Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Exploring purine analogues as inhibitors against Katanin, a microtubule severing enzyme using molecular modeling approach

Katanin, a key protein in cellular architecture, plays a crucial role in severing microtubules, which are vital components of the cytoskeleton. Given its central involvement in cell division and proliferation, katanin represents a promising target for therapeutic intervention, particularly in cancer treatment. Inhibiting katanin's function could potentially hinder the uncontrolled growth of cancerous cells, making it an attractive target for novel anti-cancer therapies. Previous studies have shown that purine-based compounds exhibit a strong affinity for microtubule-severing enzymes. In this study, we aim to identify potential purine-type inhibitors of katanin using molecular modeling techniques. A total of 276,280 purine-type compounds from the PubChem database were subjected to structure-based high-throughput virtual screening, followed by ADME prediction, PASS analysis, and molecular docking studies. These efforts led to the identification of two potent compounds: PubChem CID 122589735 and 123629569, which demonstrated strong binding interactions with katanin. Molecular dynamics simulations further revealed that these compounds effectively altered katanin's conformation when compared to ATP. Additionally, binding energy calculations indicated that PubChem CID 122589735 exhibited the strongest binding affinity for katanin, with the binding free energy ranking as follows: 122589735 > 123629569 > ATP. Our findings suggest that the screened compounds, particularly PubChem CID 122589735, hold promise as potential katanin inhibitor. These compounds could play a significant role in the development of new anti-cancer therapies targeting a variety of carcinoma. Future research, including in vitro and in vivo studies, is essential to assess the efficacy and safety of these inhibitors, paving the way for innovative cancer treatments.

Discovery of α-amylase and α-glucosidase dual inhibitors from NPASS database for management of Type 2 Diabetes Mellitus: A chemoinformatic approach

Postprandial hyperglycemia, typical manifestation of Type 2 Diabetes Mellitus (T2DM), is associated with notable global morbidity and mortality. Preventing the advancement of this condition by delaying the rate of glucose absorption through inhibition of α-amylase and α-glucosidase enzymatic activities is of utmost importance. Finding a safe antidiabetic drug is essential since those that are currently on the market have drawbacks like unpleasant side effects. The current study utilized computer-aided drug design (CADD), as a quick and affordable method to find a substitute drug template that can be used to control postprandial hyperglycemia by modulating the activity of α-amylase and α-glucosidase enzymes. The Natural Products Activity and Species database (NPASS) (30,926 compounds) was screened in silico, with a focus on evaluating drug-likeness, toxicity profiles and ability to bind on a target protein. Two molecules NPC204580 (Chrotacumine C) and NPC137813 (1-O-(2-Methoxy-4-Acetylphenyl)-6-O-(E-Cinnamoyl)-Beta-D-Glucopyranoside) were identified as potential dual inhibitors for α-amylase and α-glucosidase with free binding energies of -14.46 kcal/mol and -12.58 kcal/mol for α-amylase, and -8.42 kcal/mol and -8.76 kcal/mol for α-glucosidase, respectively. The molecules showed ionic, H-bonding and hydrophobic interactions with critical amino acid residues of both enzymes. Moreover, 100 ns molecular dynamic simulations showed that both molecules are stable on the receptors' active sites based on root mean square deviation (RMSD), root mean square fluctuation (RMSF), and the Generalized Born surface area (GBSA) energy calculated. The two compounds are thus promising therapeutic agents for T2DM that merit further investigation due to their excellent binding energies, encouraging pharmacokinetics, toxicity profiles, and stability as demonstrated in simulated studies.

Screening of promising molecules against potential drug targets in Yersinia pestis by integrative pan and subtractive genomics, docking and simulation approach

This study focuses on Yersinia pestis, the bacterium responsible for plague, which posed a severe threat to public health in history. Despite the availability of antibiotics treatment, the emergence of antibiotic resistance in this pathogen has increased challenges of controlling the infections and plague outbreaks. The development of new drug targets and therapies is urgently needed. This research aims to identify novel protein targets from 28 Y. pestis strains by the integrative pan-genomic and subtractive genomics approach. Additionally, it seeks to screen out potential safe and effective alternative therapies against these targets via high-throughput virtual screening. Targets should lack homology to human, gut microbiota, and known human 'anti-targets', while should exhibit essentiality for pathogen's survival and virulence, druggability, antibiotic resistance, and broad spectrum across multiple pathogenic bacteria. We identified two promising targets: the aminotransferase class I/class II domain-containing protein and 3-oxoacyl-[acyl-carrier-protein] synthase 2. These proteins were modeled using AlphaFold2, validated through several structural analyses, and were subjected to molecular docking and ADMET analysis. Molecular dynamics simulations determined the stability of the ligand-target complexes, providing potential therapeutic options against Y. pestis.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.