BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data

Identification of the genomic features responsible for the progression of Multiple Myeloma (MM) cancer from its precancerous stage MGUS can improve the understanding of the disease pathogenesis and, in devising suitable preventive and treatment measures. We have designed an innovative AI-based model, namely, the Bio-inspired Deep Learning architecture for the identification of altered Signaling Pathways (BDL-SP) to discover pivotal genomic biomarkers that can potentially distinguish MM from MGUS. The proposed BDL-SP model comprehends gene-gene interactions using the PPI network and analyzes genomic features using a deep learning (DL) architecture to identify significantly altered genes and signaling pathways in MM and MGUS. For this, whole exome sequencing data of 1174 MM and 61 MGUS patients were analyzed. In the quantitative benchmarking with the other popular machine learning models, BDL-SP performed almost similar to the two other best performing predictive ML models of Random Forest and CatBoost. However, an extensive post-hoc explainability analysis, capturing the application specific nuances, clearly established the significance of the BDL-SP model. This analysis revealed that BDL-SP identified a maximum number of previously reported oncogenes, tumor-suppressor genes, and actionable genes of high relevance in MM as the top significantly altered genes. Further, the post-hoc analysis revealed a significant contribution of the total number of single nucleotide variants (SNVs) and genomic features associated with synonymous SNVs in disease stage classification. Finally, the pathway enrichment analysis of the top significantly altered genes showed that many cancer pathways are selectively and significantly dysregulated in MM compared to its precursor stage of MGUS, while a few that lost their significance with disease progression from MGUS to MM were actually related to the other disease types. These observations may pave the way for appropriate therapeutic interventions to halt the progression to overt MM in the future.

Correlation of changes in subclonal architecture with progression in the MMRF CoMMpass study

Multiple myeloma (MM) is a heterogeneous plasma cell proliferative disorder that arises from its premalignant precursor stages through a complex cascade of interactions between clonal mutations and co-evolving microenvironment. The temporo-spatial evolutionary trajectories of MM are established early during myelomatogenesis in precursor stages and retained in MM. Such molecular events impact subsequent disease progression and clinical outcomes. Identification of clonal sweeps of actionable gene targets in MM could reveal potential vulnerabilities that may exist in early stages and thus potentiate prognostication and customization of early therapeutic interventions. We have evaluated clonal evolution at multiple time points in 76 MM patients enrolled in the MMRF CoMMpass study. The major findings of this study are (a) MM progresses predominantly through branching evolution, (b) there is a heterogeneous spectrum of mutational landscapes that include unique actionable gene targets at diagnosis compared to progression, (c) unique clonal gains/ losses of mutant driver genes can be identified in patients with different cytogenetic aberrations, (d) there is a significant correlation between co-occurring oncogenic mutations/ co-occurring subclones e.g., with mutated TP53+SYNE1, NRAS+MAGI3, and anticorrelative dependencies between FAT3+FCGBP gene pairs. Such co-trajectories may synchronize molecular events of drug response, myelomatogenesis and warrant future studies to explore their potential for early prognostication and development of risk stratified personalized therapies in MM.

