Unravelling Phyto-Compound Therapeutics against SRC Protein via FGF Pathway Targeting-A Comprehensive Approach Integrating Omics Data Analysis, Network Pharmacology, Virtual Screening, and Molecular Dynamics.

INTRODUCTION: Colorectal cancer is a complex condition influenced by genetic mutations and environmental factors. Due to its intricate nature, the diagnosis and treatment of this condition require a comprehensive approach that considers individual circumstances. The study aimed to identify genes linked with colorectal cancer and their therapeutic agents from natural bioactive compounds. METHODS: The significantly prognostic differentially expressed genes (DEGs) were screened out from NCBI Gene Expression Omnibus (GEO) datasets. A protein-protein interaction network was constructed using STRING Database, and key genes were identified using Network Analyzer and CytoNCA plugins within Cytoscape. Further analysis involved functional annotations, and biological pathways analysis, SRC mechanism to uncover the role of SRC in CRC. Additionally, we performed virtual screening and molecular docking, Physiochemical property analysis along with MD simulation study to propose suitable natural compounds for promising therapeutic targets. RESULTS: The study conducted differential gene expression analysis, identifying 3621 statistically significant genes, with 1467 upregulated and 2154 downregulated. The top ten genes with the highest degree, betweenness centrality, and closeness centrality in the PPI network were selected as key genes. The SRC gene was found to have the highest degree and closeness centrality. Functional annotation and pathway analysis of key genes with a specific focus on the SRC mechanism revealed that the SRC's role in activating the RAS-RAF-MEK-ERK and Wnt/ß-catenin pathways in CRC cells, promoting proliferation and invasion. Molecular modelling of SRC led to the screening of phyto-compounds from tropical fruits, with Rutinexhibiting a higher docking score compared to FDA-approved anticancer drugs. MD simulations over 100 ns and the post-MD analysis i.e. RMSD, SASA, RMSF, FEL, RG, Hydrogen bond, PCA, and MMPBSA, comprehended the stable and robust interactions of a protein-ligand complex. These findings suggest Rutin's potential as a potent natural molecule for treating CRC. The study concludes that SRC plays a pivotal role in CRC, influencing cellular processes critical to cancer development and Rutin has been found to be a promising SRC inhibitor, suggesting a potential alternative therapeutic strategy for CRC. The consistent molecular interactions of Rutin necessitate further validation through wet lab experiments, offering hope for individuals affected by CRC.

Vaccine Designing Technology against Leishmaniasis: Current Challenges and Implication.

Leishmaniasis, a debilitating disease caused by protozoan parasites of the genus Leishmania and transmitted by the bite of a female sandfly, continues to present significant challenges despite ongoing research and collaboration in vaccine development. The intricate interaction between the parasite's life cycle stages and the host's immunological response, namely the promastigote and amastigote forms, adds complexity to vaccine design. The quest for a potent vaccine against Leishmaniasis demands a comprehensive understanding of the immune mechanisms that confer long-lasting protection, which necessitates extensive research efforts. In this pursuit, innovative approaches such as reverse vaccinology and computer-aided design offer promising avenues for unraveling the intricacies of host-pathogen interactions and identifying effective vaccine candidates. However, numerous obstacles, including limited treatment options, the need for sustained antigenic presence, and the prevalence of co-infections, particularly with HIV, impede progress. Nevertheless, through persistent research endeavours and collaborative initiatives, the goal of developing a highly efficacious vaccine against Leishmaniasis can be achieved, offering hope through the latest Omics data development with immunoinformatics approaches for effective vaccine design for the prevention of this disease.

KRAS Pathways: A Potential Gateway for Cancer Therapeutics and Diagnostics.

