The impact of oral diseases on respiratory health and the influence of respiratory infections on the oral microbiome.

OBJECTIVE: The objective of this review is to examine the relationship between oral diseases and respiratory health, investigating how oral microbiome disruptions contribute to respiratory tract infections. Additionally, it aims to explore the impact of respiratory disease symptoms and treatments on the oral microbiome. DATA SOURCES: The literature utilized in this review was sourced from studies focusing on the correlation between oral health and respiratory infections, spanning a period of 40 years. Various databases and scholarly sources were likely consulted to gather relevant research articles, reviews, and clinical studies. STUDY SELECTION: This review summarizes four decades-long research, providing insights into the intricate relationship between oral and respiratory health. It delves into how oral diseases influence respiratory tract conditions and vice versa. The selection process likely involved identifying studies that addressed the interaction between oral microbiome disruptions and respiratory complications. CONCLUSION: Oral diseases or poor oral habits have been known to increase the risk of getting respiratory infections. Modern techniques have demonstrated the relationship between oral disease and respiratory tract infections like influenza, chronic obstructive pulmonary diseases, asthma, and Pneumonia. Apart from that, the medications used to treat respiratory diseases affect oral physiological factors like the pH of saliva, and saliva flow rate, which can cause significant changes in the oral microbiome. This review provides regular oral hygiene and care that can prevent respiratory health and respiratory infections. CLINICAL SIGNIFICANCE: Understanding the intricate relationship between oral health and respiratory infections is crucial for healthcare providers. Implementing preventive measures and promoting good oral hygiene habits can reduce respiratory tract infections and improve overall respiratory health outcomes.

PHB Production by Bacillus megaterium LSRB 0103 Using Cornstarch and Urea.

Polyhydroxybutyrates (PHBs) are biopolymers that are good green alternative for synthetic carbon-based polymers, and are also one of the most researched members of the Polyhydroxyalkanoates (PHA) family. In this study, a gram-positive bacterial strain Bacillus megaterium LSRB 0103 was isolated from Pallikaranai Marshland, Chennai, India. Primary screening using Sudan Black dye revealed the presence of intracellular PHB granules. Minimal Davis Media (MDM) which was used or PHB production gave a yield of 0.7107 g/L. Subsequently, using response surface methodology (RSM), a central composite design (CCD) model was designed for media optimization having cornstarch, urea, and pH as independent variables. The findings of the CCD model were fitted into a second-order polynomial equation. The RSM model predicted the maximum PHB yield of 0.93 g/L, at these independent variable levels, cornstarch, 5 g/L; urea, 2.1 g/L; and pH 7.0; while the experimental PHB yield was 0.94 g/L, with a percentage error of 1.1%. This study is the first-time report of production of PHB by Bacillus megaterium using cornstarch and urea as substrate.

A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data.

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.

Unveiling bacterial consortium for xenobiotic biodegradation from Pichavaram mangrove forest soil: a metagenomic approach.

Pichavaram mangrove forest was established as a wetland of International Importance by Article 2.1 in April 2022 by the Ministry of Environment, Forest and Climate Change, India. Even though it is a conserved site, xenobiotic agrochemical leaching on the forest land during monsoon is inevitable. These threaten the microbial diversity in the environment. Xenobiotic degradation is achieved using bacterial consortia already acclimatised to this environment. This study aims to identify the indigenous microbial consortia able to degrade xenobiotic compounds such as fluorobenzoate, furfural, and steroids. Pichavaram mangrove metagenomic dataset was obtained by shotgun sequencing of soil DNA and processed using the automated tool SqueezeMeta. Further, the DIAMOND database provided the taxonomical classification of the microbes in each contig. With reference to the KEGG database, the selected xenobiotic degradation pathways were confirmed in the dataset. Of 1,253,029 total contigs, 1332, 72 and 1262 were involved in fluorobenzoate, furfural and steroid degradation, respectively. This study identified that microbial consortia comprising Marinobacter, Methyloceanibacter and Vibrio natriegens/Gramella sp. can degrade fluorobenzoate. While Afipia, Nitrosopumilus sp., and Phototrophicus methaneseepsis favour the degradation of furfural compound. The steroid degradation pathway possessed a plethora of bacteria belonging to the phylum Proteobacteria.

