Veronica Salmaso

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.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

Synthesis, structure-activity relationships and biological evaluation of 7-phenyl-pyrroloquinolinone 3-amide derivatives as potent antimitotic agents

A small library of 7-pyrrolo[3,2-f]quinolinones was obtained by introducing benzoyl, sulfonyl and carbamoyl side chains at the 3-N position, and their cytotoxicity against a panel of leukemic and solid tumor cell lines was evaluated. Most of them showed high antiproliferative activity with GI50s ranging from micro-to sub-nanomolar values, and these values correlated well with the inhibitory activities of the compounds against tubulin polymerization. Based on a recently proposed colchicine bind site inhibitors (CBSIs) pharmacophore, the interactions of the novel 7-PPyQs at the colchicine domain were rationalized. The most active compounds (4a and 4b) did not induce significant cell death in normal human lymphocytes, suggesting that the compounds may be selective against cancer cells. In particular, 4a was a potent inducer of apoptosis in both the HeLa and Jurkat cell lines. On the other hand, the sulfonyl derivative 4b exhibited a lower potency in comparison with 4a. With both compounds, induction of apoptosis was associated with dissipation of the mitochondrial transmembrane potential and production of reactive oxygen species, suggesting that cells treated with the compounds followed the intrinsic pathway of apoptosis.

The role of 5-arylalkylamino- and 5-piperazino- moieties on the 7-aminopyrazolo[4,3-d]pyrimidine core in affecting adenosine A1 and A2A receptor affinity and selectivity profiles

Abstract New 7-amino-2-phenylpyrazolo[4,3-d]pyrimidine derivatives, substituted at the 5-position with aryl(alkyl)amino- and 4-substituted-piperazin-1-yl- moieties, were synthesized with the aim of targeting human (h) adenosine A1 and/or A2A receptor subtypes. On the whole, the novel derivatives 1–24 shared scarce or no affinities for the off-target hA2B and hA3 ARs. The 5-(4-hydroxyphenethylamino)- derivative 12 showed both good affinity (Ki = 150 nM) and the best selectivity for the hA2A AR while the 5-benzylamino-substituted 5 displayed the best combined hA2A (Ki = 123 nM) and A1 AR affinity (Ki = 25 nM). The 5-phenethylamino moiety (compound 6) achieved nanomolar affinity (Ki = 11 nM) and good selectivity for the hA1 AR. The 5-(N4-substituted-piperazin-1-yl) derivatives 15–24 bind the hA1 AR subtype with affinities falling in the high nanomolar range. A structure-based molecular modeling study was conducted to rationalize the experimental binding data from a molecular point of view using both molecular docking studies and Interaction Energy Fingerprints (IEFs) analysis.

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2

Molecular docking is a powerful tool in the field of computer-aided molecular design. In particular, it is the technique of choice for the prediction of a ligand pose within its target binding site. A multitude of docking methods is available nowadays, whose performance may vary depending on the data set. Therefore, some non-trivial choices should be made before starting a docking simulation. In the same framework, the selection of the target structure to use could be challenging, since the number of available experimental structures is increasing. Both issues have been explored within this work. The pose prediction of a pool of 36 compounds provided by D3R Grand Challenge 2 organizers was preceded by a pipeline to choose the best protein/docking-method couple for each blind ligand. An integrated benchmark approach including ligand shape comparison and cross-docking evaluations was implemented inside our DockBench software. The results are encouraging and show that bringing attention to the choice of the docking simulation fundamental components improves the results of the binding mode predictions.

AquaMMapS: An Alternative Tool to Monitor the Role of Water Molecules During Protein-Ligand Association

Unquestionably, water appears to be an active player in the noncovalent protein–ligand binding process, as it can either bridge interactions between protein and ligand or can be replaced by the bound ligand. Accordingly, in the last decade, alternative computational methodologies have been sought with the aim of predicting the position and thermodynamic profile of water molecules (i.e., hydration sites) in the binding site using either the ligand‐bound or ligand‐free protein conformation. Herein, we present an alternative approach, named AquaMMapS, that provides a three‐dimensional sampling of putative hydration sites. Interestingly, AquaMMapS can post‐inspect molecular dynamics (MD) trajectories obtained from different MD engines using indifferently crystallographic or docking‐driven structures as a starting point. Moreover, AquaMMapS is naturally integrated into supervised molecular dynamics (SuMD) simulations, presenting the possibility to inspect hydration sites during the ligand–protein association process. Finally, a penalty scoring method, named AquaMMapScoring(AMS), was developed to evaluate the number and nature of the water molecules displaced by a ligand approaching its binding site during the binding event, guiding a medicinal chemist to explore the most suitable regions of a ligand that can be decorated either with or without interfering with the interaction networks mediated by water molecules with specific recognition regions of the protein.

New Trends in Inspecting GPCR-ligand Recognition Process: the Contribution of the Molecular Modeling Section (MMS) at the University of Padova.

In this review, we present a survey of the recent advances carried out by our research groups in the field of ligand‐GPCRs recognition process simulations recently implemented at the Molecular Modeling Section (MMS) of the University of Padova. We briefly describe a platform of tools we have tuned to aid the identification of novel GPCRs binders and the better understanding of their binding mechanisms, based on two extensively used computational techniques such as molecular docking and MD simulations. The developed methodologies encompass: (i) the selection of suitable protocols for docking studies, (ii) the exploration of the dynamical evolution of ligand‐protein interaction networks, (iii) the detailed investigation of the role of water molecules upon ligand binding, and (iv) a glance at the way the ligand might go through prior reaching the binding site.

Supervised Molecular Dynamics (SuMD) Approaches in Drug Design

Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation. SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand (small molecules or peptides), and also from its receptor-binding affinity. The application of SuMD highlights an appreciable capability of the technique to reproduce the crystallographic structures of several ligand-protein complexes and can provide high-quality protein-ligand models of for which yet experimental confirmation of binding mode is not available.

Could Adenosine Recognize its Receptors with a Stoichiometry Other than 1 : 1?

One of the most largely accepted concepts in the G protein‐coupled receptors (GPCRs) field is that the ligand, either agonist or antagonist, recognizes its receptor with a stoichiometry of 1 : 1. Recent experimental evidence, reporting ternary complexes formed by GPCR:orthosteric: allosteric ligands, has complicated the ligand‐receptor 1 : 1 binding scenario. Molecular modeling simulations have been used to retrieve insights on the whole ligand‐receptor recognition process, beyond information on the final bound state provided by experimental techniques. The simulation of adenosine binding pathways towards the A2A adenosine receptor highlighted the presence of alternative binding sites (meta‐binding sites) beside the canonical orthosteric one, mainly in proximity to the extracellular vestibule. In light of all these considerations, we investigated the possibility that a second molecule of adenosine engages its receptor when this is already in the holo form, generating a ternary complex with a stoichiometry of 2 : 1. Unexpectedly, supervised molecular dynamics (SuMD) simulations showed that the A2A adenosine receptor could bind the second molecule of adenosine in one of the possible meta‐binding sites as well as into its orthosteric site. The formation of this ternary complex, which favored the formation of the intracellular “ionic lock” between R102 (3.50) and E228 (6.30), could putatively be framed in the context of a negative allosteric regulation.

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.