Vidya Niranjan

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained. � 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Reactive oxygen species (ROS) increases under stress and damages cellular processes leading to decrease in productivity. Many genes have been known to be involved in scavenging ROS. We report the identification of master regulators of oxidative stress responsive genes from contrasting rice genotypes. Using microarray analysis, we identified 52 differentially expressed transcription factors (TFs) from the contrasting rice genotypes under oxidative stress. Upregulation of these TFs induces the expression of many genes in resistant or sensitive genotypes. The promoters of these TFs are enriched with reactive oxygen species binding elements (ROSE). The promoter analysis of genes that respond to oxidative stress also revealed that these TF binding sites were present and that these genes expressed differently in contrasting rice genotypes. The transcript levels of TFs correlate with expression level of stress responsive genes coding for various pathways such as polyol, ABA, JA biosynthesis and signaling. Functional validation of HSF-C1a using virus-induced gene silencing (VIGS), showed reduced expression of its target genes. Our study demonstrates that identified TFs could act as major transcriptional regulators of oxidative stress tolerance. These TFs can be used as markers and are potential candidates to improve stress tolerance in plants. � 2023, Korean Society for Plant Biotechnology.

Abstract: The challenges of the twenty-first century for the pharmaceutical industry are to deliver new and safe medicines within a short period of time. Novel drug discovery is a complex and expensive process with decades of the venture. With the present technologies and inventions, the task is much faster in recent years. Computer simulations give the dynamic picture of the reactions along with the potential drug molecule. Diabetes is a carbohydrate disorder caused due to the effect of environment, which results in increased hepatic glucose production, decreased insulin secretion. It hinders the function of eyes, kidneys, heart, nerves and blood vessels. The most commonly available drug for diabetes involves Metformin, Sulfonylureas, Meglitinide and Thiazolidinedione which cause severe damage to internal organs and many diseases like bladder cancer, hypoglycemia, risk of liver disease and many more. Docking techniques ease the identification of potential drug molecules to specific target. The present work aims at molecular docking studies on derivatives of pioglitazone while taking pioglitazone as reference. Peroxisome Proliferator Activated Receptor gamma (PPAR-?) was taken as receptor and the derivatives were docked by using Autodock software. Docking results showed that pioglitazone derivatives were active against PPAR-? with enhanced binding affinity when compared to standard marketed drug i.e. Pioglitazone. Molecular docking studies on the pioglitazone derivatives indicate that they may be used as a promising antidiabetic drug. � 2022, Pleiades Publishing, Ltd.

Multidrug-resistant Acinetobacter baumannii (MDRAb), a priority-I pathogen declared by the World Health Organization, became a potential healthcare concern worldwide with a high mortality rate. Thus, the identification of putative molecular targets and potential lead molecules is an important concern in healthcare. The present study aimed to screen a prospective molecular target and effectual binders for the drug discovery of MDRAb by computational virtual screening approach. Based on the functional role, ?-carboxymuconolactone decarboxylase (CMD) was prioritized as the target and its three-dimensional (3D) structure was computationally modeled. Based on the availability of the 3D structure, twenty-five herbal molecules were selected by database search, and their drug-likeliness, pharmacokinetic, and toxicity features were predicted. The effectual binding of the selected molecules towards CMD was predicted by molecular docking. The stability of the best-docked complexes was predicted by molecular dynamics (MD) simulation for 100 ns and binding energy calculations were carried out by molecular mechanics generalized Born and surface area solvation (MM/GBSA) method. Out of twenty-five molecules screened, hirsutine (an indole alkaloid of Uncaria rhynchophylla) and thymoquinone (a phytochemical of Nigella sativa) were qualified for drug likeliness, pharmacokinetic, and toxicity features and demonstrated significant effectual binding to CMD when compared with the binding of co-crystallized inhibitor and CMD (control). The docked complexes of hirsutine and thymoquinone, and CMD were stabilized by the binding energies of ?8. 30 and ?8. 46 kcal/mol respectively. These molecules were qualified in terms of ideal drug likeliness, ADME, and toxicity properties. MD simulation studies showed that the ligand-protein complexes were stable throughout the simulation. The binding free energies of the complexes by MMGBSA were estimated to be ?42.08157745 kcal/mol and ?36.58618242 kcal/mol for hirsutine and thymoquinone respectively when compared with the calculated binding free energy of the control (?28.75032666 kcal/mol). This study concluded that hirsutine and thymoquinone can act as potential lead molecules against CMD and the present hypothesis can be scaled up to develop potential inhibitors against MDRAb. � 2023 Elsevier Ltd

