Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches

Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifically HDAC6 is required for the NLRP3 inflammasome to assemble and activate. Thus, NLRP3 serves as an attractive target for the development of novel therapeutic approaches. Several companies are now attempting to develop specific modulators of the NLRP3 inflammasome, but only a handful of small molecules of NLRP3 inflammasome inhibitors, such as MCC950 and Tranilast, are currently available for clinical use. However, their use is limited due to severe side effects and short half-lives. Thus, the repurposing of FDA-approved drugs with NLRP3 inhibitory activity is needed. The present study was aimed at repurposing preexisting drugs that might act as safe and effective NLRP3 inhibitors. A library of 2,697 FDA-approved drugs was screened for binding with NLRP3 (PDB: 7ALV) using Glide (Schrödinger). The top seven FDA-approved drugs with potential binding affinities were selected based on docking scores and subjected to ADMET profiling using pkCSM and SwissADME. The binding of the ADMET-favorable FDA-approved drugs to NLRP3 was validated using MMGBSA (Prime) and Molecular Dynamics (Desmond) in the Schrödinger suite. ADMET profiling revealed that of the seven best docking drugs, empagliflozin and citicoline had good drug-likeness properties. Moreover, MMGBSA analysis and molecular dynamics demonstrated that empagliflozin and citicoline exhibited stable ligand-NLRP3 interactions in the presence of solvents. This study sheds light on the ability of various FDA-approved drugs to act as NLRP3 inhibitors.Communicated by Ramaswamy H. Sarma.

Virtual screening of acetylcholinesterase inhibitors through pharmacophore-based 3D-QSAR modeling, ADMET, molecular docking, and MD simulation studies

Alzheimer's disease (AD) is a leading cause of dementia in elderly patients. The pathophysiology of AD includes various pathways, such as the degradation of acetylcholine, amyloid-beta deposition, neurofibrillary tangle formation, and neuroinflammation. Many studies showed that targeting acetylcholinesterase enzyme (AChE) to improve acetylcholine can be an effective option to treat AD. In the current work, we employed a 3D QSAR-based approach to generate a pharmacophore to screen a chemical library of compounds that may inhibit AChE. Data from experimental studies were collected and used for the generation of pharmacophores. More than 1 million compounds were screened, and further drug-like properties were determined via in-silico ADMET studies. Techniques like molecular docking and molecular dynamics simulation were performed to analyze the binding of novel AChE inhibitors. A novel AChE inhibitor ligand-1 was identified as best with a docking score of -13.560 kcal/mol with RMSD of 1.71 Å during a 100 ns MD run. Further biological studies can give an insight into the potential of ligand-1 as a therapeutic agent for AD.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00189-1.

Binding behavior of receptor binding domain of the SARS-CoV-2 virus and ivermectin

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), sparked an international debate on effective ways to prevent and treat the virus. Specifically, there were many varying opinions on the use of ivermectin (IVM) throughout the world, with minimal research to support either side. IVM is an FDA-approved antiparasitic drug that was discovered in the 1970s and was found to show antiviral activity. The objective of this study is to examine the binding behavior and rates of association and dissociation between SARS-CoV-2 receptor binding domain (RBD), IVM, and their combination using aminopropylsilane (APS) biosensors as surrogates for the hydrophobic interaction between the viral protein and human angiotensin-converting enzyme 2 (ACE2) receptors to determine the potential of IVM as a repurposed drug for SARS-CoV-2 prevention and treatment. The IVM, RBD, and combination binding kinetics were analyzed using biolayer interferometry (BLI) and validated with multiple in silico techniques including protein-ligand docking, molecular dynamics simulation, molecular mechanics-generalized Born surface area (MM-GBSA), and principal component analysis (PCA). Our results suggest that with increasing IVM concentrations the association rate with the hydrophobic biosensor increases with a simultaneous decrease in dissociation. Significant kinetic changes to RBD, when combined with IVM, were found only at a concentration a thousand times the approved dosage with minimal changes found over a 35-min time period. Our study suggests that IVM is not an effective preventative or treatment method at the currently approved dosage.

Phytochemical composition, anti-microbial, anti-oxidant and anti-diabetic effects of Solanum elaeagnifolium Cav. leaves: in vitro and in silico assessments

