In silico discovery of potential sodium-glucose cotransporter-2 inhibitors from natural products for treatment of heart failure via molecular docking and molecular dynamics simulation approach

A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein

The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and -8.66 kcal mol-1, respectively), when compared to the control drug, Ribavirin (-7.01 and -6.52 kcal mol-1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol-1) and NiV M (-40.26 kJ mol-1) proteins in comparison with the control drug Ribavirin (-13.12 and -13.94 kJ mol-1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Relationship of single and co-exposure of per-and polyfluoroalkyl substances and their alternatives with uric acid: A community-based study in China

Numerous studies have suggested per-and polyfluoroalkyl substances (PFASs) are related to uric acid levels, but evidence related to PFAS alternatives is limited. Moreover, the effect of the combined exposure to PFASs and their alternatives on uric acid has not been reported. Hence, we conducted a cross-sectional study involving 1312 adults in Guangzhou, China. Generalized linear regression model was adopted to explore the effect of single PFAS exposure on serum uric acid levels. Further, multi-pollutant models such as Bayesian kernel machine regression, weighted quantile sum, and quantile G-computation were employed to investigate the combined association of PFASs and alternatives with serum uric acid levels. We performed molecular docking to understand the potential interaction of PFAS with Organic Anion Transporters (OATs), involved in the secretion of uric acid. Per log serum 6:2 Cl-PFESA and PFOA increases were accompanied with an increase of serum uric acid with statistical significance (for 6:2 Cl-PFESA: beta: 0.19 ng/mL, 95% CI 0.11-0.26 and for PFOA: beta: 0.43 ng/mL, 95% CI 0.34-0.52). The associations were strongest among overweight and elderly. Multi-pollutant models also revealed a positive association. These positive associations may be PFASs can competitively combine with OAT1 and OAT3, leading to the increase of serum uric acid.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.