Combining molecular dynamics and docking simulations to develop targeted protocols for performing optimized virtual screening campaigns on the HTRPM8 channel

Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 \u3bcs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen\u2122 provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets

EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2

The social and economic impact of the COVID-19 pandemic demands a reduction of the time required to find a therapeutic cure. In this paper, we describe the EXSCALATE molecular docking platform capable to scale on an entire modern supercomputer for supporting extreme-scale virtual screening campaigns. Such virtual experiments can provide in short time information on which molecules to consider in the next stages of the drug discovery pipeline, and it is a key asset in case of a pandemic. The EXSCALATE platform has been designed to benefit from heterogeneous computation nodes and to reduce scaling issues. In particular, we maximized the accelerators’ usage, minimized the communications between nodes, and aggregated the I/O requests to serve them more efficiently. Moreover, we balanced the computation across the nodes by designing an ad-hoc workflow based on the execution time prediction of each molecule. We deployed the platform on two HPC supercomputers, with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billion of small molecules and 15 binding-sites of 12 viral proteins of SARS-CoV-2. The experiment lasted 60 hours and it performed more than one trillion ligand-pocket evaluations, setting a new record on the virtual screening scale

Tunable approximations to control time-to-solution in an HPC molecular docking Mini-App

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded to the later stages of the drug discovery process. Given the huge complexity of the problem, molecular docking cannot be solved by exploring the entire space of the ligand poses. State-of-the-art approaches face the problem by sampling the space of the ligand poses to generate results in a reasonable time budget. In this work, we improve the geometric approach to molecular docking by introducing tunable approximations. In particular, we analysed and enriched the original implementation with tunable software knobs to explore and control the performance-accuracy trade-offs. We modelled time-to-solution of the virtual screening task as a function of software knobs, input data features, and available computational resources. Therefore, the application can autotune its configuration according to a user-defined time budget. We used a Mini-App derived by LiGenDock—a state-of-the-art molecular docking application—to validate the proposed approach. We run the enhanced Mini-App on a high-performance computing system by using a very large database of pockets and ligands. The proposed approach exposes a time-to-solution interval spanning more than one order of magnitude with accuracy degradation up to 30%, more in general providing different accuracy levels according to the needs of the virtual screening campaign

“Molecular Anatomy”: a new multi-dimensional hierarchical scaffold analysis tool

The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we present a novel approach called “Molecular Anatomy” as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together active molecules belonging to different molecular classes, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold’s space and significantly contributing to perform high quality SAR analysis. The protocol is freely available as a web interface at https://ma.exscalate.eu

Computational studies of SARS-CoV-2 3clpro: Insights from md simulations

Given the enormous social and health impact of the pandemic triggered by severe acute respiratory syndrome 2 (SARS-CoV-2), the scientific community made a huge effort to provide an immediate response to the challenges posed by Coronavirus disease 2019 (COVID-19). One of the most important proteins of the virus is an enzyme, called 3CLpro or main protease, already identified as an important pharmacological target also in SARS and Middle East respiratory syndrome virus (MERS) viruses. This protein triggers the production of a whole series of enzymes necessary for the virus to carry out its replicating and infectious activities. Therefore, it is crucial to gain a deeper understanding of 3CLpro structure and function in order to effectively target this enzyme. All-atoms molecular dynamics (MD) simulations were performed to examine the different conformational behaviors of the monomeric and dimeric form of SARS-CoV-2 3CLpro apo structure, as revealed by microsecond time scale MD simulations. Our results also shed light on the conformational dynamics of the loop regions at the entry of the catalytic site. Studying, at atomic level, the characteristics of the active site and obtaining information on how the protein can interact with its substrates will allow the design of molecules able to block the enzymatic function crucial for the virus

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available

Binding mode exploration of b1 receptor antagonists’ by the use of molecular dynamics and docking simulation—how different target engagement can determine different biological effects

The kinin B1 receptor plays a critical role in the chronic phase of pain and inflammation. The development of B1 antagonists peaked in recent years but almost all promising molecules failed in clinical trials. Little is known about these molecules’ mechanisms of action and additional information will be necessary to exploit the potential of the B1 receptor. With the aim of contributing to the available knowledge of the pharmacology of B1 receptors, we designed and characterized a novel class of allosteric non‐peptidic inhibitors with peculiar binding characteristics. Here, we report the binding mode analysis and pharmacological characterization of a new allosteric B1 antagonist, DFL20656. We analyzed the binding of DFL20656 by single point mutagenesis and radioligand binding assays and we further characterized its pharmacology in terms of IC50, B1 receptor internalization and in vivo activity in comparison with different known B1 antagonists. We highlighted how different binding modes of DFL20656 and a Merck compound (compound 14) within the same molecular pocket can affect the biological and pharmacological properties of B1 inhibitors. DFL20656, by its peculiar binding mode, involving tight interactions with N114, efficiently induced B1 receptor internalization and evoked a long‐lasting effect in an in vivo model of neuropathic pain. The pharmacological characterization of different B1 antagonists highlighted the effects of their binding modes on activity, receptor occupancy and internalization. Our results suggest that part of the failure of most B1 inhibitors could be ascribed to a lack of knowledge about target function and engagement

The ANTAREX tool flow for monitoring and autotuning energy efficient HPC systems

Designing and optimizing HPC applications are difficult and complex tasks, which require mastering specialized languages and tools for performance tuning. As this is incompatible with the current trend to open HPC infrastructures to a wider range of users, the availability of more sophisticated programming languages and tools to assist and automate the design stages is crucial to provide smoothly migration paths towards novel heterogeneous HPC platforms. The ANTAREX project intends to address these issues by providing a tool flow, a Domain Specific Launguage and APIs to provide application's adaptivity and to runtime manage and autotune applications for heterogeneous HPC systems. Our DSL provides a separation of concerns, where analysis, runtime adaptivity, performance tuning and energy strategies are specified separately from the application functionalities with the goal to increase productivity, significantly reduce time to solution, while making possible the deployment of substantially improved implementations. This paper presents the ANTAREX tool flow and shows the impact of optimization strategies in the context of one of the ANTAREX use cases related to personalized drug design. We show how simple strategies, not devised by typical compilers, can substantially speedup the execution and reduce energy consumption

DHFR Inhibitors Display a Pleiotropic Anti-Viral Activity against SARS-CoV-2: Insights into the Mechanisms of Action

During the COVID-19 pandemic, drug repurposing represented an effective strategy to obtain quick answers to medical emergencies. Based on previous data on methotrexate (MTX), we evaluated the anti-viral activity of several DHFR inhibitors in two cell lines. We observed that this class of compounds showed a significant influence on the virus-induced cytopathic effect (CPE) partly attributed to the intrinsic anti-metabolic activity of these drugs, but also to a specific anti-viral function. To elucidate the molecular mechanisms, we took advantage of our EXSCALATE platform for in-silico molecular modelling and further validated the influence of these inhibitors on nsp13 and viral entry. Interestingly, pralatrexate and trimetrexate showed superior effects in counteracting the viral infection compared to other DHFR inhibitors. Our results indicate that their higher activity is due to their polypharmacological and pleiotropic profile. These compounds can thus potentially give a clinical advantage in the management of SARS-CoV-2 infection in patients already treated with this class of drugs