One of the major disturbing pathways within cancer is "The Kirsten rat sarcoma viral oncogene homolog (KRAS) pathway", and it has recently been demonstrated to be the most crucial in therapies and diagnostics. KRAS pathway includes numerous genes. This multi-component signaling system promotes cell growth, division, survival, and death by transferring signals from outside the cell to its interior. KRAS regulates the activation of a variety of signaling molecules. The KRAS oncogene is a key player in advancing a wide range of malignancies, and the mutation rank of this gene is a key feature of several tumors. For some malignancies, the mutation type of the gene may offer information about prognostic, clinical, and predictive. KRAS belongs to the RAS oncogene family, which consists of a compilation of minor GTP-binding proteins that assimilate environmental inputs and trigger internal signaling pathways that control survival, cell differentiation, and proliferation. This review aims to examine the recent and fascinating breakthroughs in the identification of new therapies that target KRAS, including the ever-expanding experimental approaches for reducing KRAS activity and signaling as well as direct targeting of KRAS. A literature survey was performed. All the relevant articles and patents related to the KRAS pathway, the mutation in the KRAS gene, cancer treatment, and diagnostics were found on PubMed and Google Patents. One of the most prevalent causes of cancer in humans is a mutation in the K-RAS protein. It is extremely difficult to decipher KRAS-mediated signaling. It allows transducing signals to go from the cell's outer surface to its nucleus, having an influence on a variety of crucial cellular functions including cell chemotaxis, division, dissemination, and cell death. Other involved signaling pathways are RAF, and the phosphatidylinositol 3 kinase also known as AKT. The EGFR pathway is incomplete without KRAS. The activation of PI3K significantly contributes to acquiring resistance to a mixture of MEK inhibitors and anti-EGFR in colorectal cancer cell lines which are mutated by KRAS. A series of recent patent studies towards cancer diagnostics and therapeutics reveals the paramount importance of mutated protein KRAS as an extensive driver in human tumors. For the prognosis, diagnosis, and treatment of colorectal cancer, KRAS plays a critical role. This review concludes the latest and vowing developments in the discovery of novel techniques for diagnosis and drugs that target KRAS, the advancements in experimental techniques for signaling and inhibiting KRAS function, and the direct targeting of KRAS for cancer therapeutics.

Computer Aided Reverse Vaccinology: A Game-changer Approach for Vaccine Development.

One of the most dynamic approaches in biotechnology is reverse vaccinology, which plays a huge role in today's developing vaccines. It has the capability of exploring and identifying the most potent vaccine candidate in a limited period of time. The first successful novel approach of reverse vaccinology was observed in Neisseria meningitidis serogroup B, which has revolutionised the whole field of computational biology. In this review, we have summarized the application of reverse vaccinology for different infectious diseases, discussed epitope prediction and various available bioinformatic tools, and explored the advantages, limitations and necessary elements of this approach. Some of the modifications in the reverse vaccinology approach, like pan-genome and comparative reverse vaccinology, are also outlined. Vaccines for illnesses like AIDS and hepatitis C have not yet been developed. Computer Aided Reverse vaccinology has the potential to be a game-changer in this area. The use of computational tools, pipelines and advanced soft-computing methods, such as artificial intelligence and deep learning, and exploitation of available omics data in integration have paved the way for speedy and effective vaccine designing. Is reverse vaccinology a viable option for developing vaccines against such infections, or is it a myth? Vaccine development gained momentum after the spread of various infections, resulting in numerous deaths; these vaccines are developed using the traditional technique, which includes inactivated microorganisms. As a result, reverse vaccinology may be a far superior technique for creating an effective vaccine.

Medicinal Plant-Rich Diet: A Potential Therapeutic Role in Colorectal Cancer.

BACKGROUND: Colorectal cancer is estimated to become the leading cause of cancer death worldwide. Since most of the available therapies affect vital organs such as heart and liver, herbal remedies as a substitute therapy have been reported in several evidence-based studies. OBJECTIVE: Medicinal plants exhibit a diverse range of bioactive elements known for their medicinal properties, such as anti-inflammatory, anticancer, antioxidant, and antimicrobial effects. Phytochemicals present in medicinal plants significantly trigger different signaling pathways, contributing to their therapeutic activities. This review covers a comprehensive summary of the therapeutic potential of an herbal diet in treating colorectal cancer and other ailments. Special attention will be given to exploring the interactions of medicinal plants with the microbiota and their associations with cancer pathways. CONCLUSION: A medicinal plant rich in bioactive compounds is a therapeutic option for colorectal cancer and potent cardioprotective and hepatoprotective agents. These bioactive compounds have demonstrated the ability to impede the growth of cancerous cells and trigger apoptosis. Our findings suggest that pomegranate, garlic, soybean, olive, green tea, papaya, and grapes are potential medicinal plants for combating cancer and related side effects. Bioactive compounds can modulate the gut microbiota's metabolism, and short-chain fatty acid production shows cardioprotective effects and reduces the risk of colorectal cancer. Hence, it can be stated that the interaction between a medicinal plant-rich diet and the gut microbiota plays a crucial role in preventing colorectal cancer and cardiac arrest.

