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.tb which may prove to be therapeutically important targets. Dynamic Light Scattering and Analytical Gel Filtration Chromatography exhibited that Rv1509 exists as a monomer while Rv2231A as a dimer in solution. Secondary structures were determined using Circular Dichroism and further validated through Fourier Transform Infrared spectroscopy. Both the proteins are capable of withstanding a wide range of temperature and pH variations. Fluorescence spectroscopy based binding affinity experiments showed that Rv1509 binds to iron and may promote organism growth by chelating iron. In the case of Rv2231A, a high affinity for its substrate RNA was observed, which is facilitated in presence of Mg2+ suggesting it might have RNAse activity, supporting the prediction through in-silico studies. This is the first study on biophysical characterization of these two therapeutically important proteins, Rv1509 and Rv2231A, providing important insights into their structure -function correlations which are crucial for development of new drugs/ early diagnostics tools targeting these proteins.

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., "molecular markers". The MX proteins are a) conformationally versatile, mainly comprising of essential proteins, b) the targets for interactions by the proteins of viral and bacterial pathogens, c) evolutionally constrained, involved in multiple pathways, enriched with disease genes and d) involved in the functions such as protein stabilization, phosphorylation, and mRNA slicing processes. PB proteins are a) enriched with extracellular and cancer-related proteins, b) enriched with the approved drug targets and c) involved in cell-cell signaling processes. Finally, PH are a) structurally versatile, b) enriched with essential proteins primarily involved in housekeeping processes (transcription and replication). The fact that the proteins belonging to these three categories form three distinct sets in terms of their molecular properties reveals the existence of trichotomy among hubs and bottlenecks, and this knowledge is of paramount importance while prioritizing protein targets for further studies such as drug design and disease association studies based on their network centrality values.

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. It is known that the SRCR domains as well as LDLRA repeat harboring proteins mediate endocytosis of viruses and certain ligands. Based on this, we put forward a hypothesis that the exposed hNECD binds to the S2' as both are at an interaction proximity soon after S2 is processed by the SPD and this interaction may lead to the endocytosis of virus. Based on this hypothesis we have modelled the hNECD structure, followed by docking studies with the known 3D structure of S2'. The interaction interface of hNECD with S2' was further used for virtual screening of FDA-approved drug molecules and Indian medicinal plant-based compounds. We also mapped the known mutations of concern and mutations of interest on interaction interface of S2' and found that none of the known mutations map onto the interaction interface. This indicates that targeting the interaction between the hNECD of TMPRSS2 and S2' may serve as an attractive therapeutic target.Communicated by Ramaswamy H. Sarma.

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. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

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). In addition to the classical triad of symptoms including loss (or reduction) of ectodermal structures, such as hair, teeth, and sweat glands, we detected additional HED-related clinical features including facial dysmorphism and hyperpigmentation in several patients. Interestingly, global developmental delay was identified as an unusual clinical symptom in many patients. More importantly, we identified 22 causal pathogenic variants that included 15 missense, four small in-dels, and one nonsense, splice site, and large deletion each. Interestingly, we detected 12 unique (India-specific) pathogenic variants. Of the 29 XLHED families analyzed, 11 (38%) harbored pathogenic variant localized to the furin cleavage site. A comparison with HGMD revealed significant differences in the frequency of missense pathogenic variants; involvement of specific exons and/or protein domains and transition/transversion ratios. A significantly higher proportion of missense pathogenic variants (33%) localized to the EDA furin cleavage when compared to HGMD (7%), of which p.R155C, p.R156C, and p.R156H were detected in three families each. Therefore, the first comprehensive analysis of XLHED from India has revealed several unique features including unusual clinical symptoms and high frequency of furin cleavage site pathogenic variants.