A review on applications of ß-glucosidase in food, brewery, pharmaceutical and cosmetic industries.

ß-glucosidases hydrolyse glycosidic bonds to release non-reducing terminal glucosyl residues from glycosides and oligosaccharides via catalytic mechanisms. It is very well known that the ß-glucosidase enzyme is used in biorefineries for cellulose degradation, where ß-glucosidases is the rate-limiting enzyme for the final glucose production from cellobiose. The ß-glucosidase enzyme is used as a catalyst in other industrial sectors, including pharmaceuticals, breweries, dairy, and food processing. With the aid of ß-glucosidase enzymes, cyanogenic glycosides and plant glycosides are transformed into sugar moiety and aglycones. These aglycone compounds are employed as aromatic compounds in the food processing and brewing industries. They are also used as medications and dietary supplements based on their pharmacological qualities. Applications of aglycones and the microbiological sources of ß-glucosidase in aglycone production have been discussed in this review.

CRISPR detection in metagenome-assembled genomes (MAGs) of coal mine.

Bacterial and archaeal CRISPR-Cas systems provide adaptive immune protection against foreign mobile genetic elements. When viruses infect bacteria, a small portion of the viral DNA is inserted into the bacterial DNA in a specific pattern to produce segments known as CRISPR arrays. Metagenome assembled genomes (MAGs) were used in our study to identify the CRISPR sequence for determining the interacted phage. Metagenomic data from a coal mine was used to perform a computational study. From raw reads, 206151 contigs were assembled. Then contigs were clustered into 150 Metagenome assembled genomes from which 78 non-redundant MAGs were selected. Using the CHECKM standard, seven MAGs were found to have >80 completeness and <20 contaminations. Those MAGs were analyzed for the presence of CRISPR elements. Out of seven MAGs, four MAGs have the CRISPR elements and are searched against the VIROblast database. CRISPR arrays have 4, 1, 3, and 7 spacer sequences in the MAGs of Burkholderia, Acinetobacter, Oxalobacteraceae, and Burkholderia multivorans respectively. The uncultured Caudovirales phage genomic regions were present in the genomes of Burkholderia, Oxalobacteriaceae, and Burkholderia multivorans. This study follows the unconventional metagenomics workflow to provide a better understanding of bacteria and phage interactions.

Functional metagenomics uncovers nitrile-hydrolysing enzymes in a coal metagenome.

Introduction: Nitriles are the most toxic compounds that can lead to serious human illness through inhalation and consumption due to environmental pollution. Nitrilases can highly degrade nitriles isolated from the natural ecosystem. In the current study, we focused on the discovery of novel nitrilases from a coal metagenome using in silico mining. Methods: Coal metagenomic DNA was isolated and sequenced on the Illumina platform. Quality reads were assembled using MEGAHIT, and statistics were checked using QUAST. Annotation was performed using the automated tool SqueezeMeta. The annotated amino acid sequences were mined for nitrilase from the unclassified organism. Sequence alignment and phylogenetic analyses were carried out using ClustalW and MEGA11. Conserved regions of the amino acid sequences were identified using InterProScan and NCBI-CDD servers. The physicochemical properties of the amino acids were measured using ExPASy's ProtParam. Furthermore, NetSurfP was used for 2D structure prediction, while AlphaFold2 in Chimera X 1.4 was used for 3D structure prediction. To check the solvation of the predicted protein, a dynamic simulation was conducted on the WebGRO server. Ligands were extracted from the Protein Data Bank (PDB) for molecular docking upon active site prediction using the CASTp server. Results and discussion: In silico mining of annotated metagenomic data revealed nitrilase from unclassified Alphaproteobacteria. By using the artificial intelligence program AlphaFold2, the 3D structure was predicted with a per-residue confidence statistic score of about 95.8%, and the stability of the predicted model was verified with molecular dynamics for a 100-ns simulation. Molecular docking analysis determined the binding affinity of a novel nitrilase with nitriles. The binding scores produced by the novel nitrilase were approximately similar to those of the other prokaryotic nitrilase crystal structures, with a deviation of ±0.5.