Psychosomatic Disorder: The Current Implications and Challenges.

In recent years, there has been increasing global concern about the rising prevalence and rapid progression of psychosomatic disorders (PD). This surge can be attributed to irregular biological conditions and the increasingly stressful lifestyles that individuals lead, ultimately resulting in functional impairments of vital organs. PD arises from intricate interactions involving the central nervous, endocrine, and immune systems. Notably, the hypothalamic-pituitaryadrenal (HPA) axis plays an essential role, as its dysregulation is influenced by prolonged stress and psychological distress. Consequently, stress hormones, including cortisol, exert detrimental effects on immunological function, inflammation, and homeostatic equilibrium. It emerges as physical symptoms influenced by psychological factors, such as persistent pain, gastrointestinal disturbances, or respiratory complications, and is pertinent to highlight that excessive and chronic stress, anxiety, or emotional distress may engender the onset or exacerbation of cardiovascular disorders, namely hypertension and heart disease. Although several therapeutic strategies have been proposed so far, the precise etiology of PD remains elusive due to the intricate nature of disease progression and the underlying modalities of action. This comprehensive review seeks to elucidate the diverse classifications of psychosomatic disorders, explicate their intricate mechanisms, and shed light on their impact on the human body, which may act as catalysts for the development of various other diseases. Additionally, it explores the inherent medico-clinical challenges posed by PD and also explores the cutting-edge technologies, tools, and data analytics pipelines that are being applied in the contemporary era to effectively analyze psychosomatic data.

Multidrug-Resistant Acinetobacter baumannii: An Emerging Aspect of New Drug Discovery.

BACKGROUND: Acinetobacter baumannii is an opportunistic multidrugresistant, aerobic, glucose non-fermentative, and oxidative-negative coccobacilli bacteria. This life-threatening nosocomial infection is associated with immunocompromised patients. OBJECTIVE: This review aims to investigate the multiple drug resistance mechanisms and new emerging diagnostics & treatments for Acinetobacter baumannii. METHODS: All the articles that were most relevant to A. baumannii virulence and drug resistance mechanisms were founded by a literature search on PubMed. Google Patents were used to find discoveries related to diagnostics and treatment. RESULTS: Efflux pumps, ß-lactamases, aminoglycosides, outer membrane proteins, and alteration of the target sites were identified in the Acinetobacter baumannii pathogen as the most prevalent drug resistance mechanisms. Gene detection, peptide detection, and antigen-antibody-associated detection were the latest diagnostics. Novel antimicrobial peptides, sterilization techniques using blue light, and combination therapies are being developed to effectively treat A. baumannii infections. CONCLUSION: This review concludes that new drugs and formulations with high efficiency, low cytotoxicity, and no nephrotoxicity are in absolute need. In the near future, we can expect omics technology to play a significant role in discovering new drugs and potential targets.

LDRGDb - Legumes disease resistance genes database.

