A global optimization algorithm for protein surface alignment

Background  

A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved.     

Accessibility and partner number of protein residues, their relationship and a webserver, ContPlot for their display

Background  

Depending on chemical features residues have preferred locations – interior or exterior – in protein structures, which also determine how many other residues are found around them. The close packing of residues is the hallmark of protein interior and protein-protein interaction sites.     

Prediction of protein-protein binding site by using core interface residue and support vector machine

Background  

The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.     

High-throughput identification of interacting protein-protein binding sites

Background  

With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Therefore, methods that are able to identify whether these binding sites interact are needed.     

Selective prediction of interaction sites in protein structures with THEMATICS

Background  

Methods are now available for the prediction of interaction sites in protein 3D structures. While many of these methods report high success rates for site prediction, often these predictions are not very selective and have low precision. Precision in site prediction is addressed using Theoretical Microscopic Titration Curves (THEMATICS), a simple computational method for the identification of active sites in enzymes. Recall and precision are measured and compared with other methods for the prediction of catalytic sites.     

Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins

Background  

Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.     

PSAIA – Protein Structure and Interaction Analyzer

Background  

PSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites.     

A computational survey of candidate exonic splicing enhancer motifs in the model plant <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis>

Background  

Algorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions. However exonic splicing enhancers have been shown to enhance the utilization of nearby splice sites.     

Protein complex prediction based on <Emphasis Type="Italic">k</Emphasis>-connected subgraphs in protein interaction network

Background  

Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph.     

A protein domain interaction interface database: InterPare

Background  

Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently.     

LIGSITE csc : predicting ligand binding sites using the Connolly surface and degree of conservation

Background  

Identifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed.     

Residue contact-count potentials are as effective as residue-residue contact-type potentials for ranking protein decoys

Background  

For over 30 years potentials of mean force have been used to evaluate the relative energy of protein structures. The most commonly used potentials define the energy of residue-residue interactions and are derived from the empirical analysis of the known protein structures. However, single-body residue 'environment' potentials, although widely used in protein structure analysis, have not been rigorously compared to these classical two-body residue-residue interaction potentials. Here we do not try to combine the two different types of residue interaction potential, but rather to assess their independent contribution to scoring protein structures.     

Identification of physicochemical selective pressure on protein encoding nucleotide sequences

Background  

Statistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account.     

Functional clustering of yeast proteins from the protein-protein interaction network

Background  

The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.     

Mining protein networks for synthetic genetic interactions

Background  

The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified.     

Large-scale evaluation of dynamically important residues in proteins predicted by the perturbation analysis of a coarse-grained elastic model

Backgrounds  

It is increasingly recognized that protein functions often require intricate conformational dynamics, which involves a network of key amino acid residues that couple spatially separated functional sites. Tremendous efforts have been made to identify these key residues by experimental and computational means.     

CTCF binding site classes exhibit distinct evolutionary, genomic, epigenomic and transcriptomic features

Background  

CTCF (CCCTC-binding factor) is an evolutionarily conserved zinc finger protein involved in diverse functions ranging from negative regulation of MYC, to chromatin insulation of the beta-globin gene cluster, to imprinting of the Igf2 locus. The 11 zinc fingers of CTCF are known to differentially contribute to the CTCF-DNA interaction at different binding sites. It is possible that the differences in CTCF-DNA conformation at different binding sites underlie CTCF's functional diversity. If so, the CTCF binding sites may belong to distinct classes, each compatible with a specific functional role.     

Stable evolutionary signal in a Yeast protein interaction network

Background  

The recently emerged protein interaction network paradigm can provide novel and important insights into the innerworkings of a cell. Yet, the heavy burden of both false positive and false negative protein-protein interaction data casts doubt on the broader usefulness of these interaction sets. Approaches focusing on one-protein-at-a-time have been powerfully employed to demonstrate the high degree of conservation of proteins participating in numerous interactions; here, we expand his 'node' focused paradigm to investigate the relative persistence of 'link' based evolutionary signals in a protein interaction network of S. cerevisiae and point out the value of this relatively untapped source of information.