Use of Graphene and Its Derivatives for the Detection of Dengue Virus.

Every year, the dengue virus and its principal mosquito vector, Aedes sp., have caused massive outbreaks, primarily in equatorial countries. The pre-existing techniques available for dengue detection are expensive and require trained personnel. Graphene and its derivatives have remarkable properties of electrical and thermal conductivity, and are flexible, light, and biocompatible, making them ideal platforms for biosensor development. The incorporation of these materials, along with appropriate nanomaterials, improves the quality of detection methods. Graphene can help overcome the difficulties associated with conventional techniques. In this review, we have given comprehensive details on current graphene-based diagnostics for dengue virus detection. We have also discussed state-of-the-art biosensing technologies and evaluated the advantages and disadvantages of the same.

In-Silico Analysis of rSNPs in miRNA:mRNA Duplex Involved in Insulin Signaling Genes Shows a Possible Pathogenesis of Insulin Resistance.

BACKGROUND: Insulin resistance is a condition in which the body produces insulin but is unable to use it effectively. Aberrations in insulin signaling are known to play a crucial role in the pathogenesis of this disease state. Eventually, patients will have glucose build-up in their blood instead of being absorbed by the cells, leading to type 2 diabetes. OBJECTIVE: In the current study, we focus on understanding the role of rSNP mediated miRNA:mRNA dysregulation and its impact on the above metabolic condition. METHODS: More than 30 genes involved in the insulin signaling pathway were found using the KEGG database. The 3'UTR end of genes was studied by using RegRNA and Ensembl, whereas TargetScan along with miRbase were used to identify their target miRNAs. Binding free energy was used as a parameter to analyze the effect of polymorphism on the miRNA:mRNA duplex formation. Further, the UNA fold was used to determine the heat capacity changes. RESULTS: The genes INSR, INS, GLUT4, FOXO1, IL6, TRIB3, and SREBF1, were selected for analysis. Multiple miRNAs, hsa-miR-16-5p, hsa-miR-15a-15p were identified in the SNP occurring region for INSR. INS, too, showed similar results. INSR, INS, and TRIB3 were found to have the maximum change in their binding free energy due to rSNP variation. A destabilisation in the heat capacity values was observed too, which contributed due to rSNP induction. CONCLUSION: A direct relationship between miRNA target polymorphism and the stability of the miRNA:mRNA duplex was observed. The current methodology used to study insulin resistance pathogenesis could elaborate on our existing knowledge of miRNA-mediated disease states.

IND-enzymes: a repository for hydrolytic enzymes derived from thermophilic and psychrophilic bacterial species with potential industrial usage.

Biocatalysts provide many advantages over the traditional chemically assisted processes prevalent in industries. Consequently, the search for novel enzymes has increased over the years with a renewed interest in thermophilic and psychrophilic bacterial species. Enzymes or extremozymes extracted from such species have exhibited an affinity to extreme temperatures which is a prerequisite for many industrial applications. However, utilisation of these enzymes faces a major bottleneck. The distribution of sequence data associated with thermophiles and psychrophiles is overwhelming, spanning various databases and scientific literature. Based on more than 100 publications and genomes from over 300 thermophilic and psychrophilic bacterial species, we have constructed the database IND-Enzymes (indenzymes.srmist.edu.in). This database consists of over 20,120 nucleotide and protein sequences belonging to the hydrolytic enzyme class lipase, protease, esterase and amylase. Users can access over 100 published enzymes, 200 PDB structural data. Enzymes derived from genomes can be directly downloaded and users can also access the entire annotation data derived from species individually. Along with an alignment tool and python based pipelines, IND-Enzymes serves as the largest sequence repository for hydrolytic enzymes from thermophilic and psychrophilic bacterial species. This database showcases resources that are essential for protein engineering of hot-cold stable enzymes.