Legumes comprise one of the world's largest, most diverse, and economically important plant families, known for their nutritional and medicinal benefits. Legumes are susceptible to a wide range of diseases, similar to other agricultural crops. Diseases have a considerable impact on the production of legume crop species, resulting in large yield losses worldwide. Due to continuous interactions between plants and their pathogens in the environment and the evolution of new pathogens under high selection pressure; disease resistant genes emerge in plant cultivars in the field against those pathogens or disease. Thus, disease resistant genes play critical roles in plant resistance responses, and their discovery and subsequent use in breeding programmes aid in reducing yield loss. The genomic era, with its high-throughput and low-cost genomic tools, has revolutionised our understanding of the complex interactions between legumes and pathogens, resulting in the identification of several critical participants in both the resistant and susceptible relationships. However, a substantial amount of existing information about numerous legume species has been disseminated as text or is preserved across fractions in different databases, posing a challenge for researchers. As a result, the range, scope, and complexity of these resources pose challenges to those who manage and use them. Therefore, there is an urgent need to develop tools and a single conjugate database to manage genetic information for the world's plant genetic resources, allowing for the rapid incorporation of essential resistance genes into breeding strategies. Here, developed the first comprehensive database of disease resistance genes named as LDRGDb - LEGUMES DISEASE RESISTANCE GENES DATABASE comprises 10 legumes [Pigeon pea (Cajanus cajan), Chickpea (Cicer arietinum), Soybean (Glycine max), Lentil (Lens culinaris), Alfalfa (Medicago sativa), Barrelclover (Medicago truncatula), Common bean (Phaseolus vulgaris), Pea (Pisum sativum),Faba bean (Vicia faba), and Cowpea (Vigna unguiculata)]. The LDRGDb is a user-friendly database developed by integrating a variety of tools and software that combine knowledge about resistant genes, QTLs, and their loci, with proteomics, pathway interactions, and genomics (https://ldrgdb.in/).

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.

Targeting Notch Pathway in Cancer Diagnostics and Therapeutics: An Emerging Approach.

The Notch signaling pathway is an evolutionarily conserved pathway usually present in multicellular organisms, which plays a pivotal role in cell fate determination and proliferation. Due to this property, it is known to be highly oncogenic, especially in the dysregulated version of the Notch pathway, where apoptosis is inhibited and abnormal cell growth is supported. Notch receptors and ligand proteins play an essential role in cancers, such as myeloid leukemia, T-cell lymphoblastic leukemia, and organ-specific, i.e., breast, colon, pancreas, and skin cancers. Any type of cancer generates due to genetic defects, including epigenetic alterations and mutations. The researchers can use these alterations to find a promising diagnostic as well as therapeutic tool for cancer. The successful inhibition of the Notch pathway with the help of specific biomarkers or suppression of gene expression represents a new remedy in cancer research. This article focuses on the various remedies hidden within the Notch pathway's mechanism, primarily based on different patents published in recent years for assisting cancer diagnosis and succeeding treatment.

Predicting liver cancer on epigenomics data using machine learning.

Epigenomics is the branch of biology concerned with the phenotype modifications that do not induce any change in the cell DNA sequence. Epigenetic modifications apply changes to the properties of DNA, which ultimately prevents such DNA actions from being executed. These alterations arise in the cancer cells, which is the only cause of cancer. The liver is the metabolic cleansing center of the human body and the only organ, which can regenerate itself, but liver cancer can stop the cleansing of the body. Machine learning techniques are used in this research to predict the gene expression of the liver cells for the liver hepatocellular carcinoma (LIHC), which is the third biggest reason of death by cancer and affects five hundred thousand people per year. The data for LIHC include four different types, namely, methylation, histone, the human genome, and RNA sequences. The data were accessed through open-source technologies in R programming languages for The Cancer Genome Atlas (TCGA). The proposed method considers 1,000 features across the four types of data. Nine different feature selection methods were used and eight different classification methods were compared to select the best model over 5-fold cross-validation and different training-to-test ratios. The best model was obtained for 140 features for ReliefF feature selection and XGBoost classification method with an AUC of 1.0 and an accuracy of 99.67% to predict the liver cancer.

miRNA therapeutics in precision oncology: a natural premium to nurture.

