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Rathod, S. B.

Publications and source records attributed to Rathod, S. B..

4 recordsLinked to original sources

Human blood plasma proteins modeling and binding affinities with Δ9-tetrahydrocannabinol active metabolites: In silico approach

Tetrahydrocannabinol (THC) is a key psychotropic constituent of cannabis sativa. It is also known as {Delta}9-tetrahydrocannabinol ({Delta}9-THC). Previous study suggested that owing to its high lipophilicity, it piles up in adipose tissue and it is disseminated into blood stream for prolonged time. Research suggests that numerous diseases such as multiple sclerosis, neurodegenerative disorders, epilepsy, schizophrenia, osteoporosis, cancer, glaucoma and cardiovascular disorders can be treated using this substance. However, apart from having therapeutic potential, many studies have reported detrimental outcomes along with addiction of {Delta}9-THC for short-term and long-term consumption. Thus, in this study, we determined the binding affinities of {Delta}9-THC and its two active metabolites, 11-Hydroxy-{Delta}9-tetrahydrocannabinol (11-OH-{Delta}9-THC) and 8beta,11-dihydroxy-{Delta}9-tetrahydrocannabinol (8{beta},11-diOH-{Delta}9-THC) with 401 human blood plasma proteins using molecular docking analysis. Results show that {Delta}9-THC has greater binding potential with plasma proteins as compared to other two metabolites. Overall, ADGRE5, ALB, APOA5, APOD, CP, PON1 and PON3 proteins showed the highest binding affinities with three cannabis metabolites.

bioinformatics↗

Time series analysis of SARS-CoV-2 genomes and correlations among highly prevalent mutations

The efforts of the scientific community to tame the recent SARS-CoV-2 pandemic seems to have been diluted by the emergence of new viral strains. Therefore, it becomes imperative to study and understand the effect of mutations on viral evolution, fitness and pathogenesis. In this regard, we performed a time-series analysis on 59541 SARS-CoV-2 genomic sequences from around the world. These 59541 genomes were grouped according to the months (January 2020-March 2021) based on the collection date. Meta-analysis of this data led us to identify highly significant mutations in viral genomes. Correlation and Hierarchical Clustering of the highly significant mutations led us to the identification of sixteen mutation pairs that were correlated with each other and were present in >30% of the genomes under study. Among these mutation pairs, some of the mutations have been shown to contribute towards the viral replication and fitness suggesting the possible role of other unexplored mutations in viral evolution and pathogenesis. Additionally, we employed various computational tools to investigate the effects of T85I, P323L, and Q57H mutations in Non-structural protein 2 (Nsp2), RNA-dependent RNA polymerase (RdRp) and Open reading frame 3a (ORF3a) respectively. Results show that T85I in Nsp2 and Q57H in ORF3a mutations are deleterious and destabilize the parent protein whereas P323L in RdRp is neutral and has a stabilizing effect. The normalized linear mutual information (nLMI) calculations revealed the significant residue correlation in Nsp2 and ORF3a in contrast to reduce correlation in RdRp protein.

bioinformatics↗

Dynamic play between human N-α-acetyltransferase D and H4-mutant histones: Molecular dynamics study

