Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps

<p>Abstract</p> <p>Background</p> <p>Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.</p> <p>Results</p> <p>We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.</p> <p>Discussion</p> <p>Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.</p> <p>Conclusions</p> <p>The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.</p

MetaMHC: a meta approach to predict peptides binding to MHC molecules

As antigenic peptides binding to major histocompatibility complex (MHC) molecules is the prerequisite of cellular immune responses, an accurate computational predictor will be of great benefit to biologists and immunologists for understanding the underlying mechanism of immune recognition as well as facilitating the process of epitope mapping and vaccine design. Although various computational approaches have been developed, recent experimental results on benchmark data sets show that the development of improved predictors is needed, especially for MHC Class II peptide binding. To make the most of current methods and achieve a higher predictive performance, we developed a new web server, MetaMHC, to integrate the outputs of leading predictors by several popular ensemble strategies. MetaMHC consists of two components: MetaMHCI and MetaMHCII for MHC Class I peptide and MHC Class II peptide binding predictions, respectively. Experimental results by both cross-validation and using an independent data set show that the ensemble approaches outperform individual predictors, being statistically significant. MetaMHC is freely available at http://www.biokdd.fudan.edu.cn/Service/MetaMHC.html

Predicting essential proteins by integrating orthology, gene expressions, and PPI networks

<div><p>Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein’s essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of <i>Saccharomyces cerevisiae</i>. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.</p></div

The number of essential proteins predicted by OGN, BC, CC, DC, EC, SC, CoEWC, SON, and LBCC.

<p>(a)-(f) show the results of these methods when select top 100 to 600 ranked proteins as candidate essential proteins.</p

Comparison of the ensemble method with different threshold <i>T</i> and OGN (α = 0, 0.3, and 1) using Jackknife method.

<p>Comparison of the ensemble method with different threshold <i>T</i> and OGN (α = 0, 0.3, and 1) using Jackknife method.</p

The number of true essential proteins identified by OGN with different α.

<p>The number of true essential proteins identified by OGN with different α.</p

The protein interaction network for the top 100 selected proteins by OGN (alpha = 0.3).

<p>The protein interaction network for the top 100 selected proteins by OGN (alpha = 0.3).</p

Comparison of OGN, CoEWC, SON, LBCC, and five common used centrality measures (BC, CC, DC, EC, and SC) using Jackknife method.

<p>Comparison of OGN, CoEWC, SON, LBCC, and five common used centrality measures (BC, CC, DC, EC, and SC) using Jackknife method.</p

Performance of ensemble method with different top <i>n</i> and threshold <i>T</i>.

<p>Performance of ensemble method with different top <i>n</i> and threshold <i>T</i>.</p