The dynamic spectrum of microRNA (miRNA) has grown significantly over the years with its identification and exploration in cancer therapeutics and is currently identified as an important resource for innovative strategies due to its functional behavior for gene regulation and modulation of complex biological networks. The progression of cancer is the consequence of uncontrolled, nonsynchronous procedural faults in the biological system. Diversified and variable cellular response of cancerous cells has always raised challenges in effective cancer therapy. miRNAs, a class of non-coding RNAs (ncRNAs), are the natural genetic gift, responsible to preserve the homeostasis of cell to nurture. The unprecedented significance of endogenous miRNAs has exhibited promising therapeutic potential in cancer therapeutics. Currently, miRNA mimic miR-34, and an antimiR aimed against miR-122 has entered the clinical trials for cancer treatments. This review, highlights the recent breakthroughs, challenges, clinical trials, and advanced delivery vehicles in the administration of miRNA therapies for precision oncology.

Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models.

For decades, tuberculosis (TB), a potentially serious infectious lung disease, continues to be a leading cause of worldwide death. Proven to be conveniently efficient and cost-effective, chest X-ray (CXR) has become the preliminary medical imaging tool for detecting TB. Arguably, the quality of TB diagnosis will improve vastly with automated CXRs for TB detection and the localization of suspected areas, which may manifest TB. The current line of research aims to develop an efficient computer-aided detection system that will support doctors (and radiologists) to become well-informed when making TB diagnosis from patients' CXRs. Here, an integrated process to improve TB diagnostics via convolutional neural networks (CNNs) and localization in CXRs via deep-learning models is proposed. Three key steps in the TB diagnostics process include (a) modifying CNN model structures, (b) model fine-tuning via artificial bee colony algorithm, and (c) the implementation of linear average-based ensemble method. Comparisons of the overall performance are made across all three steps among the experimented deep CNN models on two publicly available CXR datasets, namely, the Shenzhen Hospital CXR dataset and the National Institutes of Health CXR dataset. Validated performance includes detecting CXR abnormalities and differentiating among seven TB-related manifestations (consolidation, effusion, fibrosis, infiltration, mass, nodule, and pleural thickening). Importantly, class activation mapping is employed to inform a visual interpretation of the diagnostic result by localizing the detected lung abnormality manifestation on CXR. Compared to the state-of-the-art, the resulting approach showcases an outstanding performance both in the lung abnormality detection and the specific TB-related manifestation diagnosis vis-à-vis the localization in CXRs.

Comparative evaluation of four triterpenoid glycoside saponins of bacoside A in alleviating sub-cellular oxidative stress of N2a neuroblastoma cells.

OBJECTIVES: To examine the neuroprotective property of triterpenoid glycoside saponins of Bacopa monnieri (L.) Wettst. bacoside A and its components against H2 O2 -induced oxidative stress on neuronal (N2a) cells. METHODS: The cytoprotective effects of individual bacoside A components were evaluated towards oxidative stressed neuronal cells. Bacoside A was screened for neuronal cell viability (MTT assay) and change in intracellular reactive oxygen species (ROS), anti-apoptotic properties and mitochondrial membrane potential (MMP) using fluorescence microscopy. KEY FINDINGS: Different bacoside A components showed decrease in N2a cell viability below 100 (%) after bacoside A concentration of 0.4 mg/ml. Further, cytoprotective effect of optimized dose of bacoside A was analysed for alleviating oxidative stressed, apoptosis and MMP in H2 O2 stressed neuronal cells. Results showed increase in MMP, and decrease in apoptotic induction, without much change in nuclear integrity in stressed neuronal cells. Results showed bacoside A3 and bacopaside II have comparatively higher cytoprotective ability whilst isomer of bacopasponin C, bacopasaponin C and mixture showed comparatively less response. CONCLUSIONS: Amongst four different bacoside A components, bacoside A3 and bacopaside II showed comparatively higher neuroprotective response analysed as higher cell viability and decreased intracellular ROS, suggesting better regulation of cyto-(neuronal) protection of N2a cells.

Zinc oxide nanoparticles (ZnO NP) mediated regulation of bacosides biosynthesis and transcriptional correlation of HMG-CoA reductase gene in suspension culture of Bacopa monnieri.

