Conference Paper

Analysis of Protein Protein Dimeric Interfaces

Iowa State Univ., Ames
DOI: 10.1109/BIBM.2007.60 Conference: Bioinformatics and Biomedicine, 2007. BIBM 2007. IEEE International Conference on
Source: IEEE Xplore

ABSTRACT We analyzed the structural properties and the local surface environment of surface amino acid residues of proteins using a large, non-redundant dataset of 2383 protein chains in dimeric complexes from PDB. We compared the interface residues and non-interface residues based on six properties: side chain orientation, surface roughness, solid angle, ex value, hydrophobicity and interface cluster size. The results of our analysis show that interface residues have side chains pointing inward; interfaces are rougher, tend to be flat, moderately convex or concave and protrude more relative to non-interface surface residues. Interface residues tend to be surrounded by hydrophobic neighbors and tend to form clusters consisting of three or more interfaces residues. These findings are consistent with previous published studies using much smaller datasets, while allowing for more qualitative conclusions due to our larger dataset. Preliminary results suggest the possibility of using the six the properties to identify putative interface residues.

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Available from: Vasant Honavar, Aug 02, 2015
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    • "The program generates a standard tab-delimited output file with the PDBID, chain name, residue name (three letter abbreviation), residue number, a + or − indicating whether or not the residue is part of the interface, a score derived from the structural property being examined (roughness and cx) and a + or − denoting whether or not the residue is part of the surface of the protein (the definition of a surface residue can be varied within the class as desired). In our analysis, surface residues are defined as residues that have a solvent accessible surface area that is at least 5% of their total surface area (Wu et al., 2007; Connolly, 1993). "
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    ABSTRACT: We analyse sequence and structural features of protein-RNA interfaces using RB-147, a non-redundant dataset of protein-RNA complexes extracted from the PDB. We train classifiers using machine learning algorithms to predict protein-RNA interfaces from sequence and structure-derived features of proteins. Our experiments show that Struct-NB, a Naive Bayes classifier that exploits structural features, outperforms its counterparts that use only sequence features to predict protein-RNA binding residues.
    International Journal of Data Mining and Bioinformatics 11/2010; 4(1):21-43. DOI:10.1504/IJDMB.2010.030965 · 0.66 Impact Factor
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    • "The program generates a standard tabdelimited output file with the PDBID, chain name, residue name (three letter abbreviation), residue number, a + or indicating whether or not the residue is part of the interface, a score derived from the structural property being examined (roughness, cx, solid angle) and a + or -denoting whether or not the residue is part of the surface of the protein (the definition of a surface residue can be varied within the class as desired). In our analysis, surface residues are defined as residues that have a solvent accessible surface area that is at least 5% of their total surface area [22] [5]. "
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    ABSTRACT: We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA...etc), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the protein data bank. Our analysis of surface roughness, solid angle and CX value of amino acid residues for each of the protein chains in the dataset shows that: The protein-RNA interface residues tend to be protruding compared to non-interface residues and tend to have higher surface roughness and exhibit moderately convex or concave solid angles. Furthermore, the protein chains in protein-RNA interfaces that contain Viral RNA and rRNA significantly differ from those that contain dsRNA, mRNA siRNA, snRNA, SRP RNA and tRNA with respect to their CX values. The results of this analysis sug gests the possibility of using such structural features to reliably identify protein-RNA interface residues when the structure of the protein is available but the structures of complexes formed by the protein with RNA are not.
    Bioinformatics and Biomedicine Workshops, 2007. BIBMW 2007. IEEE International Conference on; 12/2007
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