DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for docking and molecular dynamics analysis.

MOTIVATION: The ligand/protein binding interaction is typically investigated by docking and molecular dynamics (MD) simulations. In particular, docking-based virtual screening (VS) is used to select the best ligands from database of thousands of compounds, while MD calculations assess the energy stability of the ligand/protein binding complexes. Considering the broad use of these techniques, it is of great demand to have one single software that allows a combined and fast analysis of VS and MD results. With this in mind, we have developed the Drug Discovery Tool (DDT) that is an intuitive graphics user interface able to provide structural data and physico-chemical information on the ligand/protein interaction. RESULTS: DDT is designed as a plugin for the Visual Molecular Dynamics (VMD) software and is able to manage a large number of ligand/protein complexes obtained from AutoDock4 (AD4) docking calculations and MD simulations. DDT delivers four main outcomes: i) ligands ranking based on an energy score; ii) ligand ranking based on a ligands' conformation cluster analysis; iii) identification of the aminoacids forming the most occurrent interactions with the ligands; iv) plot of the ligands' center-of-mass coordinates in the Cartesian space. The flexibility of the software allows saving the best ligand/protein complexes using a number of user-defined options. AVAILABILITY AND IMPLEMENTATION: DDT_site_1 (alternative DDT_site_2); the DDT tutorial movie is available here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aggregation propensity of Aib homo-peptides of different length: an insight from molecular dynamics simulations.

Interactions between peptides are relevant from a biomedical point of view, in particular for the role played by their aggregates in different important pathologies, and also because peptide aggregates represent promising scaffolds for innovative materials. In the present article, the aggregation properties of the homo-peptides formed by α-aminoisobutyric acid (U) residues are discussed. The peptides investigated have chain lengths between six and 15 residues and comprise benzyl and naphthyl groups at the N- and C-termini, respectively. Spectroscopic experiments and molecular dynamics simulations show that the shortest homo-peptide, constituted by six U, does not exhibit any tendency to aggregate under the conditions examined. On the other hand, the homologous peptide with 15 U forms very stable and compact aggregates in 70/30(v/v) methanol/water solution. Atomic force microscopy images indicate that these aggregates promote formation of long fibrils once they are deposited on a mica surface. The aggregation phenomenon is mainly due to hydrophobic interactions occurring between very stable helical structures, and the aromatic groups in the peptides seem to play a minor role.

Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.

Transferring chemical and energetic knowledge between molecular systems with machine learning.

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

Conformational plasticity and allosteric communication networks explain Shelterin protein TPP1 binding to human telomerase.

The Shelterin complex protein TPP1 interacts with human telomerase (TERT) by means of the TEL-patch region, controlling telomere homeostasis. Aberrations in the TPP1-TERT heterodimer formation might lead to short telomeres and severe diseases like dyskeratosis congenita and Hoyeraal-Hreidarsson syndrome. In the present study, we provide a thorough characterization of the structural properties of the TPP1's OB-domain by combining data coming from microsecond-long molecular dynamics calculations, time-series analyses, and graph-based networks. Our results show that the TEL-patch conformational freedom is influenced by a network of long-range amino acid communications that together determine the proper TPP1-TERT binding. Furthermore, we reveal that in TPP1 pathological variants Glu169Δ, Lys170Δ and Leu95Gln, the TEL-patch plasticity is reduced, affecting the correct binding to TERT and, in turn, telomere processivity, which eventually leads to accelerated aging of affected cells. Our study provides a structural basis for the design of TPP1-targeting ligands with therapeutic potential against cancer and telomeropathies.

Uracil/H+ Symport by FurE Refines Aspects of the Rocking-bundle Mechanism of APC-type Transporters.