Compost Samples from Different Temperature Zones as a Model to Study Co-occurrence of Thermophilic and Psychrophilic Bacterial Population: a Metagenomics Approach.

In this study, using a metagenomic approach, we explore the bacterial diversity of compost sites categorized based on their ambient temperatures. The two sites were Reckong Peo in the lower Himalayas and Tambaram in the southern region of the country, namely, CPR and CT. Following assembly of the raw reads from shotgun metagenomics, similarity hits were generated using NCBI BLAST + and SILVA database. A total of 1463 and 1483 species were annotated from CPR and CT. A species-level annotation was performed using a python-based literature search pipeline revealing their growth characteristics. Thermophiles Thermomonospora curvata and Thermus scotoductus were among the prominent species in CT. CPR too was seen abundant with Acidothermus cellulolyticus and Moorella thermoacetica, constituting 10% of the population. Nearly 3% of the identified species in the site CPR were psychrophilic. Although found higher in CPR, psychrophilic species were identified in CT too. Flavobacterium and Psychrobacter spp. were present in both sites without any significant changes in their relative distribution contrary to the thermophilic species abundance (z = - 4.3). Akin to the sequenced samples, database-derived metagenomes also showed similar distribution of thermophiles and psychrophiles. Identifying such peculiar prevalence of extremophiles can be central to understanding extended growth temperatures.

Identifying heat shock response systems from the genomic assembly of Ureibacillus thermophilus LM102 using protein-protein interaction networks.

Ureibacillus thermophilus strain LM102 is a facultative thermophile with growth in range 37 °C-60 °C. Upon identification using the 16S rRNA marker, it showed highest similarity of 99.8% with U. thermophilus strain HC148. A phylogenetic analysis revealed U. suwonensis strain 6 T19 to be the closest species to strain LM102. Our aim was to determine the unique thermotolerant properties of LM102 by identifying thermostablity of its proteins and the interactions existing in its heat shock response systems (HSRS). The strain was sequenced, assembled and the draft genome (3,017,325 bp) was analyzed. Post-annotation, we randomly selected a set of hundred proteins and computed the percentage distribution of 12 amino acids which have been substantially studied for their role in thermostability. The protein homologues were searched and the residues of LM102 were compared with Bacillus subtilis and Thermus thermophilus, a mesophile and hyperthermophile respectively. Within the 95% confidence limit, a Z-score of -0.61 was observed between LM102 and B. subtilis. However, a significantly lower value of -8.84 was observed for the pair LM102 and T. thermophilus. The amino acid distribution did not appear to influence the protein thermostability. Further, we investigated the role of Protein-Protein interactions by building networks for heat shock responses, namely DNA repair, transcriptional regulation, and activation of heat shock proteins. Interaction data retrieved from the STRING database for more than 50 species were used to build these networks. Highly clustered MCODE results notably revealed RNA 3'-5' exonuclease, CshA and HemW previously unreported, in association with other proteins. Additionally, these and other proteins estimated from the HSRS networks were found in both mesophiles and thermophiles, suggesting a crucial role of gene regulatory networks in the cellular viability of LM102 at high temperatures.

Identification of novel selective MMP-9 inhibitors as potential anti-metastatic lead using structure-based hierarchical virtual screening and molecular dynamics simulation.

