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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    • "A recent work (Bera and Ray, 2009), banking on (Aqvist and Tapia, 1987) and employing SAFD methodology (Pettit and Bowie, 1999), has investigated interfaces of obligate and non-obligate protein-protein complexes. From similar motivation, scope of the pioneering Aqvist and Tapia work has been studied systematically in another recent work (Feihong et al., 2007) on 2383 non-redundant protein chains in dimeric complexes. The last study differed from the traditional knowledge-base in their quantitative assertion that a residue with smooth surface is unlikely to be there in interface, pointing therefore at the importance of investigating the roughness of molecular surface. "
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    ABSTRACT: Year 2010 marked the 25th year since we came to know that roughness of a protein surface has fractal symmetry. Ever since the publication of Lewis and Rees' paper, hundreds of works from a spectrum of perspectives have established that fractal dimension (FD) can be considered as a reliable marker that describes roughness of protein surface objectively. In this article, we introduce readers to the fundamentals of fractals and present categorical biophysical and geometrical reasons as to why FD-based constructs can describe protein surface roughness more accurately. We then review the commonality (and the lack of it) between numerous approaches that have attempted to investigate protein surface with fractal measures, before exploring the patterns in the results that they have produced. Apart from presenting the genealogy of approaches and results, we present an analysis that quantifies the difference in surface roughness in stretches of protein surface containing the active site, before and after binding to ligands, to underline the utility of FD-based measures further. It has been found that surface stretches containing the active site, in general, undergo a significant increment in its roughness after binding. After presenting the entire repertoire of FD-based surface roughness studies, we talk about two yet-unexplored problems where application of FD-based techniques can help in deciphering underlying patterns of surface interactions. Finally, we list the limitations of FD-based constructs and put down several precautions that one must take while working with them. Copyright © 2013 John Wiley & Sons, Ltd.
    Full-text · Article · May 2013 · Journal of Molecular Recognition
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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.
    Full-text · Article · Nov 2010 · International Journal of Data Mining and Bioinformatics
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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.
    Full-text · Conference Paper · Dec 2007
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