Transporters mediate the uptake of solutes, metabolites and drugs across the cell membrane. The eukaryotic FurE nucleobase/H+ symporter of Aspergillus nidulans has been used as a model protein to address structure-function relationships in the APC transporter superfamily, members of which are characterized by the LeuT-fold and seem to operate by the so-called 'rocking-bundle' mechanism. In this study, we reveal the binding mode, translocation and release pathway of uracil/H+ by FurE using path collective variable, funnel metadynamics and rational mutational analysis. Our study reveals a stepwise, induced-fit, mechanism of ordered sequential transport of proton and uracil, which in turn suggests that FurE, functions as a multi-step gated pore, rather than employing 'rocking' of compact domains, as often proposed for APC transporters. Finally, our work supports that specific residues of the cytoplasmic N-tail are involved in substrate translocation, in line with their essentiality for FurE function.

Improving Small-Molecule Force Field Parameters in Ligand Binding Studies.

Small molecules are major players of many chemical processes in diverse fields, from material science to biology. They are made by a combination of carbon and heteroatoms typically organized in system-specific structures of different complexity. This peculiarity hampers the application of standard force field parameters and their in silico study by means of atomistic simulations. Here, we combine quantum-mechanics and atomistic free-energy calculations to achieve an improved parametrization of the ligand torsion angles with respect to the state-of-the-art force fields in the paradigmatic molecular binding system benzamidine/trypsin. Funnel-Metadynamics calculations with the new parameters greatly reproduced the high-resolution crystallographic ligand binding mode and allowed a more accurate description of the binding mechanism, when the ligand might assume specific conformations to cross energy barriers. Our study impacts on future drug design investigations considering that the vast majority of marketed drugs are small-molecules.

Ligand binding free-energy calculations with funnel metadynamics.

The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.

Protein-ligand binding with the coarse-grained Martini model.

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein-ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Targeting Cytokine Release Through the Differential Modulation of Nrf2 and NF-κB Pathways by Electrophilic/Non-Electrophilic Compounds.

The transcription factor Nrf2 coordinates a multifaceted response to various forms of stress and to inflammatory processes, maintaining a homeostatic intracellular environment. Nrf2 anti-inflammatory activity has been related to the crosstalk with the transcription factor NF-κB, a pivotal mediator of inflammatory responses and of multiple aspects of innate and adaptative immune functions. However, the underlying molecular basis has not been completely clarified. By combining into new chemical entities, the hydroxycinnamoyl motif from curcumin and the allyl mercaptan moiety of garlic organosulfur compounds, we tested a set of molecules, carrying (pro)electrophilic features responsible for the activation of the Nrf2 pathway, as valuable pharmacologic tools to dissect the mechanistic connection between Nrf2 and NF-κB. We investigated whether the activation of the Nrf2 pathway by (pro)electrophilic compounds may interfere with the secretion of pro-inflammatory cytokines, during immune stimulation, in a human immortalized monocyte-like cell line (THP-1). The capability of compounds to affect the NF-κB pathway was also evaluated. We assessed the compounds-mediated regulation of cytokine and chemokine release by using Luminex X-MAP® technology in human primary peripheral blood mononuclear cells (PBMCs) upon LPS stimulation. We found that all compounds, also in the absence of electrophilic moieties, significantly suppressed the LPS-evoked secretion of pro-inflammatory cytokines such as TNFα and IL-1ß, but not of IL-8, in THP-1 cells. A reduction in the release of pro-inflammatory mediators similar to that induced by the compounds was also observed after siRNA mediated-Nrf2 knockdown, thus indicating that the attenuation of cytokine secretion cannot be directly ascribed to the activation of Nrf2 signaling pathway. Moreover, all compounds, with the exception of compound 1, attenuated the LPS-induced activation of the NF-κB pathway, by reducing the upstream phosphorylation of IκB, the NF-κB nuclear translocation, as well as the activation of NF-κB promoter. In human PBMCs, compound 4 and CURC attenuated TNFα release as observed in THP-1 cells, and all compounds acting as Nrf2 inducers significantly decreased the levels of MCP-1/CCL2, as well as the release of the pro-inflammatory cytokine IL-12. Altogether, the compounds induced a differential modulation of innate immune cytokine release, by differently regulating Nrf2 and NF-κB intracellular signaling pathways.

