Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment

Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment

Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment

Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment

Perturbation of Fluid Dynamics Properties of Water Molecules during G Protein-Coupled Receptor–Ligand Recognition: The Human A_2A Adenosine Receptor as a Key Study

Recent advances in structural biology revealed that water molecules play a crucial structural role in the protein architecture and ligand binding of G protein-coupled receptors. In this work, we present an alternative approach to monitor the time-dependent organization of water molecules during the final stage of the ligand–receptor recognition process by means of membrane molecular dynamics simulations. We inspect the variation of fluid dynamics properties of water molecules upon ligand binding with the aim to correlate the results with the binding affinities. The outcomes of this analysis are transferred into a bidimensional graph called water fluid dynamics maps, that allow a fast graphical identification of protein “hot-spots” characterized by peculiar shape and electrostatic properties that can play a critical role in ligand binding. We hopefully believe that the proposed approach might represent a valuable tool for structure-based drug discovery that can be extended to cases where crystal structures are not yet available, or have not been solved at high resolution

Alternative Quality Assessment Strategy to Compare Performances of GPCR-Ligand Docking Protocols: The Human Adenosine A_2A Receptor as a Case Study

The progress made in the field of G protein-coupled receptors (GPCRs) structural determination has increased the adoption of docking-driven approaches for the identification or optimization of novel potent and selective ligands. In this work, we compared the performances of the 16 different docking/scoring combinations using the recently released crystal structures of the human A_2A AR (hA_2A AR) in complex with both agonists and antagonists. The proposed evaluation strategy encompasses the use of three complementary “quality descriptors”: (a) the number of conformations generated by a docking algorithm having a RMSD value lower than the crystal structure resolution (R); (b) a novel consensus-based function defined as “protocol score”; and (c) the interaction energy maps (IEMs) analysis, based on the identification of key ligand–receptor interactions observed in the crystal structures

Bridging Molecular Docking to Membrane Molecular Dynamics To Investigate GPCR–Ligand Recognition: The Human A_2A Adenosine Receptor as a Key Study

G protein-coupled receptors (GPCRs) represent the largest family of cell-surface receptors and about one-third of the actual targets of clinically used drugs. Following the progress made in the field of GPCRs structural determination, docking-based screening for novel potent and selective ligands is becoming an increasingly adopted strategy in the drug discovery process. However, this methodology is not yet able to anticipate the “bioactive” binding mode and discern it among other conformations. In the present work, we present a novel approach consisting in the integration of molecular docking and membrane MD simulations with the aim to merge the rapid sampling of ligand poses into in the binding site, typical of docking algorithms, with the thermodynamic accuracy of MD simulations in describing, at the molecular level, the stability a GPCR-ligand complex embedded into explicit lipid–water environment. To validate our approach, we have chosen as a key study the human A_2A adenosine receptor (hA_2A AR) and selected four receptor–antagonist complexes and one receptor–agonist complex that have been recently crystallized. In light of the obtained results, we believe that our novel strategy can be extended to other GPCRs and might represent a valuable tool to anticipate the “bioactive” conformation of high-affinity ligands

Synthesis and preliminary structure-activity relationship study of 2-aryl-2<i>H</i>-pyrazolo[4,3-<i>c</i>]quinolin-3-ones as potential checkpoint kinase 1 (Chk1) inhibitors

<p>The serine-threonine checkpoint kinase 1 (Chk1) plays a critical role in the cell cycle arrest in response to DNA damage. In the last decade, Chk1 inhibitors have emerged as a novel therapeutic strategy to potentiate the anti-tumour efficacy of cytotoxic chemotherapeutic agents. In the search for new Chk1 inhibitors, a congeneric series of 2-aryl-2 <i>H</i>-pyrazolo[4,3-<i>c</i>]quinolin-3-one (PQ) was evaluated by <i>in-vitro</i> and <i>in-silico</i> approaches for the first time. A total of 30 PQ structures were synthesised in good to excellent yields using conventional or microwave heating, highlighting that 14 of them are new chemical entities. Noteworthy, in this preliminary study two compounds <b>4e₂</b> and <b>4h₂</b> have shown a modest but significant reduction in the basal activity of the Chk1 kinase. Starting from these preliminary results, we have designed the second generation of analogous in this class and further studies are in progress in our laboratories.</p

Deciphering the Complexity of Ligand–Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations

Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires classical MD experiments in a long microsecond time scale that are affordable only with a high-level computational capacity. In order to overcome this limiting factor, we have recently implemented an alternative MD approach, named supervised molecular dynamics (SuMD), and successfully applied it to G protein-coupled receptors (GPCRs). SuMD enables the investigation of ligand–receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity. In this article, we present an extension of the SuMD application domain including different types of proteins in comparison with GPCRs. In particular, we have deeply analyzed the ligand–protein recognition pathways of six different case studies that we grouped into two different classes: globular and membrane proteins. Moreover, we introduce the SuMD-Analyzer tool that we have specifically implemented to help the user in the analysis of the SuMD trajectories. Finally, we emphasize the limit of the SuMD applicability domain as well as its strengths in analyzing the complexity of ligand–protein recognition pathways

Novel 3‑Substituted 7‑Phenylpyrrolo[3,2‑<i>f</i>]quinolin-9(6<i>H</i>)‑ones as Single Entities with Multitarget Antiproliferative Activity

A series of chemically modified 7-phenylpyrrolo­[3,2-<i>f</i>]­quinolinones was synthesized and evaluated as anticancer agents. Among them, the most cytotoxic (subnanomolar GI₅₀ values) amidic derivative <b>5f</b> was shown to act as an inhibitor of tubulin polymerization (IC₅₀, 0.99 μM) by binding to the colchicine site with high affinity. Moreover, <b>5f</b> induced cell cycle arrest in the G2/M phase of the cell cycle in a concentration dependent manner, followed by caspase-dependent apoptotic cell death. Compound <b>5f</b> also showed lower toxicity in nontumoral cells, suggesting selectivity toward cancer cells. Additional experiments revealed that <b>5f</b> inhibited the enzymatic activity of multiple kinases, including AURKA, FLT3, GSK3A, MAP3K, MEK, RSK2, RSK4, PLK4, ULK1, and JAK1. Computational studies showed that <b>5f</b> can be properly accommodated in the colchicine binding site of tubulin as well as in the ATP binding clefts of all examined kinases. Our data indicate that the excellent antiproliferative profile of <b>5f</b> may be derived from its interactions with multiple cellular targets