Bacopa monnieri (L.) Wettst. is a well documented nootropic plant, extensive known for alleviating symptoms of neurological disorder, along with other symptomatic relief. This property is attributed to the active phytocompounds, saponins (bacoside A) present in the plant. However, lack of stringent validation guidelines in most of the countries bring to the market, formulations differing in phytocompounds yield, thereby suggesting possible variation in therapeutic efficacy. The in-vitro suspension cultures of the Bacopa monnieri, provide an ease of scale-up, but regulating saponin yield is a stringent task. The aim of the study is to explore the effects of different concentrations (0, 0.25, 0.50, 0.75 and 1.0 ppm) of zinc oxide nanoparticles (ZnO NP) (24 nm in size), in regulating growth rate, bacoside yield and transcriptional profile of HMG CoA reductasegene in the suspension cells of Bacopa monnieri. Results showed a linear correlation between Bacoside A yield and ZnO NP concentrations with around 2 fold increase in total bacoside A concentration at 1 ppm. Also, ZnO NP supplemented suspension cells showed variation in the specific growth rate. Neuroprotective properties, analyzed using methanolic extracts of suspension cells again obtrude the extract of ZnO NP supplemented (0.75 ppm and 1 ppm) culture for better response in alleviating oxidative stress mediated damage to neuronal cells. ZnO NP supplemented system showed lower expression of HMG CoA reductasegene (the rate limiting step in bacoside A biosynthesis) but higher concentration of bacoside A, suggesting possible role of ZnO NP in isoprenoid pathway than MVA pathways.

Identification of Phosphoribosyl-AMP cyclohydrolase, as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis.

Brucella melitensis is a pathogenic Gram-negative bacterium which is known for causing zoonotic diseases (Brucellosis). The organism is highly contagious and has been reported to be used as bioterrorism agent against humans. Several antibiotics and vaccines have been developed but these antibiotics have exhibited the sign of antibiotic resistance or ineffective at lower concentrations, which imposes an urgent need to identify the novel drugs/drug targets against this organism. In this work, metabolic pathways analysis has been performed with different filters such as non-homology with humans, essentially of genes and choke point analysis, leading to identification of novel drug targets. A total of 18 potential drug target proteins were filtered out and used to develop the high confidence protein-protein interaction network The Phosphoribosyl-AMP cyclohydrolase (HisI) protein has been identified as potential drug target on the basis of topological parameters. Further, a homology model of (HisI) protein has been developed using Modeller with multiple template (1W6Q (48%), 1ZPS (55%), and 2ZKN (48%)) approach and validated using PROCHECK and Verify3D. The virtual high throughput screening (vHTS) using DockBlaster tool has been performed against 16,11,889 clean fragments from ZINC database. Top 500 molecules from DockBlaster were docked using Vina. The docking analysis resulted in ZINC04880153 showing the lowest binding energy (-9.1 kcal/mol) with the drug target. The molecular dynamics study of the complex HisI-ZINC04880153 was conducted to analyze the stability and fluctuation of ligand within the binding pocket of HisI. The identified ligand could be analyzed in the wet-lab based experiments for future drug discovery.

Protein-protein interaction and molecular dynamics analysis for identification of novel inhibitors in Burkholderia cepacia GG4.

The lack of complete treatments and appearance of multiple drug-resistance strains of Burkholderia cepacia complex (Bcc) are causing an increased risk of lung infections in cystic fibrosis patients. Bcc infection is a big risk to human health and demands an urgent need to identify new therapeutics against these bacteria. Network biology has emerged as one of the prospective hope in identifying novel drug targets and hits. We have applied protein-protein interaction methodology to identify new drug-target candidates (orthologs) in Burkhloderia cepacia GG4, which is an important strain for studying the quorum-sensing phenomena. An evolutionary based ortholog mapping approach has been applied for generating the large scale protein-protein interactions in B. Cepacia. As a case study, one of the identified drug targets; GEM_3202, a NH (3)-dependent NAD synthetase protein has been studied and the potential ligand molecules were screened using the ZINC database. The three dimensional structure (NH (3)-dependent NAD synthetase protein) has been predicted from MODELLERv9.11 tool using multiple PDB templates such as 3DPI, 2PZ8 and 1NSY with sequence identity of 76%, 50% and 50% respectively. The structure has been validated with Ramachandaran plot having 100% residues of NadE in allowed region and overall quality factor of 81.75 using ERRAT tool. High throughput screening and Vina resulted in two potential hits against NadE such as ZINC83103551 and ZINC38008121. These molecules showed lowest binding energy of -5.7kcalmol-1 and high stability in the binding pockets during molecular dynamics simulation analysis. The similar approach for target identification could be applied for clinical strains of other pathogenic microbes.

