Underappreciated Chemical Interactions in Protein-Ligand Complexes

Role of death-associated protein kinase 1 (DAPK1) in retinal degenerative diseases: an in-silico approach towards therapeutic intervention

The Death-associated protein kinase 1 (DAPK1) has emerged as a crucial player in the pathogenesis of degenerative diseases. As a serine/threonine kinase family member, DAPK1 regulates critical signaling pathways, such as apoptosis and autophagy. In this study, we comprehensively analyzed DAPK1 interactors and enriched molecular functions, biological processes, phenotypic expression, disease associations, and aging signatures to elucidate the molecular networks of DAPK1. Furthermore, we employed a structure-based virtual screening approach using the PubChem database, which enabled the identification of potential bioactive compounds capable of inhibiting DAPK1, including caspase inhibitors and synthetic analogs. Three selected compounds, CID24602687, CID8843795, and CID110869998, exhibited high docking affinity and selectivity towards DAPK1, which were further investigated using molecular dynamics simulations to understand their binding patterns. Our findings establish a connection between DAPK1 and retinal degenerative diseases and highlight the potential of these selected compounds for the development of novel therapeutic strategies. This study provides valuable insights into the molecular mechanisms underlying DAPK1-related diseases, and offers new opportunities for the discovery of effective treatments for retinal degeneration.Communicated by Ramaswamy H. Sarma.

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

Heart failure (HF) is the end stage of cardiovascular disease. Because of its complex condition and poor prognosis, HF has become an important public health problem in the world. Sodium-glucose cotransporter-2 (SGLT2) is a member of the glucose transporter family. Recently, SGLT2 inhibitors have been applied to treat HF. In this study, the main aim was to discover natural SGLT2 inhibitor from Chinese herbs through docking-based virtual screening. Totally 113 natural compounds of potential SGLT2 inhibitor were identified, which displayed docking affinity higher than six approved inhibitors (dapagliflozin (IC50 = 4.9 nM), canagliflozin (IC50 = 4.4 nM 6.7), ipragliflozin (IC50 = 7.4 nM), empagliflozin (IC50 = 3.1 nM), tofogliflozin (IC50 = 4 nM) and luseogliflozin (IC50 = 2.3 nM)) through docking-based virtual screening. Then, the top three hits (ZINC70455591, ZINC85594065 and ZINC14588133) and six known inhibitors were selected for molecular dynamics simulation and the binding free energy calculation using molecular mechanics Poisson-Boltzmann surface area to demonstrate the stability and affinity of docked complexes. These results pointed out that the three docked complexes were stabilized and the chosen compounds were tightly adhering to the binding site of SGLT2. Besides, pharmacokinetic properties of the selected compounds showed those natural compounds may be potential drug candidates. This study may be contributed to further in vitro and in vivo validation and the development of novel SGLT2 inhibitor for treating HF.Communicated by Ramaswamy H. Sarma.

Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.

Why 90% of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies, which raised the question whether certain aspects in target validation and drug optimization are overlooked? Current drug optimization overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity in disease/normal tissues using structure‒tissue exposure/selectivity–relationship (STR), which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity. We propose structure‒tissue exposure/selectivity–activity relationship (STAR) to improve drug optimization, which classifies drug candidates based on drug's potency/selectivity, tissue exposure/selectivity, and required dose for balancing clinical efficacy/toxicity. Class I drugs have high specificity/potency and high tissue exposure/selectivity, which needs low dose to achieve superior clinical efficacy/safety with high success rate. Class II drugs have high specificity/potency and low tissue exposure/selectivity, which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated. Class III drugs have relatively low (adequate) specificity/potency but high tissue exposure/selectivity, which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked. Class IV drugs have low specificity/potency and low tissue exposure/selectivity, which achieves inadequate efficacy/safety, and should be terminated early. STAR may improve drug optimization and clinical studies for the success of clinical drug development.

Docking and ADMET studies for investigating the anticancer potency of Moscatilin on APC10/DOC1 and PKM2 against five clinical drugs

BACKGROUND

Moscatilin is a bibenzyl derivative (stilbenoid), mainly found in Dendrobium species. This plant-derived chemical is a potential cytotoxic anticancer drug that acts against different cancer types. The present study compared the structural interactions of Moscatilin along with five clinically relevant drugs against two target proteins, viz., Anaphase-Promoting Complex subunit 10/Death of Cyclase 1 and Pyruvate Kinase Muscle isozyme M2 in silico. Out of five clinical ligands, four were plant-derived compounds, viz., Resveratrol, Paclitaxel, Shikonin, and Colchicine. The synthetic chemotherapeutic agent, Mitomycin-C, was used as a ligand to compare the mechanistic insights. The objective of the study was to determine the anticancer potency of Moscatilin in silico.

RESULTS

Moscatilin was found to have an advantage over other drugs of interest due to its structural simplicity and folding bridge connecting the bibenzyl structures. Moscatilin exhibited dual function by exclusively affecting the cancer cells, creating instabilities in biochemical and molecular cascades.

CONCLUSIONS

The study demonstrates that Moscatilin is has a multi-antimetastatic function. Moscatilin interaction with APC10/DOC1 indicated that the drug is involved with post-replicative inhibition, and with PKM2 showed glycolytic pathway inhibition in cancer cells. Moscatilin can function as an effective cell cycle inhibitor.