MMP-9 is an attractive target for the development of new anticancer drugs. In the current study, pharmacophore modeling was employed using two highly active and selective gelatinase inhibitors obtained from two X-ray crystal structures (PDB IDs: and ) to identify novel selective MMP-9 inhibitors. The derived model was refined manually and also validated by the GH scoring method. The refined pharmacophore model, ADRR, was able to retrieve 86% of actives with a GH score of 0.774, indicating that the model was capable of retrieving the active MMP-9 inhibitors. ADRR was used to screen 2 838 166 unique structures. Hit filtration was carried out using a fitness score >1.5 and drug-likeness properties. Hierarchical clustering generates 33 clusters based on diversity. A total of 33 molecules were obtained and these molecules were taken for cross-docking studies with 5 subtype MMPs. Among 33 tested, 2 molecules, P10A-0000088030 (Lig-1) and P10A-0001383812 (Lig-2), were found to have the highest docking scores (-8.59 kcal mol(-1) and -8.27 kcal mol(-1)) towards MMP-9 compared with the other MMPs. Further MM-GBSA analysis was performed for two hits with 5 subtype MMPs to reveal the essential features that contribute to selectivity. The results showed that van der Waals contributions play a central role in determining the selectivity of MMP-9 inhibitors. Molecular dynamics studies were carried out for total time of 330 ns to assess the stability of ligands at the active site. MD analysis showed that binding of Lig-1 with MMP-9 is stable compared to that with Lig-2. Hence, we suggest the Lig-1 compound as a good lead in designing novel potent inhibitors of MMP-9.

Genetic engineering of Clostridium thermocellum DSM1313 for enhanced ethanol production.

BACKGROUND: The twin problem of shortage in fossil fuel and increase in environmental pollution can be partly addressed by blending of ethanol with transport fuel. Increasing the ethanol production for this purpose without affecting the food security of the countries would require the use of cellulosic plant materials as substrate. Clostridium thermocellum is an anaerobic thermophilic bacterium with cellulolytic property and the ability to produce ethanol. But its application as biocatalyst for ethanol production is limited because pyruvate ferredoxin oxidoreductase, which diverts pyruvate to ethanol production pathway, has low affinity to the substrate. Therefore, the present study was undertaken to genetically modify C. thermocellum for enhancing its ethanol production capacity by transferring pyruvate carboxylase (pdc) and alcohol dehydrogenase (adh) genes of the homoethanol pathway from Zymomonas mobilis. RESULTS: The pdc and adh genes from Z. mobilis were cloned in pNW33N, and transformed to Clostridium thermocellum DSM 1313 by electroporation to generate recombinant CTH-pdc, CTH-adh and CTH-pdc-adh strains that carried heterologous pdc, adh, and both genes, respectively. The plasmids were stably maintained in the recombinant strains. Though both pdc and adh were functional in C. thermocellum, the presence of adh severely limited the growth of the recombinant strains, irrespective of the presence or absence of the pdc gene. The recombinant CTH-pdc strain showed two-fold increase in pyruvate carboxylase activity and ethanol production when compared with the wild type strain. CONCLUSIONS: Pyruvate decarboxylase gene of the homoethanol pathway from Z mobilis was functional in recombinant C. thermocellum strain and enhanced its ability to produced ethanol. Strain improvement and bioprocess optimizations may further increase the ethanol production from this recombinant strain.

Identification of Potent Virtual Leads Specific to S1' Loop of ADAMTS4: Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Dynamic Studies.

ADAMTS4 (Aggrecanase-1) is an important enzyme, which belongs to ADAMTS family. Aggrecanase-1 is involved in aggrecan degradation of articular cartilage in osteoarthritis and rheumatoid arthritis. Overall variability of S1' domain of ADAMTS4 has been the main selectivity determinant to design the unique inhibitors. 34 inhibitors from Binding database and literature were used to develop the pharmacophore model. The five featured pharmacophore model AHHRR had the best survival score of 3.493 and post-hoc score of 2.545, indicating that the model is highly reliable. The 3D-QSAR acquired had excellent r(2) value of 0.99 and GH score of 0.839. The validated pharmacophore model was used for insilico screening of Asinex and ZINC database for finding the potential lead compounds. ZINC00987406 and ASN04459656 which pose high glide score i.e >7 Kcal/mol and H-bond and hydrophobic interactions in the S1'loop residues of ADAMTS4 were subjected to Molecular Dynamics Simulation studies. Molecular dynamic simulation result indicates that the RMSD and RMSF of backbone atoms for the above complexes were within the limit of 2.0 A˚. These compounds can be potential candidates for osteoarthritis by inhibiting ADAMTS4.

