Emerging landscape of molecular interaction networks:Opportunities, challenges and prospects.

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational biomodelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug-disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Delineating infection strategies of Leishmania donovani secretory proteins in Human through host-pathogen protein Interactome prediction.

Interactions of Leishmania donovani secretory virulence factors with the host proteins and their interplay during the infection process in humans is poorly studied in Visceral Leishmaniasis. Lack of a holistic study of pathway level de-regulations caused due to these virulence factors leads to a poor understanding of the parasite strategies to subvert the host immune responses, secure its survival inside the host and further the spread of infection to the visceral organs. In this study, we propose a computational workflow to predict host-pathogen protein interactome of L.donovani secretory virulence factors with human proteins combining sequence-based Interolog mapping and structure-based Domain Interaction mapping techniques. We further employ graph theoretical approaches and shortest path methods to analyze the interactome. Our study deciphers the infection paths involving some unique and understudied disease-associated signaling pathways influencing the cellular phenotypic responses in the host. Our statistical analysis based in silico knockout study unveils for the first time UBC, 1433Z and HS90A mediator proteins as potential immunomodulatory candidates through which the virulence factors employ the infection paths. These identified pathways and novel mediator proteins can be effectively used as possible targets to control and modulate the infection process further aiding in the treatment of Visceral Leishmaniasis.

In silico studies on the interaction of phage displayed biorecognition element (TFQAFDLSPFPS) with the structural protein VP28 of white spot syndrome virus.

White spot disease caused by the white spot syndrome virus (WSSV) incurs a huge loss to the shrimp farming industry. Since no effective therapeutic measures are available, early detection and prevention of the disease are indispensable. Towards this goal, we previously identified a 12-mer phage displayed peptide (designated as pep28) with high affinity for VP28, the structural protein of the white spot syndrome virus (WSSV). The peptide pep28 was successfully used as a biorecognition probe in the lateral flow assay developed for rapid, on-site detection of WSSV. To unravel the structural determinants for the selective binding between VP28 and pep28, we used bioinformatics, structural modeling, protein-protein docking, and binding-free energy studies. We performed atomistic molecular dynamics simulations of pep28-pIII model totaling 300 ns timescale. The most representative pep28-pIII structure from the simulation was used for docking with the crystal structure of VP28. Our results reveal that pep28 binds in a surface groove of the monomeric VP28 ß-barrel and makes several hydrogen bonds and non-polar interactions. Ensemble-based binding-free energy studies reveal that the binding is dominated by non-polar interactions. Our studies provide molecular level insights into the binding mechanism of pep28 with VP28, which explain why the peptide is selective and can assist in modifying pep28 for its practical use, both as a biorecognition probe and a therapeutic.