Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information

Automated linear motif discovery from protein interaction network

Master'sMASTER OF SCIENC

Clustering protein-protein interactions based on conversed domain similarities

Cataloged from PDF version of article.Protein interactions govern most cellular processes, including signal transduction, transcriptional regulation and metabolism. Saccharomyces ceravisae is estimated to have 16,000 protein interactions. Appereantly only a small number of these interactions were formed ab initio (invention), rest of them were formed through gene duplications and exon shuffling (birth). Domains form functional units of a protein and are responsible for most of the interaction births, since they can be recombined and rearranged much more easily compared to innovation. Therefore groups of functionally similar, homologous interactions that evolved through births are expected to have a certain domain signature. Several high throughput techniques can detect interacting protein pairs, resulting in a rapidly growing corpus of protein interactions. Although there are several efforts for computationally integrating this data with literature and other high throughput data such as gene expression, annotation of this corpus is inadaquate for deriving interaction mechanism and outcome. Finding interaction homologies would allow us to annotate an unannotated interaction based on already annotated known interactions, or predict new ones. In this study we propose a probabilistic model for assigning interactions to homologous groups, according to their conserved domain similarities. Based on this model we have developed and implemented an Expectation-Maximization algorithm for finding the most likely grouping of an interaction set. We tested our algorithm with synthetic and real data, and showed that our initial results are very promising. Finally we propose several directions to improve this workAyaz, AslıM.S

Développement d'une méthode de prédiction des sites d'interaction protéines-molécules à partir de la structure primaire

Nicolas Delsaux (2008). Développement d’une méthode de prédiction des sites d’interaction protéines-molécules à partir de la structure primaire (thèse de doctorat). Gembloux, Belgique, Faculté Universitaires des Sciences Agronomiques, 204p., 20 tabl., 81 fig.Résumé : Ce travail a pour but d’améliorer nos connaissances des interfaces et d’aider les scientifiques à caractériser au mieux les protéines de fonction et de structure encore inconnues. Pour cela, nous avons construit des banques de données de structures tridimensionnelles de complexes et leurs interfaces ont été analysées au niveau atomique. L’analyse détaillée des interfaces a permis de confirmer le rôle important des acides aminés aromatiques et de l’arginine ainsi que de montrer quels couples de résidus sont significativement favorisés dans celles-ci. De plus, l’importance du volume des résidus voisins des sites d’interaction et de la conformation des acides nucléiques a pu être montrée. Les principales variables corrélées aux interfaces sont : trois propensions à être en interaction, le type de résidu et sa position dans la séquence, les prédictions d’accessibilité et de structures secondaires, la prédiction en ‘Receptor Binding Domain’, et la présence de certains motifs protéiques. Finalement, une méthode de prédiction des sites d’interaction été mise au point. Cette méthode est l’une des seules à n’utiliser que des informations directement accessible à partir de la séquence et donne des résultats très encourageants. La spécificité obtenue est en effet suffisante pour améliorer les résultats expérimentaux obtenus par mutagénèse dirigée.Nicolas Delsaux (2008). Development of a prediction method of protein-molecule interaction sites from primary structure (doctoral thesis, in French). Gembloux, Belgium, Agricultural University, 204p., 20 tabl., 81 fig.Summary: The objective of this work is to improve the knowledge about interfaces and to assist the characterization of proteins with unknown function and structure. To this aim, we constructed databases of three-dimensional structures of complexes and their interfaces were analyzed at the atomic scale. The in-depth analysis of interfaces confirms the preponderance of aromatic amino acids and of arginine. Residue pairs that are significantly favored at the interfaces were also identified. Moreover, the importance of interaction sites neighboring residue volumes and of the nucleic acid’s conformation has been highlighted. The main parameters correlated to interaction sites are: three interaction propensities, residue’s type and its location within the sequence, predictions of accessibility and of secondary structures, prediction to be a Receptor Binding Domain, and the presence of protein patterns. Finally, a prediction method of interaction sites has been developed. This method is one of the few which only use information directly accessible from protein sequence and gives promising results. The achieved specificity is indeed sufficient to improve experimental results of site-directed mutagenesis