Characterizing the mutational landscape of MM and its precursor MGUS

Mutational Signatures and Tumor mutational burden (TMB) have emerged as prognostic biomarkers in cancer genomics. However, the association of TMB with overall survival (OS) is still unknown in newly diagnosed multiple myeloma (NDMM) patients. Further, the change in the mutational spectrum involving both synonymous and non-synonymous mutations as MGUS progresses to MM is unexplored. This study addresses both these aspects via extensive evaluation of the mutations in MGUS and NDMM. WES data of 1018 NDMM patients and 61 MGUS patients collected from three different global regions were analyzed in this study. Single base substitutions, mutational signatures and TMB were inferred from the variants identified in MGUS and MM patients. The cutoff value for TMB was estimated to divide patients into low TMB and high TMB (hypermutators) groups. This study finds a change in the mutational spectrum with a statistically significant increase from MGUS to MM. There was a statistically significant increase in the frequency of all the three categories of variants, non-synonymous (NS), synonymous (SYN), and others (OTH), from MGUS to MM (P<0.05). However, there was a statistically significant rise in the TMB values for TMB_NS and TMB_SYN only. We also observed that 3' and 5'UTR mutations were more frequent in MM and might be responsible for driving MGUS to MM via regulatory binding sites. NDMM patients were also examined separately along with their survival outcomes. The frequency of hypermutators was low in MM with poor OS and PFS outcome. We observed a statistically significant rise in the frequency of C>A and C>T substitutions and a statistically significant decline in T>G substitutions in the MM patients with poor outcomes. Additionally, there was a statistically significant increase in the TMB of the patients with poor outcome compared to patients with a superior outcome. A statistically significant association between the APOBEC activity and poor overall survival in MM was discovered. These findings have potential clinical relevance and can assist in designing risk-adapted therapies to inhibit the progression of MGUS to MM and prolong the overall survival in high-risk MM patients.

Branching clonal evolution patterns predominate mutational landscape in multiple myeloma

Multiple Myeloma (MM) arises from malignant transformation and deregulated proliferation of clonal plasma cells (PCs) harbouring heterogeneous molecular anomalies. The effect of evolving mutations on clone fitness and their cellular prevalence shapes the progressing myeloma genome and impacts clinical outcomes. Although clonal heterogeneity in MM is well established, which subclonal mutations emerge/persist/perish with progression in MM and which of these can be targeted therapeutically remains an open question. In line with this, we have sequenced pairwise whole exomes of 62 MM patients collected at two time points, i.e., at diagnosis and on progression. Somatic variants were called using a novel ensemble approach where a consensus was deduced from four variant callers (Illumina's Dragen, Strelka2, SomaticSniper and SpeedSeq) and actionable/druggable gene targets were identified. A marked intraclonal heterogeneity was observed. Branching evolution was observed among 72.58% patients, of whom 64.51% had low TMBs (<10) and 61.29% had 2 or more founder clones. The hypermutator patients (with high TMB levels ≥10 to ≤100) showed a significant decrease in their TMBs from diagnosis (median TMB 77.11) to progression (median TMB 31.22). A distinct temporal fall in subclonal driver mutations was identified recurrently across diagnosis to progression e.g., in PABPC1, BRAF, KRAS, CR1, DIS3 and ATM genes in 3 or more patients suggesting such patients could be treated early with target specific drugs like Vemurafenib/Cobimetinib. An analogous rise in driver mutations was observed in KMT2C, FOXD4L1, SP140, NRAS and other genes. A few drivers such as FAT4, IGLL5 and CDKN1A retained consistent distribution patterns at two time points. These findings are clinically relevant and point at consideration of evaluating multi time point subclonal mutational landscapes for designing better risk stratification strategies and tailoring time to time risk adapted combination therapies in future.

Protein-protein interaction and in silico mutagenesis studies on IL17A and its peptide inhibitor

Protein-protein interactions of Interleukin-17 (IL17) play vital role in the autoimmune and inflammatory diseases, such as rheumatoid arthritis, multiple sclerosis, and psoriasis. Potent therapeutics for these diseases could be developed by blocking or modulating these interactions through biologics, peptide inhibitors and small molecule inhibitors. Unlike biologics, peptide inhibitors are cost effective and can be orally available. Peptide inhibitors do not require a binding groove as that of small molecules either. Therefore, crystal structure of IL17A in complex with a high affinity peptide inhibitor (HAP) (1-IHVTIPADLWDWIN-14) is investigated with an aim to find hot spots that could improve its potency. An in silico mutagenesis strategy was implemented using FoldX PSSM to scan for positions tolerant to amino acid substitution. Three positions T4, A7, and N14 showed improved stability when mutated with 'F/M/Y', 'P' and 'F/M/Y', respectively. A set of 31 mutant peptides are designed through combinations of these tolerant mutations using Build Model application of FoldX. Binding affinity and interactions of 31 peptides are assessed through protein-peptide docking and binding free energy calculations. Two peptides namely, P1 ("1-IHVTIPPDLWDWIY-14") and P2 ("1-IHVMIPPDLWDWIF-14") showed better binding affinity to IL17A dimerization site compared to HAP. Interactions of P1, P2 and HAP are also analyzed through 100 ns molecular dynamics simulations using GROMACS v5.0. The results revealed that the P2 peptide likely to offer better potency compared to HAP and P1. Therefore, the P2 peptide can be synthesized to develop oral therapies for autoimmune and inflammatory diseases with further experimental evaluations.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02856-y.

Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data

Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these signatures overlap meagrely plausibly due to complexity of myeloma genome, diversity in primary cells studied, molecular technologies/ analytical tools utilized. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We have conducted genome-wide meta-analysis of DEGs/DEMs in MM versus Normal Plasma Cells (NPCs) and derived unified putative signatures for MM. 100 DEMs and 1,362 DEGs were found deranged between MM and NPCs. Signatures of 37 DEMs ('Union 37') and 154 DEGs ('Union 154') were deduced that shared 17 DEMs and 22 DEGs with published prognostic signatures, respectively. Two miRs (miR-16-2-3p, 30d-2-3p) correlated with survival outcomes. PPI analysis identified 5 topmost functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP). Transcription factor regulatory networks were determined for five seed DEGs with ≥ 4 biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) and three topmost up/ down regulated DEMs (miR-23b, 195, let7b/ miR-20a, 155, 92a). Further studies are warranted to establish and translate prognostic potential of these signatures for MM.

In silico discovery and evaluation of phytochemicals binding mechanism against human catechol-O-methyltransferase as a putative bioenhancer of L-DOPA therapy in Parkinson disease

Levodopa (L-DOPA) therapy is normally practised to treat motor pattern associated with Parkinson disease (PD). Additionally, several inhibitory drugs such as Entacapone and Opicapone are also cosupplemented to protect peripheral inactivation of exogenous L-DOPA (~80%) that occurs due to metabolic activity of the enzyme catechol-O-methyltransferase (COMT). Although, both Entacapone and Opicapone have U.S. Food and Drug Administration approval but regular use of these drugs is associated with high risk of side effects. Thus, authors have focused on in silico discovery of phytochemicals and evaluation of their effectiveness against human soluble COMT using virtual screening, molecular docking, drug-like property prediction, generation of pharmacophoric property, and molecular dynamics simulation. Overall, study proposed, nine phytochemicals (withaphysalin D, withaphysalin N, withaferin A, withacnistin, withaphysalin C, withaphysalin O, withanolide B, withasomnine, and withaphysalin F) of plant Withania somnifera have strong binding efficiency against human COMT in comparison to both of the drugs i.e., Opicapone and Entacapone, thus may be used as putative bioenhancer in L-DOPA therapy. The present study needs further experimental validation to be used as an adjuvant in PD treatment.

Identification of potential inhibitors for mycobacterial uridine diphosphogalactofuranose-galactopyranose mutase enzyme: A novel drug target through in silico approach

Background

The Mycobacterium tuberculosis (MTB) uridine diphosphogalactofuranose (UDP)-galactopyranose mutase (UGM) is an essential flavoenzyme for mycobacterial viability and an important component of cell wall. It catalyzes the interconversion of UDP-galactopyranose into UDP-galactofuranose, a key building block for cell wall construction, essential for linking the peptidoglycan and mycolic acid cell wall layers in MTB through a 2-keto intermediate. Further, as this enzyme is not present in humans, it is an excellent therapeutic target for MTB. Thus, inhibition of this UGM enzyme is a good approach to explore new anti-TB drug. This study aims to find novel and effective inhibitors against UGM from reported natural phytochemicals and ZINC database using virtual screening approach.