The aim of this study was to screen the chemical components of Solanum elaeagnifolium leaves and assess their therapeutic attributes with regard to their antioxidant, antibacterial, and antidiabetic activities. The antidiabetic effects were explored to determine the α-amylase and α-glucosidase inhibitory potential of the leaf extract. To identify the active antidiabetic drugs from the extracts, the GC-MS-screened molecules were docked with diabetes-related proteins using the glide module in the Schrodinger Tool. In addition, molecular dynamics (MD) simulations were performed for 100 ns to evaluate the binding stability of the docked complex using the Desmond module. The ethyl acetate had a significant total phenolic content (TPC), with a value of 79.04 ± 0.98 mg/g GAE. The ethanol extract was tested for its minimum inhibitory concentration (MIC) for its bacteriostatic properties. It suppressed the growth of B. subtilis, E. coli, P. vulgaris, R. equi and S. epidermis at a dosage of 118.75 µg/mL. Moreover, the IC50 values of the ethanol extract were determined to be 17.78 ± 2.38 in the α-amylase and and 27.90 ± 5.02 µg/mL in α-glucosidase. The in-silico investigation revealed that cyclolaudenol achieved docking scores of -7.94 kcal/mol for α-amylase. Likewise, the α-tocopherol achieved the docking scores of -7.41 kcal/mol for glycogen phosphorylase B and -7.21 kcal/mol for phosphorylase kinase. In the MD simulations, the cyclolaudenol and α-tocopherol complexes exhibited consistently stable affinities with diabetic proteins throughout the trajectory. Based on these findings, we conclude that this plant could be a good source for the development of novel antioxidant, antibacterial, and antidiabetic agents.Communicated by Ramaswamy H. Sarma.

Rational Design of Dual Inhibitors for Alzheimer's Disease: Insights from Computational Screening of BACE1 and GSK-3β

BACKGROUND

Alzheimer's disease (AD) is one of the most concerned neurodegenerative disorders across the world characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs), leading to cognitive decline and memory loss. Targeting key pathways involved in AD like Aβ and NFT pathways, are crucial for the development of effective therapeutic strategies. In this study, we aimed to identify and establish promising dual inhibitors targeting BACE1 and GSK-3β, two proteins implicated in Aβ and NFT formation respectively.

METHODS

We have used molecular docking, ADME property analysis, and MMGBSA calculations for the identification of hit molecules and further evaluation of binding affinity, drug-like properties, and stability against BACE1 and GSK-3β.

RESULTS

Our results demonstrated strong binding affinities of ZINC000034853956 towards the active sites of both proteins, with favorable interactions involving key residues crucial for inhibitory activity. Additionally, ZINC000034853956 exhibited favorable drug-like properties. MD simulations revealed the stable binding of ZINC000034853956 to both BACE1 and GSK-3β over a 50 ns period, with consistent ligand-protein interactions, such as hydrogen bonding and hydrophobic contacts. These findings highlight the potential of ZINC000034853956 as a promising candidate for AD treatment, acting as a dual inhibitor targeting both BACE1 and GSK-3β. Overall, our study provides valuable insights into the potential of ZINC000034853956 as a dual inhibitor for AD. The strong binding affinity, favorable drug-like properties, and stability observed in MD simulations support its suitability for further optimization and preclinical studies.

CONCLUSION

Further investigations are warranted to elucidate the precise molecular mechanisms and therapeutic benefits of ZINC000034853956. Our findings offer hope for the development of novel therapeutic interventions targeting crucial pathways involved in AD neurodegeneration.

Synthesis, docking studies and biological evaluation of 1H-1,2,3-triazole-7-chloroquinoline derivatives against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded enveloped positive RNA virus and the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Chloroquine (CQ), an antimalarial drug, was reported to be active against several viruses including coronaviruses. The mechanism of host cell invasion by SARS-CoV-2 involves the interaction of angiotensin-converting enzyme (ACE2) with receptor-binding domain (RBD) of spike protein (S). The main protease (Mpro/3CLpro) is an attractive drug target due to its vital function in regulation of polyprotein translated from viral RNA. In this study, a series of novel quinoline-triazole hybrid compounds was synthesized and subjected to evaluations on their cytotoxicity, interactions with different variants of RBD in SARS-CoV-2 and with 3CLpro enzyme by experimental and computational techniques to identify their ability of counteracting viral infection. The results of bio-layer interferometry showed that quinoline derivative 11 has good interaction with delta plus and omicron RBD variants (KD = 3.46 × 10-5 and 6.38 × 10-5 M) while derivative 1 is the best binder for recent variant omicron (KD = 26.9 µM) among the series. Potent compounds 1-4 and 11 also demonstrated a suppressive effect on 3CLpro activity in a non-dose-dependent manner. Further docking study revealed that these compounds interacted within the same area of RBD, while no correlation was found for 3CLpro. Furthermore, the molecular dynamics simulations were carried out to assess the conformational stability of docked complexes for preliminary verification.