Homology modeling and in silico site directed mutagenesis of pyruvate ferredoxin oxidoreductase from Clostridium thermocellum.

Pyruvate ferredoxin oxidoreductase is the crucial enzyme that involves in bioethanol synthesis pathway of Clostridium thermocellum. It is an ethanologenic organism but has been investigated less on its enzyme structure. The amino acid sequence of Pyruvate ferredoxin oxidoreductase was derived from UNIPROT and the screened crystal structure was taken as the template for homology modeling using MODELLER 9V11. The model was loop refined and was validated using RMSD, ProSA and PROCHECK. The docking and per residue interaction studies were carried out to elucidate the interaction energies of amino acid residues with pyruvate. To enhance the binding of pyruvate with the enzyme, mutation studies were carried out by replacing Thr31 as it had a less interaction energy. Out of 10 mutants, T31N, T31Q and T31G were selected using potential energy and the residual energy calculations. Five nanoseconds explicit MD simulations were run for apo, wild type and mutants T31N, T31Q and T31G using Desmond. RMSD, RMSF, distance plots and H-bonds analysis proved T31G to be a favorable mutant for binding of pyruvate. Thus, modeling PFOR would help in profound understanding of its structural clefts and mutation studies would aid in improving the enzyme efficiency.

Molecular docking and dynamics simulation study on the influence of Zn2+ on the binding modes of aggrecanase with its inhibitors.

Zinc plays a vital role in structural organization, regulation of function and stabilization of the folded protein, which ultimately activates or inactivates the binding sites of the protein. Its transition makes a major change in the protein and its binding affinity. The ligand binding aggrecanases can be influenced by Zn2+ ions; therefore the study focuses on checking the binding mode in the presence and absence of zinc using Docking and Molecular dynamics simulation. The crystal structure with zinc was considered as wild type (ADAMTS-4-1Zn2+, ADAMTS-5-1Zn2+) and the crystal structure without zinc was considered as the mutant type (ADAMTS-4-0Zn2+, ADAMTS-5-0Zn2+). Mutations were made manually by deleting the zinc atom. ADAMTS-4-1Zn2+ had the best Glide score of -12.66 kcal·mol−1, whereas ADAMTS-4-0Zn2+ had -11.69 kcal·mol−1. ADAMTS-4-1Zn2+ had the best glide energy of -72.29 kcal·mol−1, whereas ADAMTS-4-0Zn2+ had-68.44 kcal·mol−1. ADAMTS-4-1Zn2+ had the best glide e-model of -116.34, whereas ADAMTS-4-0Zn2+ had -104.264. The RMSD value for ADAMTS-4-1Zn2+ and ADAMTS-4-0Zn2+ was 1.9. These results suggested that the absence of zinc decreases the binding affinity of ADAMTS-4 with its inhibitor. ADAMTS-5-1Zn2+ had the best Glide score of -8.32 kcal·mol−1, whereas ADAMTS-5-0Zn2+ had -6.62 kcal·mol−1. ADAMTS-5-1Zn2+ had the best glide energy of -70.28 kcal·mol−1, whereas ADAMTS-5-0Zn2+ had -66.02 kcal·mol−1. ADAMTS-5-1Zn2+ had the best glide e-model of-108.484, whereas ADAMTS-5-0Zn2+ had -93.81. The RMSD value for ADAMTS-5-1Zn2+ and ADAMTS-5-0Zn2+ was 0.48Å. These results confirmed that the absence of zinc decreased the binding affinity of ADAMTS-5 with its inhibitor whereas the presence extended the docking energy range and strengthened the binding affinity. Per-residue interaction study, MM-GBSA and Molecular Dynamics showed that all the four complexes underwent extensive structural changes whereas the complex with zinc was stable throughout the simulation period.