The current study aimed to identify putative drug targets of multidrug resistant Acinetobacter baumannii (MDRAb) and study the therapeutic potential of natural epiestriol-16 by computer aided virtual screening and in vitro studies. The clinical isolates (n = 5) showed extreme dug resistance to carbapenems and colistins (p ? .05). Computational screening suggested that out of 236 natural molecules selected, 06 leads were qualified for drug likeliness, pharmacokinetic features and one potential molecule namely natural epiestriol-16 (16b-Hydroxy-17a-estradiol) exhibited significant binding potential towards four prioritised drug targets in comparison with the binding of faropenem to their usual target. Natural epiestriol demonstrated profound binding to the outer membrane protein (Omp38), protein RecA (RecA), orotate phosphoribosyltransferase (PyrE) and orotidine 5?-phosphate decarboxylase (PyrF) with binding energy of ?6.0, ?7.3, ?7.3 and ?8.0 kcal/mol respectively. MD simulations suggested that 16-epiestriol-receptor complexes demonstrated stability throughout the simulation. The growth curve and time kill assays revealed that MDRAb showed resistance to faropenem and polymyxin-B and the pure epiestriol-16 showed significant inhibitory properties at a concentration of 200 ?g/mL (p ? .5). Thus, natural epiestriol-16 can be used as potential inhibitor against the prioritised targets of MDRAb and this study provide insight for drug development against carbapenem and colistin resistant A. baumannii. � 2020 Elsevier B.V.

The study aimed to screen prospective molecular targets of BCC and potential natural lead candidates as effective binders by computational modeling, molecular docking, and dynamic (MD) simulation studies. Based on the virulent functions, tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein (mnmC) and pyrimidine/purine nucleoside phosphorylase (ppnP) were selected as the prospective molecular targets. In the absence of experimental data, the three-dimensional (3D) structures of these targets were computationally predicted. After a thorough literature survey and database search, the drug-likeness, and pharmacokinetic properties of 70 natural molecules were computationally predicted and the effectual binding of the best lead molecules against both the targets was predicted by molecular docking. The stabilities of the best-docked complexes were validated by MD simulation and the binding energy calculations were carried out by MM-GBSA approaches. The present study revealed that the hypothetical models of mnmC and ppnP showed stereochemical accuracy. The study also showed that among 70 natural compounds subjected to computational screening, Honokiol (3?,5-Di(prop-2-en-1-yl) [1,1?-biphenyl]-2,4?-diol) present in Magnolia showed ideal drug-likeness, pharmacokinetic features and showed effectual binding with mnmC and ppnP (binding energies ?7.3 kcal/mol and ?6.6 kcal/mol, respectively). The MD simulation and GBSA calculation studies showed that the ligand-protein complexes stabilized throughout tMD simulation. The present study suggests that Honokiol can be used as a potential lead molecule against mnmC and ppnP targets of BCC and this study provides insight into further experimental validation for alternative lead development against drug resistant BCC. � 2023 Wiley Periodicals LLC.

Improvements and advances in health care over the past few decades have increased life expectancy and quality of life. However, this has resulted in an increase in non-communicable diseases like dementia, Alzheimer�s Disease (AD), etc. AD most commonly affects the older population, but recent studies reveal that it can also affect people of any age group. As of now, there are no accessible treatments or therapies that reverse the progression of the disease. The few existing medications to treat AD include Donepezil (Aricept), Galantamine (Razadyne) and Rivastigmine (Exelon) provide only temporary relief and come with several side effects like diarrhoea, vomiting, nausea, fatigue, insomnia, loss of appetite, and weight loss. An effective and augmenting therapy with Cholinesterase inhibitors from natural products is gaining popularity among researchers. Plant sterols have been known to play roles in inhibiting proteins implicated in the development of AD. The present study highlights the significance and scope of Stigmasterol, a phytochemical in Saraca asoca in the alleviation of AD. Our study involving the use of QSAR, ADME, molecular interaction, and molecular docking has shown that Stigmasterol has the potential to be developed as a therapeutic drug to curb the progression of AD after thorough validation procedures. � 2021 Har Krishan Bhalla & Sons.