Homology modeling and protein engineering of alkane monooxygenase in Burkholderia thailandensis MSMB121: in silico insights.

The degradation of hydrocarbons plays an important role in the eco-balancing of petroleum products, pesticides and other toxic products in the environment. The degradation of hydrocarbons by microbes such as Geobacillus thermodenitrificans, Burkhulderia, Gordonia sp. and Acinetobacter sp. has been studied intensively in the literature. The present study focused on the in silico protein engineering of alkane monooxygenase (ladA)-a protein involved in the alkane degradation pathway. We demonstrated the improvement in substrate binding energy with engineered ladA in Burkholderia thailandensis MSMB121. We identified an ortholog of ladA monooxygenase found in B. thailandensis MSMB121, and showed it to be an enzyme involved in an alkane degradation pathway studied extensively in Geobacillus thermodenitrificans. Homology modeling of the three-dimensional structure of ladA was performed with a crystal structure (protein databank ID: 3B9N) as a template in MODELLER 9v11, and further validated using PROCHECK, VERIFY-3D and WHATIF tools. Specific amino acids were substituted in the region corresponding to amino acids 305-370 of ladA protein, resulting in an enhancement of binding energy in different alkane chain molecules as compared to wild protein structures in the docking experiments. The substrate binding energy with the protein was calculated using Vina (Implemented in VEGAZZ). Molecular dynamics simulations were performed to study the dynamics of different alkane chain molecules inside the binding pockets of wild and mutated ladA. Here, we hypothesize an improvement in binding energies and accessibility of substrates towards engineered ladA enzyme, which could be further facilitated for wet laboratory-based experiments for validation of the alkane degradation pathway in this organism.

Study on variability assessment and evolutionary relationships of glutamate racemase in Pseudomonas species.

Pseudomonas species is known to cause multiple nosocomial infections in patients and results in high morbidity and mortality rates (10%). The greatest obstacle in treating patients infected with the Pseudomonas species is the widespread emergence of antibiotic resistance. Hence, there is an urgent need to develop new compounds which can be effective against Pseudomonas species and possibly remain tolerant to drug resistance. The enzyme glutamate racemase plays an important role in cell wall synthesis of bacteria and as a rate limiting step, thus it is an excellent target for the designing of new class of antibacterial agents. The objective of this study is to investigate the variations in sequences of glutamate racemase, a potential drug target across the all 31 species of Pseudomonas. Sequence variability and conservation for functional motif identification is helpful for identifying evolutionarily important residues with functional significance; subsequently these results of variable sites were supported by entropy profile obtained from protein variability server using Shannon entropy. Phylogenetic profile among the different Pseudomonas sp. having fully/highly conserved residues was observed, suggesting possible functional similarities between them. The variation analysis in conserved and non-conserved region of the sequence can be used to predict the binding site for target specific drug discovery.

BIRS - Bioterrorism Information Retrieval System.

UNLABELLED: Bioterrorism is the intended use of pathogenic strains of microbes to widen terror in a population. There is a definite need to promote research for development of vaccines, therapeutics and diagnostic methods as a part of preparedness to any bioterror attack in the future. BIRS is an open-access database of collective information on the organisms related to bioterrorism. The architecture of database utilizes the current open-source technology viz PHP ver 5.3.19, MySQL and IIS server under windows platform for database designing. Database stores information on literature, generic- information and unique pathways of about 10 microorganisms involved in bioterrorism. This may serve as a collective repository to accelerate the drug discovery and vaccines designing process against such bioterrorist agents (microbes). The available data has been validated from various online resources and literature mining in order to provide the user with a comprehensive information system. AVAILABILITY: The database is freely available at http://www.bioterrorism.biowaves.org.