Methods

In this study, 148 phytochemicals with reported antitubercular activity and 5280 ZINC compounds with 70% structural similarity with the natural substrate of UGM (UDP-galactopyranose and UDP-galactofuranose) were screened against UGM.

Results

In virtual screening, 19 phytochemicals and 477 ZINC compounds showed comparatively better binding affinity than natural substrates. Among them, best 10 compounds from each group were proposed as potential inhibitors for UGM based on the binding energy and protein-ligand interaction analysis. Among phytochemicals, three compounds, namely, tiliacorine, amentoflavone, and 2'-nortiliacorinine showed highest binding affinity (binding energy of -10.5, -10.4, and -10.3 Kcal/mol, respectively), while among ZINC compounds, ZINC08219848 and ZINC08217649, showing highest binding affinity (binding energy of -10.0 and -9.7 Kcal/mol, respectively) toward UGM as compared to its substrates.

Conclusion

These selected compounds may be proposed as potential inhibitors of UGM and need to be tested in TB culture studies in vitro to assess their anti-TB activity.

Discovery of T-cell Driven Subunit Vaccines from Zika Virus Genome: An Immunoinformatics Approach

The recent outbreaks of Zika virus and the absence of a specific therapy have necessitated to identify T-cell-stimulating antigenic peptides as potential subunit vaccine candidates. The translated ssRNA (+) genome of Zika virus was explored in EMBOSS antigenic and VaxiJen to predict 63 peptides as potential antigens. Three MHC-II binding peptide prediction tools, viz. NetMHCIIpan, PREDIVAC and immune epitope database (IEDB) were employed in consensus on 63 antigenic peptides to propose 14 T-helper cell epitopes. Similarly, analysis on 63 antigenic peptides through NetMHC, NetCTL and IEDB MHC-I binding peptide prediction tool led to identification of 14 CTL epitopes. Seven T-cell epitopes, C:44-66, M:135-149, NS2A:124-144, NS3:421-453, NS3:540-554, NS4B:90-134 and NS4B:171-188, are observed to share overlapping MHC-I and MHC-II binding motifs and hence, are being proposed to constitute minimum T-cell antigens to elicit protective T-cell immune response against Zika. Three of them, C:44-66, NS3:421-453 and NS3:540-554 are identified to be conserved across all the selected strains of Zika virus. Moreover, the 21 T-cell epitopes are non-self to humans and exhibited good coverage in variable populations of 14 geographical locations. Therefore, 21 T-cell epitopes are proposed as potential subunit vaccines against Zika virus.

Virtual Screening for Potential Inhibitors of NS3 Protein of Zika Virus

Zika virus (ZIKV) is a mosquito borne pathogen, belongs to Flaviviridae family having a positive-sense single-stranded RNA genome, currently known for causing large epidemics in Brazil. Its infection can cause microcephaly, a serious birth defect during pregnancy. The recent outbreak of ZIKV in February 2016 in Brazil realized it as a major health risk, demands an enhanced surveillance and a need to develop novel drugs against ZIKV. Amodiaquine, prochlorperazine, quinacrine, and berberine are few promising drugs approved by Food and Drug Administration against dengue virus which also belong to Flaviviridae family. In this study, we performed molecular docking analysis of these drugs against nonstructural 3 (NS3) protein of ZIKV. The protease activity of NS3 is necessary for viral replication and its prohibition could be considered as a strategy for treatment of ZIKV infection. Amongst these four drugs, berberine has shown highest binding affinity of –5.8 kcal/mol and it is binding around the active site region of the receptor. Based on the properties of berberine, more similar compounds were retrieved from ZINC database and a structure-based virtual screening was carried out by AutoDock Vina in PyRx 0.8. Best 10 novel drug-like compounds were identified and amongst them ZINC53047591 (2-(benzylsulfanyl)-3-cyclohexyl-3H-spiro[benzo[h]quinazoline-5,1'-cyclopentan]-4(6H)-one) was found to interact with NS3 protein with binding energy of –7.1 kcal/mol and formed H-bonds with Ser135 and Asn152 amino acid residues. Observations made in this study may extend an assuring platform for developing anti-viral competitive inhibitors against ZIKV infection.