Aedes aegypti is the target for repellents to curb incidences of vector-borne diseases. Stopping breeding of this mosquito species at its larval stages helps in controlling spread of insect-borne diseases. Therefore, the present study focused on deciphering the mechanism of interaction of selected natural actives against larval proteins of A. aegypti to identify potential natural alternative larvicides. 65 larval proteins were identified from literature, whose structures were modelled and validated using RaptorX and ProCheck. 11 natural actives were selected for predicting their ligand properties and toxicities via Toxicity Estimation Software Tool and ProTox-II. Molecular docking studies were carried out using POAP followed by 100 ns molecular dynamic simulation studies for top three best docked complexes to better understand the robustness of docking, complex stabilities and molecular mechanisms of interactions. Toxicity predictions revealed that 6 molecules belonged to toxicity class 4, and five to toxicity class 5, implying that they were all safe to use. Complexes goniothalamin-translation elongation factor (?10 kcal/mol), andrographolide-acetyl-CoA C-myristoyltransferase (?9.2 kcal/mol) and capillin-translation elongation factor (?8.4 kcal/mol) showed best binding energies. When simulated, capillin-translation elongation factor showed most stability, while the remaining two also evidenced robust docking. Evolutionary studies for top two larval proteins disclosed 100 other insect species in which these proteins can be targeted using various larvicides. Protein-protein interaction network analysis revealed several protein pathways that might be affected due to aforesaid naturals. Therefore, this study provides computational insights into the molecular interaction of naturals against larval proteins, acting as potential natural larvicides. Communicated by Ramaswamy H. Sarma. � 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Aedes aegypti (A. aegypti) is the principal vector for several diseases and despite having synthetic repellents to curb its feeding and transmission, they pose a threat to humans upon chronic use. Therefore, this study explores natural alternative thymol, via both structural and molecular binding properties against whole proteome targets of A. using a multi-target approach. Properties of thymol were studied using ProTox-II to determine the toxicity class. A preliminary screening of the proteome of A. aegypti was performed using existing microarray data analysis, conserved domain studies and protein modelling to narrow down the target categories. RaptorX standalone high-computing server was utilised for 309 protein structure modelling. Molecular docking was performed for 20 shortlisted protein categories against thymol, and top three docked complexes were simulated at 100 ns. Results showed that thymol belonged to class 4 low-toxicity, and molecular docking and 100 ns simulations in dynamic environment revealed stable complexes of thymol with glutathione-S-transferase (GST), octopamine receptor and glutamate-gated chloride channels (GGCC). Free energy binding via molecular mechanics revealed thymol with GST and GGCC to be stable. Our multi-target study presents insights into the molecular binding events that take place when thymol binds to newly identified A. aegypti targets. � 2022 Informa UK Limited, trading as Taylor & Francis Group.

Rheumatic Arthritis (RA) is a chronic, autoimmune, inflammatory disease involving primarily the peripheral synovial joints. The diagnosis of RA in its pre-clinical phase is of at most importance as it can prevent progressive and irreversible joint damage if treated early. As RA is a genetic disorder, diagnosis through genomic sequence analysis has proven to be an appropriate solution to achieve the above goal [2]. Digital Signal Processing (DSP) applications in bio- informatics has received great attention in recent years, where computationally efficient methods for genome sequence analysis have been developed by utilizing existing signal processing algorithms. In the proposed work, a software module that uses signal processing techniques to predict probability of the future occurrence of RA has been developed. This is done by reviewing medical literature to identify the genes responsible for causing the disease and subsequently obtaining the nucleotide sequences of these genes through GenBank, a standard open-access gene database. The nucleotides are then mapped onto a unit circle in the complex plane so that complimentary base pairs are complex conjugates of each other and the magnitudes of the nucleotides are normalized at unity. Risk gene patterns are then searched in the chromosome sequence under test. Cross-correlation, which is a signal processing algorithm, was used for recognition of presence of risk genes in the chromosome sequence. The usage of cross- correlation not only allowed the identification of mutated sequences but also reduced the time complexity to O[Nlog2(N)].A relative genetic risk score and overall genetic risk score of probability of developing RA was then calculated using statistical methods. In order to test the system, a genome sequence simulator whose underlying architecture is that of a state machine, was created. Using this simulator multiple datasets containing several combinations of risk genes were generated. The system tested using the datasets thus obtained was found to be 95% accurate when the risk magnitudes obtained by the system was compared against the ground truth values given in RAVariome database for the same set of genes chosen. Hence by ensuring early diagnosis, the system will assist doctors to formulate effective treatment plans and thus prevent joint deterioration and permanent functional disability. � 2017 IEEE.