Understanding the recognition mechanism of protein-RNA complexes using energy based approach.
ABSTRACT Protein-RNA interactions perform diverse functions within the cell. Understanding the recognition mechanism of protein-RNA complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-RNA complexes. The new approach considers the repulsive interactions as well as the effect of distance between the atoms in protein and RNA in terms of interaction energy, which are not considered in traditional distance based methods to identify the binding sites. We found that the positively charged, polar and aromatic residues are important for binding. These residues influence to form electrostatic, hydrogen bonding and stacking interactions. Our observation has been verified with the experimental binding specificity of protein-RNA complexes and found good agreement with experiments. Further, the propensities of residues/nucleotides in the binding sites of proteins/RNA and their atomic contributions have been derived. Based on these results we have proposed a novel mechanism for the recognition of protein-RNA complexes: the charged and polar residues in proteins initiate recognition with RNA by making electrostatic and hydrogen bonding interactions between them; the aromatic side chains tend to form aromatic-aromatic interactions and the hydrophobic residues aid to stabilize the complex.
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ABSTRACT: Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology. We have developed an energy based approach for identifying the binding site residues in protein-protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching. We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix. The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.Proteome Science 01/2011; 9 Suppl 1:S13. · 2.42 Impact Factor
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ABSTRACT: Predicting DNA-binding residues from a protein three-dimensional structure is a key task of computational structural proteomics. In the present study, based on machine learning technology, we aim to explore a reduced set of weighted average features for improving prediction of DNA-binding residues on protein surfaces. Via constructing the spatial environment around a DNA-binding residue, a novel weighting factor is first proposed to quantify the distance-dependent contribution of each neighboring residue in determining the location of a binding residue. Then, a weighted average scheme is introduced to represent the surface patch of the considering residue. Finally, the classifier is trained on the reduced set of these weighted average features, consisting of evolutionary profile, interface propensity, betweenness centrality and solvent surface area of side chain. Experimental results on 5-fold cross validation and independent tests indicate that the new feature set are effective to describe DNA-binding residues and our approach has significantly better performance than two previous methods. Furthermore, a brief case study suggests that the weighted average features are powerful for identifying DNA-binding residues and are promising for further study of protein structure-function relationship. The source code and datasets are available upon request.PLoS ONE 01/2011; 6(12):e28440. · 3.53 Impact Factor
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ABSTRACT: Intra-molecular interactions within complex systems play a pivotal role in the biological function, which pose an imminent challenge in computational structural proteomics. The network paradigm treats any system as a set of nodes linked by edges correspondent to the relations existing between the nodes, making it as a computationally efficient tool to meet this challenge. Here, we review the recent advances in the use of network theory to study the topology and dynamics of protein-ligand and protein-nucleic acid complexes. The study of protein complexes networks not only involves the topological analysis in terms of network parameters, but also reveals the consistent picture of intrinsic functional dynamics. Current dynamical analysis focuses on a plethora of functional phenomenon: elucidating the process of allosteric communication, capturing the binding induced conformational changes, predicting and identifying binding sites of protein complexes, which will give insights into intra-protein complexes interactions. Furthermore, such computational results may inform a variety of known biological processes and experimental data, and thereby suggesting a huge potential for applications such as drug design. Finally we describe some web-based resource of protein complexes, as well as protein network servers and related bioinformatics tools.Current Protein and Peptide Science 02/2013; · 2.33 Impact Factor