Identification of Suitable Natural Inhibitor against Influenza A (H1N1) Neuraminidase Protein by Molecular Docking

The influenza A (H1N1) virus, also known as swine flu is a leading cause of morbidity and mortality since 2009. There is a need to explore novel anti-viral drugs for overcoming the epidemics. Traditionally, different plant extracts of garlic, ginger, kalmegh, ajwain, green tea, turmeric, menthe, tulsi, etc. have been used as hopeful source of prevention and treatment of human influenza. The H1N1 virus contains an important glycoprotein, known as neuraminidase (NA) that is mainly responsible for initiation of viral infection and is essential for the life cycle of H1N1. It is responsible for sialic acid cleavage from glycans of the infected cell. We employed amino acid sequence of H1N1 NA to predict the tertiary structure using Phyre2 server and validated using ProCheck, ProSA, ProQ, and ERRAT server. Further, the modelled structure was docked with thirteen natural compounds of plant origin using AutoDock4.2. Most of the natural compounds showed effective inhibitory activity against H1N1 NA in binding condition. This study also highlights interaction of these natural inhibitors with amino residues of NA protein. Furthermore, among 13 natural compounds, theaflavin, found in green tea, was observed to inhibit H1N1 NA proteins strongly supported by lowest docking energy. Hence, it may be of interest to consider theaflavin for further in vitro and in vivo evaluation.

Need assessment of enhancing the weightage of applied biochemistry in the undergraduate curriculum at MGIMS, sevagram

In order to review the need assessment of enhancing the weightage of Applied Biochemistry in the undergraduate curriculum at Mahatma Gandhi Institute of Medical Sciences (MGIMS), Sevagram, a validated questionnaire was sent to 453 participants which include 387 undergraduate students, 11 interns, 23 postgraduate students, and 32 faculty members. A web-based data collection and analysis tool was designed for online questionnaire distribution, data collection, and analysis. Response rate was 100%. Most of the respondents agreed that the subject Biochemistry has relevance in clinical practice (81.24%) and applied based learning of Biochemistry by medical undergraduates would help in overall improvement in the health standards/patients care (83.44%). According to 65.12% respondents, most of the medical undergraduates read Biochemistry just for examination purpose only. Nearly half of the respondents agreed that minute details of biochemical reactions were not much useful in clinical practice (53.86%) and the vast majority of diagrammatic cycles memorized by the medical undergraduates had no relevance in clinical practice (51.21%), the decreased interest in learning the Applied Biochemistry was due to more amount of clinically irrelevant information taught to medical undergraduates (73.51%), there was a need to rethink for removing the diagrammatic biochemical cycles from curriculum for medical undergraduates (48.12%), the less learning of Applied Biochemistry or competencies would affect the clinical skills and knowledge of medical undergraduates (70.42%). The result of this study suggests that there is need for restructuring the Biochemistry curriculum with more clinical relevance. © 2016 by The International Union of Biochemistry and Molecular Biology, 44:230-240, 2016.

Study of mechanism of interaction of truncated isoniazid-nicotinamide adenine dinucleotide adduct against multiple enzymes of Mycobacterium tuberculosis by a computational approach