Ligand Binding, Unbinding, and Allosteric Effects: Deciphering Small-Molecule Modulation of HSP90.

The molecular chaperone HSP90 oversees the functional activation of a large number of client proteins. Because of its role in multiple pathways linked to cancer and neurodegeneration, drug discovery targeting HSP90 has been actively pursued. Yet, a number of inhibitors failed to meet expectations due to induced toxicity problems. In this context, allosteric perturbation has emerged as an alternative strategy for the pharmacological modulation of HSP90 functions. Specifically, novel allosteric stimulators showed the interesting capability of accelerating HSP90 closure dynamics and ATPase activities while inducing tumor cell death. Here, we gain atomistic insight into the mechanisms of allosteric ligand recognition and their consequences on the functional dynamics of HSP90, starting from the fully unbound state. We integrate advanced computational sampling methods based on FunnelMetadynamics, with the analysis of internal dynamics of the structural ensembles visited during the simulations. We observe several binding/unbinding events, and from these, we derive an accurate estimation of the absolute binding free energy. Importantly, we show that different binding poses induce different dynamics states. Our work for the first time explicitly correlates HSP90 responses to binding/unbinding of an allosteric ligand to the modulation of functionally oriented protein motions.

Rational Design of Antiangiogenic Helical Oligopeptides Targeting the Vascular Endothelial Growth Factor Receptors.

Tumor angiogenesis, essential for cancer development, is regulated mainly by vascular endothelial growth factors (VEGFs) and their receptors (VEGFRs), which are overexpressed in cancer cells. Therefore, the VEGF/VEGFR interaction represents a promising pharmaceutical target to fight cancer progression. The VEGF surface interacting with VEGFRs comprises a short α-helix. In this work, helical oligopeptides mimicking the VEGF-C helix were rationally designed based on structural analyses and computational studies. The helical conformation was stabilized by optimizing intramolecular interactions and by introducing helix-inducing Cα,α-disubstituted amino acids. The conformational features of the synthetic peptides were characterized by circular dichroism and nuclear magnetic resonance, and their receptor binding properties and antiangiogenic activity were determined. The best hits exhibited antiangiogenic activity in vitro at nanomolar concentrations and were resistant to proteolytic degradation.

The Molecular Mechanism Underlying Ligand Binding to the Membrane-Embedded Site of a G-Protein-Coupled Receptor.

The crystal structure of P2Y1 receptor (P2Y1R), a class A GPCR, revealed a special extra-helical site for its antagonist, BPTU, which locates in-between the membrane and the protein. However, due to the limitation of crystallization experiments, the membrane was mimicked by use of detergents, and the information related to the binding of BPTU to the receptor in the membrane environment is rather limited. In the present work, we conducted a total of ∼7.5 µs all-atom simulations in explicit solvent using conventional molecular dynamics and multiple enhanced sampling methods, with models of BPTU and a POPC bilayer, both in the absence and presence of P2Y1R. Our simulations revealed that BPTU prefers partitioning into the interface of polar/lipophilic region of the lipid bilayer before associating with the receptor. Then, it interacts with the second extracellular loop of the receptor and reaches the binding site through the lipid-receptor interface. In addition, by use of funnel-metadynamics simulations which efficiently enhance the sampling of bound and unbound states, we provide a statistically accurate description of the underlying binding free energy landscape. The calculated absolute ligand-receptor binding affinity is in excellent agreement with the experimental data (Δ Gb0_theo = -11.5 kcal mol-1, Δ Gb0_exp= -11.7 kcal mol-1). Our study broadens the view of the current experimental/theoretical models and our understanding of the protein-ligand recognition mechanism in the lipid environment. The strategy used in this work is potentially applicable to investigate ligands association/dissociation with other membrane-embedded sites, allowing identification of compounds targeting membrane receptors of pharmacological interest.