or higher order.The structure of A 2AStaR2 is depicted as a gray surface, cut away to reveal the ligand.Proteins utilize common surface motifs to create precise chemical environments designed to perform specific functions.These motifs are not restricted to a single protein scaffold but can be found within different protein folds or at domaindomain and subunits interfaces.While biochemical activity can be attributed to a few key residues e.Powerful evolutionary forces have allowed proteins to govern ligand binding through seemingly subtle local surface variability.These changes, which are not easily detectable by sequence analysis, may provide competitive advantage for optimization of co factor specificity.In some circumstances, surface diversity adversely affects normal cell process by providing environments for undesired binding events e.The conservation of functional surfaces presents an opportunity to compare and analyze proteins independent of sequence or fold.These comparisons can be used to classify protein functions or to infer biochemical activity for proteins with unknown function, such as those targeted by structural genomics programs. 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Several methods have been developed detecting localized, spatial protein similarities with applications for evolutionary analysis, function prediction and drug discovery.The use of graph theory has been widely applied to the comparison of three dimensional patterns.Artymiuk et al. developed an algorithm based on subgraph isomorphism detection to search residue patterns against the PDB2.Kinoshita et al. used clique detection algorithms to assign protein biochemical functions using the similarity information of molecular surface geometries and electrostatic potentials3.Ligand Activity In Surface Similarity Order Of The Stick JunkUsing a clique detection algorithm, Schmitt et al., compared generic pseudo centers that code for possible ligand protein interactions in protein cavities.Query cavities are searched against Cavbase, a pre computed database of cavities extracted from the PDB4.The method has been applied to identify surfaces in non homologous proteins as well as for the classification of protein families5.Kleywegt searched for motifs of residue pseudo centers in a library of protein structures using a depth first search algorithm6.Russell also developed an algorithm based on depth first search that detects atomic geometric patterns common in between side chains in proteins and presented new examples of convergent evolution7.Parametric statistical evaluations of Russells atomic superposition method were extended by Stark et al.Another widely used approach is geometric hashing, which is an efficient method for matching features against a database.Jackson and Gold used geometric hashing to perform an all against all comparison of protein ligand binding sites in the Sites.Base database 91.Their method was also applied for functional annotation and building pharmacophore models for drug discovery1.Fischer et. al. developed an algorithm based on geometric hashing that detects surface similarities of proteins using spatial patterns of atoms1.A similar method, TESS, has been applied for the derivation and matching of annotated spatial templates1.JESS1. 5, a successor to TESS, searches small groups of atoms under arbitrary constraints on geometry and chemistry and utilized statistics to evaluate matches.It is used to query the Catalytic Site Atlas CSA1.JESS is also used in the PROFUNC1.A protein evolution based method, pv.SOAR, was developed that used the unique approach of aligning sub sequences of surface residues to establish a residue correspondence between surfaces1.The residues were then superimposed on each other and statistical significance was evaluated for the resulting RMSD.This method was used to detect similar functional surfaces in non homologous proteins.Furthermore, in a recent study of shape variation of ligand binding pockets, Kahraman et.It was shown that shape descriptors could be used to classify ligand into their binding sites.In this study, we describe a new method for the sequence order independent comparison and alignment of protein functional surfaces.Our method, Surface.Screen, attempts to optimize two components, global surface shape and local physicochemical texture, for evaluating the similarity between a pair of surfaces.Surface shape similarity is assessed using a three dimensional object recognition algorithm and is used to rapidly pre classify surfaces from a large library of surfaces.Surfaces with sufficient shape complimentarity are then aligned by combinatorially identifying the best superimposition of common residues between the two surfaces.We introduce several metrics for scoring different properties of a surface alignment and an overall scoring function used in library searches.Furthermore, we introduce the Global Protein Surface Survey GPSS, a library of annotated protein surfaces calculated from all structures in the PDB.Querying surfaces from proteins of unknown function against the GPSS library allows Surface.Screen to be utilized as predictive tool.We describe three types of analysis to assess surface shape comparisons and spatial alignments.First, we describe the retrieval of surfaces from the GPSS library for surfaces, from the same protein, that bind ligands of various size, shape and pharmacophore properties.For this we use the example of HIV 1 protease.Second, we use the example of heme iron protoporphyrin IX binding sites to describe the retrieval of a functionally diverse binding surface that binds the same ligand.We provide the example of using our method as an annotation tool, identifying a new member of the heme binding monooxygenase family.Third, we describe how conformational diversity of bound ligands impacts retrieval rate for ubiquitous nucleotide binding sites.We also present the example of a nucleotide binding surface prediction and crystallographic validation for a structural genomics target with a new fold.We conclude with an analysis of the ATP binding surface landscape to provide insight on the correlation between surface similarity and function for structures in the PDB and for the subset of protein kinases. 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