Identification of potential phytochemical for the inhibition of non-muscle invasive bladder cancer (NMIBC)

Non-muscle invasive bladder cancer (NMIBC) is one of the most common type of bladder cancer. Here, we have utilized an integrated transcriptomic-computational approach to identify alternate treatments to the NMIBC. In this study, we have performed the comprehensive comparative analysis between three groups of 36 patients with non-relapsed (NR), recurrence and progressive symptoms. Differentially expressed genes involved in the pathways associated with the NMIBC were identified. In silico protein-protein interaction (PPI) network was performed to create the network of the hub genes associated with NMIBC. Further, we compared NR individuals with two cohorts of patients with recurrent and progressive symptoms that lead to the identification of three major biomarkers CD34, FLT1 and WHSC1 genes. Concurrently, PPI also suggests that they are significant hub genes responsible for disease recurrence and progression. Furthermore, targeted genes WHSC-1 and FLT-1 were subjected to virtual screening for identification phytochemical inhibitors. Docking and molecular dynamics simulations concluded that the phytochemicals anonymously named 'UNK' and '6-hydroxycyanidin' are suitable for the inhibition of the proteins causing the NMIBC. In the future, this study will help for strengthening the strategies development at the molecular level for the control of carcinomas at early as well as detection of active and binding site, receptor-ligand interaction and also make drug designing for the early treatment of the carcinomas.Communicated by Ramaswamy H. Sarma.

Khan R, Kumalo HM. Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors

The COVID-19 pandemic has spurred intense research efforts to identify effective treatments for SARS-CoV-2. In silico studies have emerged as a powerful tool in the drug discovery process, particularly in the search for drug candidates that interact with various SARS-CoV-2 receptors. These studies involve the use of computer simulations and computational algorithms to predict the potential interaction of drug candidates with target receptors. The primary receptors targeted by drug candidates include the RNA polymerase, main protease, spike protein, ACE2 receptor, and transmembrane protease serine 2 (TMPRSS2). In silico studies have identified several promising drug candidates, including Remdesivir, Favipiravir, Ribavirin, Ivermectin, Lopinavir/Ritonavir, and Camostat Mesylate, among others. The use of in silico studies offers several advantages, including the ability to screen a large number of drug candidates in a relatively short amount of time, thereby reducing the time and cost involved in traditional drug discovery methods. Additionally, in silico studies allow for the prediction of the binding affinity of the drug candidates to target receptors, providing insight into their potential efficacy. This study is aimed at assessing the useful contributions of the application of computational instruments in the discovery of receptors targeted in SARS-CoV-2. It further highlights some identified advantages and limitations of these studies, thereby revealing some complementary experimental validation to ensure the efficacy and safety of identified drug candidates.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

Molecular Drug Simulation and Experimental Validation of the CD36 Receptor Competitively Binding to Long-Chain Fatty Acids by 7-Ketocholesteryl-9-carboxynonanoate

Long-chain fatty acids (LCFAs) are one of the main energy-supplying substances in the body. LCFAs with different lengths and saturations may have contrasting biological effects that exacerbate or alleviate progress against a variety of systemic disorders of lipid metabolism in organisms. Nonalcoholic fatty liver disease is characterized by chronic inflammation and steatosis, mainly caused by the ectopic accumulation of lipids in the liver, especially LCFAs. CD36 is a scavenger receptor that recognizes and mediates the transmembrane absorption of LCFAs and is expressed in a variety of cells throughout the body. In previous studies, our group found that 7-ketocholesteryl-9-carboxynonanoate (oxLig-1) has the biological effect of targeting CD36 to inhibit oxidized low-density lipoprotein lipotoxicity-induced lipid metabolism disorder; it has an ω-carboxyl physiologically active center and is structurally similar to LCFAs. However, the biological mechanism of oxLig-1 binding to CD36 and competing for binding to different types of LCFAs is still not clear. In this study, molecular docking and molecular dynamics simulation were utilized to simulate and analyze the binding activity between oxLig-1 and different types of LCFAs to CD36 and confirmed by the enzyme-linked immunosorbent assay (ELISA) method. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) platform was applied to predict the drug-forming properties of oxLig-1, and HepG2 cells model of oleic acid and nonalcoholic fatty liver disease (NAFLD) model mice were validated to verify the biological protection of oxLig-1 on lipid lowering. The results showed that there was a co-binding site of LCFAs and oxLig-1 on CD36, and the binding driving forces were mainly hydrogen bonding and hydrophobic interactions. The binding abilities of polyunsaturated LCFAs, oxLig-1, monounsaturated LCFAs, and saturated LCFAs to CD36 showed a decreasing trend in this order. There was a similar decreasing trend in the stability of the molecular dynamics simulation. ELISA results similarly confirmed that the binding activity of oxLig-1 to CD36 was significantly higher than that of typical monounsaturated and saturated LCFAs. ADMET prediction results indicated that oxLig-1 had a good drug-forming property. HepG2 cells model of oleic acid and NAFLD model mice study results demonstrated the favorable lipid-lowering biological effects of oxLig-1. Therefore, oxLig-1 may have a protective effect by targeting CD36 to inhibit the excessive influx and deposition of lipotoxicity monounsaturated LCFAs and saturated LCFAs in hepatocytes.

MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories

Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.