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. In contrast to most globular/ordered proteins whose amino acids composition is considered as standard, there are several classes of proteins (e.g., transmembrane proteins) in which certain types of amino acid (e.g., hydrophobic residues) are enriched. These compositional differences among various classes of proteins are manifested in their underlying residue substitution frequencies. Therefore, each of the compositionally distinct class of proteins or protein segments should be studied using specific scoring matrices that reflect their distinct residue substitution pattern. In this review, we describe the development and application of various substitution scoring matrices peculiar to proteins with standard and biased compositions. Along with most commonly used standard matrices (PAM, BLOSUM, MD and VTML) that act as default parameters in various homologs search and alignment tools, different substitution scoring matrices specific to compositionally distinct class of proteins are discussed in detail.

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. Hence, these substitution-scoring matrices are mostly inappropriate for homology searches involving proteins enriched with disordered regions as the disordered regions have distinct amino acid compositional bias, and therefore expected to have undergone amino acid substitutions that are distinct from those in the ordered regions. We, therefore, developed a novel series of substitution scoring matrices referred to as EDSSMat by exclusively considering the substitution frequencies of amino acids in the disordered regions of the eukaryotic proteins. The newly developed matrices were tested for their ability to detect homologs of proteins enriched with disordered regions by means of SSEARCH tool. The results unequivocally demonstrate that EDSSMat matrices detect more number of homologs than the widely used BLOSUM, PAM and other standard matrices, indicating their utility value for homology searches of intrinsically disordered 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. Protein disorder in viral proteins and hVIPs varies from one subcellular location to another. In any given viral-human PPI pair, at least one of the two proteins is structurally disordered suggesting that disorder associated conformational flexibility as one of the characteristic features of virus-host interaction. Further analyses reveal that hVIPs are (i) slowly evolving proteins, (ii) associated with high centrality scores in human-PPI network, (iii) involved in multiple pathways, (iv) enriched in eukaryotic linear motifs (ELMs) associated with protein modification, degradation and regulatory processes, (v) associated with high number of splice variants and (vi) expressed abundantly across multiple tissues. These aforementioned findings suggest that conformational flexibility, spatial diversity, abundance and slow evolution are the characteristic features of the human proteins targeted by viral proteins. Hu-Vir PPIs are mostly mediated via domain-motif interactions (DMIs) where viral proteins employ motifs that mimic host ELMs to bind to domains in human proteins. DMIs are shared among viruses belonging to different families indicating a possible convergent evolution of these motifs to help viruses to adopt common strategies to subvert host cellular pathways. AVAILABILITY AND IMPLEMENTATION: Hu-Vir PPI data, DDI and DMI data for human-virus PPI can be downloaded from http://cdfd.org.in/labpages/computational_biology_datasets.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. We also show that the high "centrality" of these genes/nodes could in part be explained by the presence of a large number of promiscuous domains.

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. Despite having a similar number of binding surfaces and interacting domains, TSHs are associated with a lower degree of centrality as compared with HKHs, suggesting that TSHs are "unsaturated" with regard to their binding capability and are perhaps evolving with regard to their interactions. TSHs are less abundantly expressed as compared with HKHs and are enriched with PEST motifs, indicating their tight regulation. All of these properties of TSHs and HKHs correlate with their distinct functional roles; TSHs are involved in tissue-specific functional roles, viz., secretors, receptors, and signaling proteins, whereas HKHs are involved in core-cellular functions such as transcription, translation, and so on. Our study, therefore, brings forth a clear and distinct classification of hubs simply based on their expression breadth and further assumes significance in the light of the highly debated dichotomy of date and party hubs, which is based on the coexpression pattern of hubs with their partners.

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. In this communication, we demonstrate that in the eukaryotic protein-protein interaction networks hubs tend to have a significantly higher number of splice variants than nonhubs. Also, hubs have a distinct frequency distribution of splice variants as compared to nonhubs. Furthermore, nodes with high number of splice variants, in general, are associated with high structural disorderedness. We also show that the degree of a node is substantially contributed by its structurally disordered splice variants. This suggests that the propensity of a node for a large number of interactions arises as a consequence of structurally disordered splice variants. Our work, therefore, sheds light on the phenomenon of alternative splicing as a significant contributor toward the "connection diversity" of nodes in a eukaryotic PPI network and hence to its functionality.

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. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes.

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-ß, α/ß and α + ß. 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. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.