scholarly journals In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks

2020 ◽  
Vol 18 ◽  
pp. 100-113 ◽  
Author(s):  
Suyu Mei ◽  
Kun Zhang
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Human Protein ◽  
Protein Protein Interaction ◽  
Host Signaling ◽  
Protein Protein Interaction Networks

scholarly journals Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives

2021 ◽  
Vol 38 (1) ◽  
pp. 5-17
Author(s):  
Aleksandar Velesinović ◽  
Goran Nikolić
Keyword(s):  
Modeling And Simulation ◽  
Protein Interaction ◽  
In Silico ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Ligand Docking ◽  
In Vivo Models ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
In Silico Methods

Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

Interaction and localization diversities of global and local hubs in human protein–protein interaction networks

2016 ◽  
Vol 12 (9) ◽  
pp. 2875-2882 ◽  
Author(s):  
M. Kiran ◽  
H. A. Nagarajaram
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Human Protein ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Global And Local ◽  
In Cells

Hubs, the highly connected nodes in protein–protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells.

scholarly journals In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

2016 ◽  
Vol 10 (1) ◽  
Author(s):  
Edson Luiz Folador ◽  
Paulo Vinícius Sanches Daltro de Carvalho ◽  
Wanderson Marques Silva ◽  
Rafaela Salgado Ferreira ◽  
Artur Silva ◽  
...  
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Corynebacterium Pseudotuberculosis ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
In Silico Identification ◽  
Protein Protein Interaction Networks

scholarly journals Comparative analysis of Pure Hubs and Pure Bottlenecks in Human Protein-protein Interaction Networks

2021 ◽  
Author(s):  
Nithya Chandramohan ◽  
Manjari Kiran ◽  
Hampapathalu Adimurthy Nagarajaram
Keyword(s):  
Protein Interaction ◽  
Splice Variants ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Human Protein ◽  
Cancer Genes ◽  
Protein Protein Interaction ◽  
High Betweenness ◽  
Protein Protein Interaction Networks ◽  
High Degree

Bottlenecks and hubs form a set of topologically important nodes in a network. In this communication, we have made a detailed investigation on hubs and bottlenecks in human protein-protein interaction networks. We find that, three distinct groups exist which we refer to as: a) pure hubs (PHs, nodes having high degree but low betweenness values), b) mix proteins (MXs, nodes having both high degree and high betweenness values) and c) pure bottlenecks (PBs, nodes having high betweenness values but low degree values). Our investigations have revealed that pure hubs, as compared with MXs and PBs, (i) are more disordered, (ii) have higher potential to bind to multiple partners, (iii) are enriched with essential proteins as well as enriched with a higher number of splice variants. The MX proteins, as compared with PHs and PBs, (i) show slower evolutionary patterns, (ii) are involved in multiple pathways, (iii) enriched with the products of genes associated with various diseases and (iv) are more often targeted by bacteria, viruses, protozoa, and fungi pathogens. PBs, as compared with the PHs and MXs, (i) are associated with cancer genes and (ii) are the targets or the nearest neighbors of the targets of most of the approved drugs. Furthermore, our study revealed that these three categories of proteins are involved in distinct functional roles; PHs are involved in housekeeping processes such as transcription and replication; MXs proteins are involved in core signaling pathways whereas PBs are involved in signal transduction processes. Our work, therefore, has identified the distinct characteristics features associated with pure hubs, mix proteins and pure bottlenecks and thus helps in prioritizing proteins based on their degree and betweenness centrality values.

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