N-terminal acetyltransferases (NATs) are overexpressed in various cancers. Specifically in lung cancer, human N--acetyltransferase D (hNatD) is upregulated and prevents the histone H4 N-terminal serine phosphorylation, leading to the epithelial-to-mesenchymal transition (EMT) of cancer cells. hNatD facilitates histone H4 N--terminal serine acetylation and halts the CK2-mediated serine phosphorylation. In the present study, we report the effects of four N-terminal mutant (S1C, R3C, G4D and G4S) histone H4 peptides on their bindings with hNatD by employing a molecular dynamics simulation. We also used graph theory-based analyses to understand residue correlation and communication in hNatD under the influence of WT and MT H4 peptides. Results show that S1C, R3C and G4S mutant peptides have significant stability at the catalytic site of hNatD. However, S1C, G4D and G4S peptides disrupt hNatD structure. Additionally, intramolecular hydrogen bond analysis reveals greater stability of hNatD in complex with R3C peptide. Further, intermolecular hydrogen bond analysis of acetyl-CoA with hNatD and its RMSD analysis in five complexes indicate that cofactor has greater stability in WT and R3C complexes. Our findings support previously reported experimental study on impacts of H4 mutations on its hNatD-mediated acetylation catalytic efficiency. The betweenness centrality (BC) analysis further gives insight into the hNatD residue communication dynamics that can be exploited to target hNatD using existed or novel drug candidates therapeutically. SECONDARY ABSTRACTMany N-terminal acetyltransferases (NATs) enzymes play important role in post-translational modification of histone tails. Research showed that these enzymes have been reported upregulated in many cancers. NatD is known to acetylate H4/H2A at the N-terminal. During lung cancer, this enzyme competes with the protein kinase CK2 and block the phosphorylation of H4 and, acetylates. Also, we observed that H4 has various mutations at the N-terminal and we considered only four mutations (S1C, R3C, G4D and G4S) to study the impacts of these mutations on H4 binding with NatD using MD simulation. Our results show that R3C stabilizes the NatD whereas remaining mutations destabilize the NatD. Thus, mutations have significant impacts on NatD structure. Our finding supports previous analysis also. SIGNIFICANCEOur main objective in this study was to understand the structural and dynamics of hNatD under the influence of WT and MT H4 histones bindings. Previous experimental study reported that mutations on H4 N-terminus reduce the catalytic efficiency of N-Terminal acetylation. But here, we performed molecular-level study thus, we can understand how these mutations (S1C, R3C, G4D and G4S) cause significant depletion in catalytic efficiency of hNatD. Another, interesting observation is that enzymatic activity of hNatD is altered due to the considerably large deviation of acetyl-CoA from its original position (G4D). Further, simulation and correlation data suggest which regions of the hNatD are highly flexible and rigid and, which domains or residues have the correlation and anticorrelation. As hNatD is overexpressed in lung cancer, it is an important drug target for the cancer hence, our study provides structural information to target hNatD.

biophysics↗

Protein-protein docking analysis reveals efficient binding and complex formation between the human nuclear transport proteins

The nuclear protein transport between the nucleus and cytosol can be considered a core process of cell regulation. Specially designed proteins in nature such as importins, exportins, and some other transporters facilitate this transport in the cell and control the cellular processes. Transient and weak protein-protein interactions are basis of these various biomolecular processes. Prior to cargo transports, the transport proteins recognize the Nuclear localization signals (NLSs) and Nuclear export signals (NESs) of cargo proteins and, bind to the RanGTP. Also, these proteins bind with other similar protein subunits along with RanGTP to transport cargos. Cell is enormously crowded place where DNA, RNA, proteins, lipids and small molecules cooperatively facilitate numerous cellular processes. In such environment, existence of nonspecific interactions between proteins is quite obvious. Considering this hypothesis, in this study, protein-protein docking approach was applied to determine the binding affinities of 12 human nuclear transport proteins. Results showed that KPNA1, TNPO1 and TNPO3 have greater affinity to bind with other transport proteins. Also, among 78 complexes (12 homodimers and 66 heterodimers), KPNA1-KPNB1, KPNA1-TNPO1 and KPNA1-TNPO3 complexes have the highest stability. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/436462v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@d81d48org.highwire.dtl.DTLVardef@6a929dorg.highwire.dtl.DTLVardef@bfa16eorg.highwire.dtl.DTLVardef@ff610b_HPS_FORMAT_FIGEXP M_FIG C_FIG Initially, 12 human nuclear transport proteins PDB structures were retrieved from the 1. Protein data bank (PDB). These proteins had some missing terminals and residues thus, we used 2. SWISS-MODEL and 3. MODELLER v.10.1 to model those regions in these proteins. Next, we used widely popular web server, 4. ClusPro v.2.0 for protein-protein docking analysis among 12 proteins. Then, we employed 5. PRODIGY web server to calculate the binding affinities of 78 complexes (12 homodimers & 66 heterodimers). Finally, we utilised three web tools, 6. Arpeggio, 7. PIMA and 8. PDBePISA to analyse top-three complexes (KPNA1-KPNB1, KPNA1-TNPO1 & TNPO3) for in-depth interactions and energetics.

bioinformatics↗