Exploring particulate methane monooxygenase (pMMO) proteins using experimentation and computational molecular docking

In-silico screening of Acacia pennata and Bridelia retusa reveals pinocembrin-7-O-β-D-glucopyranoside as a promising β-lactamase inhibitor to combat antibiotic resistance

The β-lactamase of Pseudomonas aeruginosa is known to degrade β-lactam antibiotics such as penicillins, cephalosporins, monobactams, and carbapenems. With the discovery of an extended-spectrum β-lactamase in a clinical isolate of P. aeruginosa, the bacterium has become multi-drug resistant. In this study, we aim to identify new β-lactamase inhibitors by virtually screening a total of 43 phytocompounds from two Indian medicinal plants. In the molecular docking studies, pinocembrin-7-O-β-D-glucopyranoside (P7G) (-9.6 kcal/mol) from Acacia pennata and ellagic acid (EA) (-9.2 kcal/mol) from Bridelia retusa had lower binding energy than moxalactam (-8.4 kcal/mol). P7G and EA formed 5 (Ser62, Asn125, Asn163, Thr209, and Ser230) and 4 (Lys65, Ser123, Asn125, and Glu159) conventional hydrogens bonds with the active site residues. 100 ns MD simulations revealed that moxalactam and P7G (but not EA) were able to form a stable complex. The binding free energy calculations further revealed that P7G (-59.6526 kcal/mol) formed the most stable complex with β-lactamase when compared to moxalactam (-46.5669 kcal/mol) and EA (-28.4505 kcal/mol). The HOMO-LUMO and other DFT parameters support the stability and chemical reactivity of P7G at the active site of β-lactamase. P7G passed all the toxicity tests and bioavailability tests indicating that it possesses drug-likeness. Among the studied compounds, we identified P7G of A. pennata as the most promising phytocompound to combat antibiotic resistance by potentially inhibiting the β-lactamase of P. aeruginosa.Communicated by Ramaswamy H. Sarma.

Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Cyclooxygenase-2 (COX-2) inhibitors are nonsteroidal anti-inflammatory drugs that treat inflammation, pain and fever. This study determined the interaction mechanisms of COX-2 inhibitors and the molecular properties needed to design new drug candidates. Using machine learning and explainable AI methods, the inhibition activity of 1488 molecules was modelled, and essential properties were identified. These properties included aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. They affected the water solubility, hydrophobicity and binding affinity of COX-2 inhibitors. The binding mode, stability and ADME properties of 16 ligands bound to the Cyclooxygenase active site of COX-2 were investigated by molecular docking, molecular dynamics simulation and MM-GBSA analysis. The results showed that ligand 339,222 was the most stable and effective COX-2 inhibitor. It inhibited prostaglandin synthesis by disrupting the protein conformation of COX-2. It had good ADME properties and high clinical potential. This study demonstrated the potential of machine learning and bioinformatics methods in discovering COX-2 inhibitors.

Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene

Quinonoid dihydropteridine reductase (QDPR) is an enzyme that regulates tetrahydrobiopterin (BH4), a cofactor for enzymes involved in neurotransmitter synthesis and blood pressure regulation. Reduced QDPR activity can cause dihydrobiopterin (BH2) accumulation and BH4 depletion, leading to impaired neurotransmitter synthesis, oxidative stress, and increased risk of Parkinson's disease. A total of 10,236 SNPs were identified in the QDPR gene, with 217 being missense SNPs. Over 18 different sequence-based and structure-based tools were employed to assess the protein's biological activity, with several computational tools identifying deleterious SNPs. Additionally, the article provides detailed information about the QDPR gene and protein structure and conservation analysis. The results showed that 10 mutations were harmful and linked to brain and central nervous system disorders, and were predicted to be oncogenic by Dr. Cancer and CScape. Following conservation analysis, the HOPE server was used to analyse the effect of six selected mutations (L14P, V15G, G23S, V54G, M107K, G151S) on the protein structure. Overall, the study provides insights into the biological and functional impact of nsSNPs on QDPR activity and the potential induced pathogenicity and oncogenicity. In the future, research can be conducted to systematically evaluate QDPR gene variation through clinical studies, investigate mutation prevalence across different geographical regions, and validate computational results with conclusive experiments.Communicated by Ramaswamy H. Sarma.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Identification of novel dual acting ligands targeting the adenosine A2A and serotonin 5-HT1A receptors

GPCRs are a family of transmembrane receptors that are profoundly linked to various neurological disorders, among which is Parkinson's disease (PD). PD is the second most ubiquitous neurological disorder after Alzheimer's disease, characterized by the depletion of dopamine in the central nervous system due to the impairment of dopaminergic neurons, leading to involuntary movements or dyskinesia. The current standard of care for PD is Levodopa, a dopamine precursor, yet the chronic use of this agent can exacerbate motor symptoms. Recent studies have investigated the effects of combining A2AR antagonist and 5-HT1A agonist on dyskinesia and motor complications in animal models of PD. It has been proved that the drug combination has significantly improved involuntary movements while maintaining motor activity, highlighting as a result new lines of therapy for PD treatments, through the regulation of both receptors. Using a combination of ligand-based pharmacophore modelling, virtual screening, and molecular dynamics simulation, this study intends on identifying potential dual-target compounds from IBScreen. Results showed that the selected models displayed good enrichment metrics with a near perfect receiver operator characteristic (ROC) and Area under the accumulation curve (AUAC) values, signifying that the models are both specific and sensitive. Molecular docking and ADMET analysis revealed that STOCK2N-00171 could be potentially active against A2AR and 5-HT1A. Post-MD analysis confirmed that the ligand exhibits a stable behavior throughout the simulation while maintaining crucial interactions. These results imply that STOCK2N-00171 can serve as a blueprint for the design of novel and effective dual-acting ligands targeting A2AR and 5-HT1A.Communicated by Ramaswamy H. Sarma.