OBJECTIVE/BACKGROUND

Isoniazid (INH) is one of the effective antituberculosis (TB) drugs used for TB treatment. However, most of the drug-resistant Mycobacterium tuberculosis (MTB) clinical strains are resistant to INH, a first-line antituberculous drug. Certain metabolic enzymes such as adenosylhomocysteinase (Rv3248c), universal stress protein (Rv2623), nicotinamide adenine dinucleotide (reduced)-dependent enoyl-acyl carrier protein reductase (Rv1484), oxidoreductase (Rv2971), dihydrofolate reductase (Rv2763c), pyrroline-5-carboxylate dehydrogenase (Rv1187) have been identified to bind INH-nicotinamide adenine dinucleotide (INH-NAD) and INH-nicotinamide adenine dinucleotide phosphate adducts coupled to Sepharose resin. These enzymes are reported to be involved in many important biochemical processes of MTB, including cysteine and methionine metabolism, mycobacterial growth regulation, mycolic acid biosynthesis, detoxification of toxic metabolites, folate biosynthesis, etc. The truncated INH-nicotinamide adenine dinucleotide (oxidized) adduct, 4-isonicotinoylnicotinamide, isolated from urine samples of human TB patients treated with INH therapy is proposed to have antimycobacterial activity.

METHODS

To understand the mechanism of interaction of the truncated INH-NAD adduct, binding energy studies were carried out on the aforementioned six enzymes with known three-dimensional structures using AutoDock4.2.

RESULTS

In silico docking analysis of these MTB enzymes with the truncated INH-NAD adduct showed favorable binding interactions with docking energies ranging from -5.29 to -7.07 kcal/mol.

CONCLUSION

Thus, in silico docking study revealed that the INH-NAD adduct, which is generated in vivo after INH activation, may undergo spontaneous hydrolysis to form the truncated INH-NAD adduct and further binds and inhibits multiple enzymes of MTB, in addition to InhA, confirming that INH is an effective anti-TB drug acting at multiple enzymes. Further analysis of amino acid residues in the active site of INH-NAD-binding proteins showed the probable presence of catalytic triad in four enzymes possibly involved in INH binding to the enzyme.

In Silico Docking to Explicate Interface between Plant-Originated Inhibitors and E6 Oncogenic Protein of Highly Threatening Human Papillomavirus 18

The leading cause of cancer mortality globally amongst the women is due to human papillomavirus (HPV) infection. There is need to explore anti-cancerous drugs against this life-threatening infection. Traditionally, different natural compounds such as withaferin A, artemisinin, ursolic acid, ferulic acid, (-)-epigallocatechin-3-gallate, berberin, resveratrol, jaceosidin, curcumin, gingerol, indol-3-carbinol, and silymarin have been used as hopeful source of cancer treatment. These natural inhibitors have been shown to block HPV infection by different researchers. In the present study, we explored these natural compounds against E6 oncoprotein of high risk HPV18, which is known to inactivate tumor suppressor p53 protein. E6, a high throughput protein model of HPV18, was predicted to anticipate the interaction mechanism of E6 oncoprotein with these natural inhibitors using structure-based drug designing approach. Docking analysis showed the interaction of these natural inhibitors with p53 binding site of E6 protein residues 108-117 (CQKPLNPAEK) and help reinstatement of normal p53 functioning. Further, docking analysis besides helping in silico validations of natural compounds also helped elucidating the molecular mechanism of inhibition of HPV oncoproteins.

Virtual screening for potential inhibitors of high-risk human papillomavirus 16 E6 protein

Human papillomavirus (HPV), a life-threatening infection is the leading cause of cancer mortality among women worldwide and needs for designing anti-cancerous drugs. In the present study, we explored specific novel inhibitors against E6 onco-protein of high risk HPV 16, known to inactivate tumor suppressor p53 protein. A homology model of HPV 16 E6 was built and validated using bioinformatics approach. A total of 5000 drug like compounds were downloaded from ZINC database based on the properties similar to the known inhibitor Jaceosidin (5, 7-Dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-6-methoxy-4H-chromen-4-one). Virtual-ligand-screenings approaches were applied to screen appropriate drug like compounds using molecular docking program Auto Dock Vina in PyRx 0.8 and 5 best novel drug-like compounds were identified as potential competitive inhibitors against HPV 16 E6 compared to Jaceosidin. Two amongst these 5 identified most potential inhibitors, N-[(5-methyl-1Hbenzimidazol-2-yl)methyl]-4-oxo-3, 4-dihydrophthalazine-1-carboxamide and 6-[3-(3-fluoro-4-methyl-phenyl)-1, 2, 4-oxadiazol-5-yl]-1, 4-dihydroquinoxaline-2, 3-dione were found to interact with E6 with binding energy of -7.7 and -7.0 Kcal/mol respectively and form H-bonds with p53 binding site of E6 protein residues 113-122 (CQKPLCPEEK). These two inhibitors may help restoration of p53 functioning. The Bioinformatics approach extends a promising platform for developing anti-cancerous competitive inhibitors targeting high-risk HPV 16.

