MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome

Motivation: High-throughput experiments are generating ever increasing amounts of various -omics data, so shedding new light on the link between human disorders, their genetic causes, and the related impact on protein behavior and structure. While numerous bioinformatics tools now exist that predict which variants in the human exome cause diseases, few tools predict the reasons why they might do so. Yet, understanding the impact of variants at the molecular level is a prerequisite for the rational development of targeted drugs or personalized therapies. Results: We present the updated MutaFrame webserver, which aims to meet this need. It offers two deleteriousness prediction softwares, DEOGEN2 and SNPMuSiC, and is designed for bioinformaticians and medical researchers who want to gain insights into the origins of monogenic diseases. It contains information at two levels for each human protein: its amino acid sequence and its 3-dimensional structure; we used the experimental structures whenever available, and modeled structures otherwise. MutaFrame also includes higher-level information, such as protein essentiality and protein-protein interactions. It has a user-friendly interface for the interpretation of results and a convenient visualization system for protein structures, in which the variant positions introduced by the user and other structural information are shown. In this way, MutaFrame aids our understanding of the pathogenic processes caused by single-site mutations and their molecular and contextual interpretation. Availability: Mutaframe webserver at http://mutaframe.com/

An Automated Model for Target Protein prediction in PPI

Background: Essential proteins play a crucial role in most of the living organisms. The computer-based task of predicting essential proteins is important for target protein identification, disease treatment and suitable drug development. Objective: Traditionally many experimental and centrality measures have been proposed by researchers to predict protein essentiality. Methods: The prediction accuracy, sensitivity, specificity identified by the traditional methods is very low. Results and Discussion: In this research work, a novel computational based approach such NC-KNN model has been proposed to identify the most essential proteins. The proposed work uses a combination of network topology measure and machine learning model to predict the essential proteins. Conclusion: The proposed work shows a remarkable improvement than seven traditional centrality based measures such as DC, BC, CC, EC, NC, ECC and SC in terms of the metrics such as accuracy(A1), precision(P1), recall(R1), sensitivity(SE) and specificity(SP).

IDENTIFICATION OF ESSENTIAL PROTEINS FROM WEIGHTED PROTEIN–PROTEIN INTERACTION NETWORKS

Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein–protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. Unfortunately, the protein–protein interactions produced by high-throughput experiments generally have high false positives. Moreover, most of centrality measures based on network topology are sensitive to false positives. We therefore propose a new method for evaluating the confidence of each interaction based on the combination of logistic regression-based model and function similarity. Nine standard centrality measures in weighted network were redefined in this paper. The experimental results on a yeast protein interaction network shows that the weighting method improved the performance of centrality measures considerably. More essential proteins were discovered by the weighted centrality measures than by the original centrality measures used in the unweighted network. Even about 20% improvements were obtained from closeness centrality and subgraph centrality.

Intracellular Expression of Peptide Fusions for Demonstration of Protein Essentiality in Bacteria

ABSTRACT We describe a “protein knockout” technique that can be used to identify essential proteins in bacteria. This technique uses phage display to select peptides that bind specifically to purified target proteins. The peptides are expressed intracellularly and cause inhibition of growth when the protein is essential. In this study, peptides that each specifically bind to one of seven essential proteins were identified by phage display and then expressed as fusions to glutathione S-transferase in Escherichia coli. Expression of peptide fusions directed against E. coli DnaN, LpxA, RpoD, ProRS, SecA, GyrA, and Era each dramatically inhibited cell growth. Under the same conditions, a fusion with a randomized peptide sequence did not inhibit cell growth. In growth-inhibited cells, inhibition could be relieved by concurrent overexpression of the relevant target protein but not by coexpression of an irrelevant protein, indicating that growth inhibition was due to a specific interaction of the expressed peptide with its target. The protein knockout technique can be used to assess the essentiality of genes of unknown function emerging from the sequencing of microbial genomes. This technique can also be used to validate proteins as drug targets, and their corresponding peptides as screening tools, for discovery of new antimicrobial agents.