Combined structure- and ligand-based pharmacophore modeling and molecular dynamics simulation studies to identify selective inhibitors of MMP-8.

Matrix metalloproteinase-8 (MMP-8) is the key mediator in initiating type I collagen degradation and is associated with rheumatoid arthritis. In the present study, a pharmacophore hypothesis was developed based on selective non zinc binding inhibitors of MMP-8. The pharmacophore hypothesis was refined manually and validated by observing structures and the interactions of MMP-8 inhibitors. The refined pharmacophore model was able to discriminate the non-zinc binding inhibitors of MMP-8 with respect to other inhibitors. Hence this study proposes a combined structure- and ligand-based pharmacophore model that is suitable for retrieving the novel inhibitors of MMP-8. The pharmacophore hypothesis AADRH was used as query for retrieving potential compounds from the Zinc database and hits were selected based on the catalytic selective amino acid residues of Arg 222, and Tyr 227. We identified six compounds as potent inhibitors and their selectivity profile were checked against different subtypes of MMPs using the cross-docking method. Molecular dynamics results indicated that ZINC 00673680 forms a stable interaction with the key amino acid residues and avoids the zinc atom with a distance of 5.49 Å. Our computational study might be useful for further development of selective MMP-8 inhibitors.

E-pharmacophore and molecular dynamics study of flavonols and dihydroflavonols as inhibitors against dihydroorotate dehydrogenase.

DiHydroOrotate DeHydrogenase [huDHODH] is a therapeutic target for Rheumatoid arthritis [RA]. Leflunomide [A771726] is a widely used synthetic inhibitor against huDHODH. We to find more efficient lead like compounds. A four featured E-Pharmacophore A1D4H6R7 was built based on the inhibitor A771726. This pharmacophore was validated by checking its ability to identify known highly active inhibitors of huDHODH and assigning higher fitness scores to them. A reverse validation was also performed where random 4 featured pharmacophores were built and its efficiency in identifying actives was compared with our E-Pharmacophore. Our Epharmacophore was very efficient, since it passed both validations by picking the known active molecules with high fitness scores. This validated E- pharmacophore was searched against the KEGG phytochemicals subset database. This search resulted in 18 molecules which were subjected to docking with huDHODH. The molecules with docking score greater than that of A771726 were selected. The docking results were further validated using MM/GBSA which gave similar ranking with high binding free energy values. The four molecules 6-Methoxytaxifolin, Rhamnetin, Rhamnazin and Pinoquercetin were taken for explicit 3ns simulation and it was observed that all four molecules had acceptable RMSD values and stable interactions. Thus our study, suggests four phytomolecules that might inhibit huDHODH more efficiently than A771726. Interestingly, some of the obtained hits have already been proven in vitro anti-inflammatory activity which confirms that, the developed E-pharmacophore can be used to identify novel small molecules against inflammatory target, huDHODH.

Discovery of potent inhibitor for matrix metalloproteinase-9 by pharmacophore based modeling and dynamics simulation studies.

Matrix metalloproteinase-9 (MMP-9) is an attractive target for anticancer therapy. In the present study ligand based pharmacophore modeling was performed to elucidate the structural elements for a diverse class of MMP-9 inhibitors. The pharmacophore model was validated through Güner-Henry (GH) scoring method. The final pharmacophore model consisted of three hydrogen bond acceptors (HBA), and two ring aromatic regions (RA). This model was utilized to screen the natural compound database to seek novel compounds as MMP-9 inhibitors. The identified hits were validated using molecular docking and molecular dynamics simulation studies. Finally, one compound named Hinokiflavone from Juniperus communis had high binding free energy of -26.54kJ/mol compared with the known inhibitors of MMP-9. Cytotoxicity for hinokiflavone was evaluated by MTT assay. Inhibition of MMP-9 in the presence of hinokiflavone was detected by gelatin zymography and gelatinolytic inhibition assay. Results revealed that the natural compounds derived based on the developed pharmacophore model would be useful for further design and development of MMP-9 inhibitors.