A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Potential Therapeutic Agents on Alzheimer's Disease through Molecular Docking and Molecular Dynamics Simulation Study of Plant-Based Compounds.

Globally Alzheimer's disease (AD) is a highly complex, heterogeneous, and multifactorial neurological disease. AD is categorized clinically through a steady loss in memory and progressive decline of cognitive function. So far, there is no effective cure is available for the treatment of AD. Here, we identified Plant-based compounds (PBCs) from seven therapeutic plants through pharmacophore and pharmacokinetics approaches. Subsequently, we retrieved 65 AD associated proteins by Text Mining approach .We observed the interactions between 39 PBCs with 65 AD-associated targets by using molecular docking. Further, we carried out Molecular dynamics simulation analysis to predict the steady binding of top drug-target complexes. The entire MD simulation results analysis was evidence that seven drug-target complexes consistently interacted during the in silico experiment. The top complexes were the target CHLE interacted with 2 PBCs (Pseudojujubogenin and Anahygrine), target VDAC1 interacted with Withanolide R, target THOP1 interacted with Withaolide R, target AOFB interacted with 2 PBCs (Nardostachysin and Viscosalactone B), and target ACHE interacted with the drug (12-Deoxywithastramonolide). These PBCs have stably and flexibly interacted at the protein's active site region. Our results suggest that these PBCs and targets are potential therapeutic candidates for molecular development in AD.

Repurposing of FDA-approved drugs to target MurB and MurE enzymes in Mycobacterium tuberculosis.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) is one amongst the top 10 causes of death worldwide. The growing rise in antibiotic resistance compounded with slow and expensive drug discovery has further aggravated the situation. 'Drug repurposing' is a promising approach where known drugs are examined for a new indication. In the present study, we have attempted to identify drugs that could target MurB and MurE enzymes involved in the muramic acid synthesis pathway (Mur Pathway) in Mtb. FDA-approved drugs from two repositories i.e. Drug Bank (1932 drugs) and e-LEA3D (1852 drugs) were screened against these proteins. Several criteria were applied to study the protein-drug interactions and the consensus drugs were further studied by molecular dynamics (MD) simulation. Our study found Sulfadoxine (-7.3 kcal/mol) and Pyrimethamine (-7.8 kcal/mol) to show stable interaction with MurB while Lifitegrast (-10.5 kcal/mol) and Sildenafil (-9.1 kcal/mol) showed most reliable interaction with MurE. Furthermore, binding free energy (ΔGbind), RMSD and RMSF data and the number of hydrogen bonds corroborated the stability of interactions and hence these drugs for repurposing should be explored further.Communicated by Ramaswamy H. Sarma.

Structural bioinformatics-based identification of putative plant based lead compounds for Alzheimer Disease Therapy.

Plant based lead compounds have been historically incredible as a source of therapeutic agents for various complex disorders including Alzheimer's disease (AD). AD is one of the leading neurodegenerative disorder in which the underlying risk factors remain largely unclear and presently, there is no disease modifying treatment available. Despite its potential, to date only few compounds have entered for clinical trials. Herein, we described the identification of plant based lead compounds for treatment of AD through an integrative approach of pharmacokinetics and structure bioinformatics approach. In particular we performed screening of lead compounds from 3 traditional medicinal plants namely Withania somnifera, Bacopa monnieri and Morus alba, which are known to have potential for treatment of neurodegenerative disease. We retrieved a total of 210 plant based compounds of which 21 compounds were screened based on their pharmacokinetic properties. Further, Docking study against 7 known AD associated targets were carried out to identify the binding sites and direct interacting residues. In addition we investigate the stable and reliable binding mechanism of top such plant compounds against 3 targets through molecular docking followed by Molecular Dynamic(MD) simulation. The results obtained in the study revealed that 3 drug compounds namely Morusin (MRSN), Withanone (WTHN) and 27-Hydroxywithanolide B (HWTHN) were identified as putative lead compounds against mono amine oxidase (MAOB), Beta-secretase 1(BACE1) and phosphodiesterase 4D.

Synthesis of D-Ring Annulated Pyridosteroids from ß-Formyl Enamides and Their Biological Evaluations.

Herein, we report the synthesis of a novel class of substituted androst[17,16- b]pyridines (pyridosteroids) from the reaction of ß-formyl enamides with alkynes in high yields. The optimized reaction protocol was extended to acyclic and cyclic ß-formyl enamides to afford nonsteroidal pyridines. Cell survival assay of all compounds were carried against prostate cancer PC-3 cells wherein 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine showed the highest cytotoxic activity. Phase contrast microscopy and flow cytometry studies exhibited marked morphological features characteristic of apoptosis in 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine and abiraterone treated PC-3 cells. The treatment of 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine induces G2/M phase cell cycle arrest in prostate cancer PC-3 cells. Enhancement of apoptotic inductions of PC-3 cells by 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine and abiraterone through the activation of caspases-6, -7, and -8 pathways were supported by qRT-PCR. In silico study of the compound 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine showed stable and promising interaction with the key caspase proteins. Our studies revealed that the pyridosteroid 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine, bearing pyridine-2,3-dicarbethoxy pharmacophore, facilitated initiation of caspase-8 and activates downstream effectors caspase-6 and caspase-7 and thereby triggering apoptosis of PC-3 cancer cells.