Alberto Cuzzolin

Understanding allosteric interactions in G protein-coupled receptors using Supervised Molecular Dynamics: A prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000.

The search for G protein-coupled receptors (GPCRs) allosteric modulators represents an active research field in medicinal chemistry. Allosteric modulators usually exert their activity only in the presence of the orthosteric ligand by binding to protein sites topographically different from the orthosteric cleft. They therefore offer potentially therapeutic advantages by selectively influencing tissue responses only when the endogenous agonist is present. The prediction of putative allosteric site location, however, is a challenging task. In facts, they are usually located in regions showing more structural variation among the family members. In the present work, we applied the recently developed Supervised Molecular Dynamics (SuMD) methodology to interpret at the molecular level the positive allosteric modulation mediated by LUF6000 toward the human adenosine A3 receptor (hA3 AR). Our data suggest at least two possible mechanisms to explain the experimental data available. This study represent, to the best of our knowledge, the first case reported of an allosteric recognition mechanism depicted by means of molecular dynamics simulations.

DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations

Virtual screening (VS) is a computational methodology that streamlines the drug discovery process by reducing costs and required resources through the in silico identification of potential drug candidates. Structure-based VS (SBVS) exploits knowledge about the three-dimensional (3D) structure of protein targets and uses the docking methodology as search engine for novel hits. The success of a SBVS campaign strongly depends upon the accuracy of the docking protocol used to select the candidates from large chemical libraries. The identification of suitable protocols is therefore a crucial step in the setup of SBVS experiments. Carrying out extensive benchmark studies, however, is usually a tangled task that requires users’ proficiency in handling different file formats and philosophies at the basis of the plethora of existing software packages. We present here DockBench 1.0, a platform available free of charge that eases the pipeline by automating the entire procedure, from docking benchmark to VS setups. In its current implementation, DockBench 1.0 handles seven docking software packages and offers the possibility to test up to seventeen different protocols. The main features of our platform are presented here and the results of the benchmark study of human Checkpoint kinase 1 (hChk1) are discussed as validation test.

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.

Exploring the recognition pathway at the human A2A adenosine receptor of the endogenous agonist adenosine using supervised molecular dynamics simulations

Adenosine is a naturally occurring purine nucleoside that exerts a variety of important biological functions through the activation of four G protein-coupled receptor (GPCR) isoforms, namely the A1, A2A, A2B and A3 adenosine receptors (ARs). Recently, the X-ray structure of adenosine-bound hA2A AR has been solved, thus providing precious structural details on receptor recognition and activation mechanisms. To date, however, little is still known about the possible recognition pathway the endogenous agonist might go through while approaching the hA2A AR from the extracellular environment. In the present work, we report the adenosine-hA2A AR recognition pathway through the analysis of a series of Supervised Molecular Dynamics (SuMD) trajectories. Interestingly, a possible energetically stable meta-binding site has been detected and characterized.

Implementing the “Best Template Searching” tool into Adenosiland platform

BackgroundAdenosine receptors (ARs) belong to the G protein-coupled receptors (GCPRs) family. The recent release of X-ray structures of the human A2A AR (h A2A AR ) in complex with agonists and antagonists has increased the application of structure-based drug design approaches to this class of receptors. Among them, homology modeling represents the method of choice to gather structural information on the other receptor subtypes, namely A1, A2B, and A3 ARs. With the aim of helping users in the selection of either a template to build its own models or ARs homology models publicly available on our platform, we implemented our web-resource dedicated to ARs, Adenosiland, with the “Best Template Searching” facility. This tool is freely accessible at the following web address: http://mms.dsfarm.unipd.it/Adenosiland/ligand.php.FindingsThe template suggestions and homology models provided by the “Best Template Searching” tool are guided by the similarity of a query structure (putative or known ARs ligand) with all ligands co-crystallized with hA2A AR subtype. The tool computes several similarity indexes and sort the outcoming results according to the index selected by the user.ConclusionsWe have implemented our web-resource dedicated to ARs Adenosiland with the “Best Template Searching” facility, a tool to guide template and models selection for hARs modelling. The underlying idea of our new facility, that is the selection of a template (or models built upon a template) whose co-crystallized ligand shares the highest similarity with the query structure, can be easily extended to other GPCRs.

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.

ALK Kinase Domain Mutations in Primary Anaplastic Large Cell Lymphoma: Consequences on NPM-ALK Activity and Sensitivity to Tyrosine Kinase Inhibitors

ALK inhibitor crizotinib has shown potent antitumor activity in children with refractory Anaplastic Large Cell Lymphoma (ALCL) and the opportunity to include ALK inhibitors in first-line therapies is oncoming. However, recent studies suggest that crizotinib-resistance mutations may emerge in ALCL patients. In the present study, we analyzed ALK kinase domain mutational status of 36 paediatric ALCL patients at diagnosis to identify point mutations and gene aberrations that could impact on NPM-ALK gene expression, activity and sensitivity to small-molecule inhibitors. Amplicon ultra-deep sequencing of ALK kinase domain detected 2 single point mutations, R335Q and R291Q, in 2 cases, 2 common deletions of exon 23 and 25 in all the patients, and 7 splicing-related INDELs in a variable number of them. The functional impact of missense mutations and INDELs was evaluated. Point mutations were shown to affect protein kinase activity, signalling output and drug sensitivity. INDELs, instead, generated kinase-dead variants with dominant negative effect on NPM-ALK kinase, in virtue of their capacity of forming non-functional heterocomplexes. Consistently, when co-expressed, INDELs increased crizotinib inhibitory activity on NPM-ALK signal processing, as demonstrated by the significant reduction of STAT3 phosphorylation. Functional changes in ALK kinase activity induced by both point mutations and structural rearrangements were resolved by molecular modelling and dynamic simulation analysis, providing novel insights into ALK kinase domain folding and regulation. Therefore, these data suggest that NPM-ALK pre-therapeutic mutations may be found at low frequency in ALCL patients. These mutations occur randomly within the ALK kinase domain and affect protein activity, while preserving responsiveness to crizotinib.

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.

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

Abstract 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 H-pyrazolo[4,3-c]quinolin-3-one (PQ) was evaluated by in-vitro and in-silico 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 4e2 and 4h2 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.

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.