Understanding Rifampicin Resistance in Tuberculosis through a Computational Approach

The disease tuberculosis, caused by Mycobacterium tuberculosis (MTB), remains a major cause of morbidity and mortality in developing countries. The evolution of drug-resistant tuberculosis causes a foremost threat to global health. Most drug-resistant MTB clinical strains are showing resistance to isoniazid and rifampicin (RIF), the frontline anti-tuberculosis drugs. Mutation in rpoB, the beta subunit of DNA-directed RNA polymerase of MTB, is reported to be a major cause of RIF resistance. Amongst mutations in the well-defined 81-base-pair central region of the rpoB gene, mutation at codon 450 (S450L) and 445 (H445Y) is mainly associated with RIF resistance. In this study, we modeled two resistant mutants of rpoB (S450L and H445Y) using Modeller9v10 and performed a docking analysis with RIF using AutoDock4.2 and compared the docking results of these mutants with the wild-type rpoB. The docking results revealed that RIF more effectively inhibited the wild-type rpoB with low binding energy than rpoB mutants. The rpoB mutants interacted with RIF with positive binding energy, revealing the incapableness of RIF inhibition and thus showing resistance. Subsequently, this was verified by molecular dynamics simulations. This in silico evidence may help us understand RIF resistance in rpoB mutant strains.

Effect of single amino acid mutations on function of Mycobacterium tuberculosis H37RV and H37RA by computational approaches

BACKGROUND

Studies have been reported on genomic analysis to explain virulence of MTB H37Rv vs. MTB H37Ra strain. Proteomic comparison analysis at our centre has shown variability of 244 proteins in MTB H37Rv. A single amino acid mutation in protein sequence may cause alteration in protein structure and function that may account for virulence and drug resistance properties of pathogenic organisms.

AIM

To find the effect of single amino acid mutations on Mycobacterium tuberculosis H37Rv and H37Ra using computational approaches

METHODS

Proteins with single amino acid mutation were analysed by different mutation analysis systems such as SIFT, PolyPhen, PROVEAN and HOPE. Subsequently, structure comparison of proposed modelled structure of mutant proteins of MTB H37Ra with native proteins of MTB H37Rv was performed.

RESULTS

We observed that amongst 40 single amino acid mutated proteins of MTB H37Ra, five were found to be damaged by all the mutation analysis systems. Upon structure comparison, the RMSD values between the native and mutant type proteins were found to be significantly higher. All the five proteins showed important biological function in MTB H37Rv. Further, when these five proteins of MTB H37Ra were compared with corresponding proteins of other virulent strains of MTB (i.e. F11 and CDC 1551), similar observation was made.

CONCLUSION

The data suggests the important role of single amino acid mutation in five proteins in causing changes in the virulence and pathogenicity of clinical strains of MTB.

Elucidating Molecular Interactions of Natural Inhibitors with HPV-16 E6 Oncoprotein through Docking Analysis

Human papillomavirus (HPV) infection is the leading cause of cancer mortality among women worldwide. The life-threatening infection caused by HPV demands the need for designing anticancerous drugs. In the recent years, different compounds from natural origins, such as carrageenan, curcumin, epigallocatechin gallate, indole-3-carbinol, jaceosidin, and withaferin, have been used as a hopeful source of anticancer therapy. These compounds have been shown to suppress HPV infection by different researchers. In the present study, we explored these natural inhibitors against E6 oncoprotein of high-risk HPV-16, which is known to inactivate the p53 tumor suppressor protein. A robust homology model of HPV-16 E6 was built to anticipate the interaction mechanism of E6 oncoprotein with natural inhibitory molecules using a structure-based drug designing approach. Docking analysis showed the interaction of these natural compounds with the p53-binding site of E6 protein residues 113-122 (CQKPLCPEEK) and helped the restoration of p53 functioning. Docking analysis, besides helping in silico validation of natural compounds, also helps understand molecular mechanisms of protein-ligand interactions.

hpvPDB: An Online Proteome Reserve for Human Papillomavirus

Human papillomavirus (HPV) infection is the leading cause of cancer mortality among women worldwide. The molecular understanding of HPV proteins has significant connotation for understanding their intrusion in the host and designing novel protein vaccines and anti-viral agents, etc. Genomic, proteomic, structural, and disease-related information on HPV is available on the web; yet, with trivial annotations and more so, it is not well customized for data analysis, host-pathogen interaction, strain-disease association, drug designing, and sequence analysis, etc. We attempted to design an online reserve with comprehensive information on HPV for the end users desiring the same. The Human Papillomavirus Proteome Database (hpvPDB) domiciles proteomic and genomic information on 150 HPV strains sequenced to date. Simultaneous easy expandability and retrieval of the strain-specific data, with a provision for sequence analysis and exploration potential of predicted structures, and easy access for curation and annotation through a range of search options at one platform are a few of its important features. Affluent information in this reserve could be of help for researchers involved in structural virology, cancer research, drug discovery, and vaccine design.

MycoProtease-DB: Useful resource for Mycobacterium tuberculosis complex and nontuberculous mycobacterial proteases

MycoProtease-DB is an online MS SQL and CGI-PERL driven relational database that domiciles protease information of Mycobacterium tuberculosis (MTB) complex and Nontuberculous Mycobacteria (NTM), whose complete genome sequence is available. Our effort is to provide comprehensive information on proteases of 5 strains of Mycobacterium tuberculosis (H(37)Rv, H(37)Ra, CDC1551, F11 and KZN 1435), 3 strains of Mycobacterium bovis (AF2122/97, BCG Pasteur 1173P2 and BCG Tokyo 172) and 4 strains of NTM (Mycobacterium avium 104, Mycobacterium smegmatis MC2 155, Mycobacterium avium paratuberculosis K-10 and Nocardia farcinica IFM 10152) at gene, protein and structural level. MycoProtease-DB currently hosts 1324 proteases, which include 906 proteases from MTB complex with 237distinct proteases & 418 from NTM with 404 distinct proteases. Flexible database design and easy expandability & retrieval of information are the main features of MycoProtease-DB. All the data were validated with various online resources and published literatures for reliable serving as comprehensive resources of various Mycobacterial proteases.

AVAILABILITY

The Database is publicly available at http://www.bicjbtdrc-mgims.in/MycoProtease-DB/

MTB-PCDB: Mycobacterium tuberculosis proteome comparison database

The Mycobacterium tuberculosis Proteome Comparison Database (MTB-PCDB) is an online database providing integrated access to proteome sequence comparison data for five strains of Mycobacterium tuberculosis (H37Rv, H37Ra, CDC 1551, F11 and KZN 1435) sequenced completely so far. MTB-PCDB currently hosts 40252 protein sequence comparison data obtained through inter-strain proteome comparison of five different strains of MTB. 2373 proteins were found to be identical in all 5 strains using MTB H(37)Rv as reference strain. To enable wide use of this data, MTB-PCDB provides a set of tools for searching, browsing, analyzing and downloading the data. By bringing together, M. tuberculosis proteome comparison among virulent & avirulent strains and also drug susceptible & drug resistance strains MTB-PCDB provides a unique discovery platform for comparative proteomics among these strains which may give insights into the discovery & development of TB drugs, vaccines and biomarkers.

AVAILABILITY

The database is available for free at http://www.bicjbtdrc-mgims.in/MTB-PCDB/