Hubs and Bottlenecks in Protein-Protein Interaction Networks.

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes.

Structural and Biophysical properties of therapeutically important proteins Rv1509 and Rv2231A of Mycobacterium tuberculosis.

Through comparative analyses using BLASTp and BLASTn of the 25 target sequences, our research identified two unique post-transcriptional modifiers, Rv1509 and Rv2231A, which serve as distinctive and characteristic proteins of M.tb - the Signature Proteins. Here, we have characterized these two signature proteins associated with pathophysiology of M.

Dissection of hubs and bottlenecks in a protein-protein interaction network.

Analysis of degree centrality in conjunction with betweenness centrality of proteins in a human protein-protein interaction network revealed three categories of centrally important proteins: a) proteins with high degree and betweenness (hub-bottlenecks denoted as MX), b) proteins with high betweenness and low degree (non-hub-bottlenecks/pure bottlenecks denoted as PB) and c) proteins with high degree and low betweenness (hub-non-bottlenecks/pure hubs denoted as PH). When subjected to a detailed statistical analysis of their molecular-level properties, the proteins belonging to each of these categories were found to be associated with distinct canonical molecular properties, i.e.

Intrinsically Disordered Proteins: An Overview.

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins.

Human TMPRSS2 non-catalytic ectodomain and SARS-CoV-2 S2' subunit interaction mediated SARS-CoV-2 endocytosis: a model proposal with virtual screening for potential drug molecules to inhibit this interaction.

This study proposes a novel model for integration of SARS-CoV-2 into host cell via endocytosis as a possible alternative to the prevailing direct fusion model. It is known that the SARS-CoV-2 spike protein undergoes proteolytic cleavage at S1-S2 cleavage site and the cleaved S2 domain is primed by the activated serine protease domain (SPD) of humanTMPRSS2 to become S2'. The activated SPD of TMPRSS2 is formed after it is cleaved by autocatalysis from the membrane bound non-catalytic ectodomain (hNECD) comprising of LDLRA CLASS-I repeat and a SRCR domain.

Ectodysplasin pathogenic variants affecting the furin-cleavage site and unusual clinical features define X-linked hypohidrotic ectodermal dysplasia in India.

Hypohidrotic ectodermal dysplasia (HED) is a rare genetic disorder caused by mutational inactivation of a developmental pathway responsible for generation of tissues of ectodermal origin. The X-linked form accounts for the majority of HED cases and is caused by Ectodysplasin (EDA) pathogenic variants. We performed a combined analysis of 29 X-linked hypohidrotic ectodermal dysplasia (XLHED) families (including 12 from our previous studies).

Substitution scoring matrices for proteins - An overview.

Sequence analysis is the primary and simplest approach to discover structural, functional and evolutionary details of related proteins. All the alignment based approaches of sequence analysis make use of amino acid substitution matrices, and the accuracy of the results largely depends on the type of scoring matrices used to perform alignment tasks. An amino acid substitution matrix is a 20 × 20 matrix in which the individual elements encapsulate the rates at which each of the 20 amino acid residues in proteins are substituted by other amino acid residues over time.

Amino acid substitution scoring matrices specific to intrinsically disordered regions in proteins.

An amino acid substitution scoring matrix encapsulates the rates at which various amino acid residues in proteins are substituted by other amino acid residues, over time. Database search methods make use of substitution scoring matrices to identify sequences with homologous relationships. However, widely used substitution scoring matrices, such as BLOSUM series, have been developed using aligned blocks that are mostly devoid of disordered regions in proteins.

Molecular principles of human virus protein-protein interactions.

Motivation: Viruses, from the human protein-protein interaction network perspective, target hubs, bottlenecks and interconnected nodes enriched in certain biological pathways. However, not much is known about the general characteristic features of the human proteins interacting with viral proteins (referred to as hVIPs) as well as the motifs and domains utilized by human-virus protein-protein interactions (referred to as Hu-Vir PPIs).

Results: Our study has revealed that hVIPs are mostly disordered proteins, whereas viral proteins are mostly ordered proteins.

Nodes occupying central positions in human tissue specific PPI networks are enriched with many splice variants.

The functional repertoire of genes in the eukaryotic organisms is enhanced by the phenomenon of alternative splicing. Hence, a node in a tissue specific protein-protein interaction (TS PPIN) network can be thought of as an ensemble of various spliced protein products of the corresponding gene expressed in that tissue. Here we demonstrate that the nodes that occupy topologically central positions characterized by high degree, betweenness, closeness, and eigenvector centrality values in TS PPINs of Homo sapiens are associated with high number of splice variants.

Global versus local hubs in human protein-protein interaction network.

In this study, we have constructed tissue-specific protein-protein interaction networks for 70 human tissues and have identified three types of hubs based on their expression breadths: (a) tissue-specific hubs (TSHs) (proteins that are expressed in ≤ 10 tissues and also form hubs in ≤ 10 tissues), (b) tissue-preferred hubs (TPHs) (proteins expressed in ≥ 60 tissues but are highly connected in ≤ 10 tissues), and (c) housekeeping hubs (HKHs) (proteins that are expressed in ≥ 60 tissues and also form hubs in ≥ 60 tissues). Comparative analyses revealed significant differences between TSHs and HKHs and also revealed that TPHs behave more like HKHs. TSHs are lengthier, more disordered, and also quickly evolving proteins as compared with HKHs.

Effect of alternative splicing on the degree centrality of nodes in protein-protein interaction networks of Homo sapiens.

Alternative splicing of an mRNA transcript could lead to formation of protein products having a different number of binding/interacting domains which in turn may relate to the number of physical interactions they make with other proteins and hence a node in a protein-protein interaction network can be considered as an ensemble of its splice variants and its degree (i.e., number of physical interactions it makes with other nodes) as the union of the individual degrees of its splice variants.

A hierarchical approach to protein fold prediction.

Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage.

SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids.

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset.