Analysis of triangular motifs in protein interaction networks and their implications to protein ages and cancer genes
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Proteins interact with each other to regulate their functionality and localisation. The accumulated protein interaction evidences are represented by protein interaction network through a graph abstraction. Topological properties of protein interaction networks have been explored to characterise proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. Moreover, the topological properties of protein interaction network are reported to have relationship with cancer-related genes. In this paper, we construct a weighted human protein interaction network based on triangles in protein interaction network and show that there is relationship between triangles in protein interaction network and phylogenetic age of proteins. We also show that triangles in protein interaction network are related to cancer-related genes.Keywords:
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Proteins interact with each other to regulate their functionality and localisation. The accumulated protein interaction evidences are represented by protein interaction network through a graph abstraction. Topological properties of protein interaction networks have been explored to characterise proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. Moreover, the topological properties of protein interaction network are reported to have relationship with cancer-related genes. In this paper, we construct a weighted human protein interaction network based on triangles in protein interaction network and show that there is relationship between triangles in protein interaction network and phylogenetic age of proteins. We also show that triangles in protein interaction network are related to cancer-related genes.
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Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host-parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co-evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user.PINAT is available at www.manit.ac.in/pinat.
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Proteins interact with each other to regulate their functionality and localisation. The accumulated protein interaction evidences are represented by protein interaction network through a graph abstraction. Topological properties of protein interaction networks have been explored to characterise proteins and predict undiscovered interactions. Meanwhile, many researchers have tried to explain how protein interaction network is formed through evolutionary process. Moreover, the topological properties of protein interaction network are reported to have relationship with cancer-related genes. In this paper, we construct a weighted human protein interaction network based on triangles in protein interaction network and show that there is relationship between triangles in protein interaction network and phylogenetic age of proteins. We also show that triangles in protein interaction network are related to cancer-related genes.
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High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a 'leave-one-out' approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org
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Abstract Motivation: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. Results: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ‘leave-one-out’ approach we find average success rates between 20 and 40% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. Availability: A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.
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Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.
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We describe two novel methods for predicting protein interactions, using only the topology of an observed protein interaction network. The first method searches the protein interaction network for defective cliques (i.e. nearly complete complexes of pair wise interacting proteins), and predicts the interactions that complete them. The second method computes the diffusion distance between each pair of proteins and then infers an interaction when such distance is below a given threshold. We show that both methods have a good predictive performance and compare their results.
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