1,721,052 research outputs found
Improving Small-Molecule Force Field Parameters in Ligand Binding Studies
No full text: 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
Drug Repurposing on G Protein-Coupled Receptors Using a Computational Profiling Approach
No full text: G protein-coupled receptors (GPCRs) are the largest human membrane receptor family regulating a wide range of cell signaling. For this reason, GPCRs are highly desirable drug targets, with approximately 40% of prescribed medicines targeting a member of this receptor family. The structural homology of GPCRs and the broad spectrum of applications of GPCR-acting drugs suggest an investigation of the cross-activity of a drug toward different GPCR receptors with the aim of rationalizing drug side effects, designing more selective and less toxic compounds, and possibly proposing off-label therapeutic applications. Herein, we present an original in silico approach named "Computational Profiling for GPCRs" (CPG), which is able to represent, in a one-dimensional (1D) string, the physico-chemical properties of a ligand-GPCR binding interaction and, through a tailored alignment algorithm, repurpose the ligand for a different GPCR. We show three case studies where docking calculations and pharmacological data confirm the drug repurposing findings obtained through CPG on 5-hydroxytryptamine receptor 2B, beta-2 adrenergic receptor, and M2 muscarinic acetylcholine receptor. The CPG code is released as a user-friendly graphical user interface with numerous options that make CPG a powerful tool to assist the drug design of GPCR ligands
Funnel metadynamics as accurate binding free-energy method
No full textA detailed description of the events ruling ligand/protein interac- tion and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have de- veloped a metadynamics-based approach, named funnel metady- namics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an effi- cient characterization of the binding free-energy surface and an accurate calculation of the absolute protein–ligand binding free energy. We illustrate our protocol in two systems, benzamidine/ trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray con- formation has been found as the lowest free-energy pose, and the computed protein–ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils im- portant information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel meta- dynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science
Energetics and structural characterization of the large-scale functional motion of adenylate kinase
Full text linkAdenylate Kinase (AK) is a signal transducing protein that regulates cellular energy homeostasis balancing between different conformations. An alteration of its activity can lead to severe pathologies such as heart failure, cancer and neurodegenerative diseases. A comprehensive elucidation of the large-scale conformational motions that rule the functional mechanism of this enzyme is of great value to guide rationally the development of new medications. Here using a metadynamics-based computational protocol we elucidate the thermodynamics and structural properties underlying the AK functional transitions. The free energy estimation of the conformational motions of the enzyme allows characterizing the sequence of events that regulate its action. We reveal the atomistic details of the most relevant enzyme states, identifying residues such as Arg119 and Lys13, which play a key role during the conformational transitions and represent druggable spots to design enzyme inhibitors. Our study offers tools that open new areas of investigation on large-scale motion in proteins
Structural basis of dimerization of chemokine receptors CCR5 and CXCR4
Full text linkG protein-coupled receptors (GPCRs) are prominent drug targets responsible for extracellular-to-intracellular signal transduction. GPCRs can form functional dimers that have been poorly characterized so far. Here, we show the dimerization mechanism of the chemokine receptors CCR5 and CXCR4 by means of an advanced free-energy technique named coarse-grained metadynamics. Our results reproduce binding events between the GPCRs occurring in the minute timescale, revealing a symmetric and an asymmetric dimeric structure for each of the three investigated systems, CCR5/CCR5, CXCR4/CXCR4, and CCR5/CXCR4. The transmembrane helices TM4-TM5 and TM6-TM7 are the preferred binding interfaces for CCR5 and CXCR4, respectively. The identified dimeric states differ in the access to the binding sites of the ligand and G protein, indicating that dimerization may represent a fine allosteric mechanism to regulate receptor activity. Our study offers structural basis for the design of ligands able to modulate the formation of CCR5 and CXCR4 dimers and in turn their activity, with therapeutic potential against HIV, cancer, and immune-inflammatory diseases
Kinetics of protein-ligand unbinding: Predicting pathways, rates, and rate-limiting steps
No full textThe ability to predict the mechanisms and the associated rate constants of protein-ligand unbinding is of great practical importance in drug design. In this work we demonstrate how a recently introduced metadynamics-based approach allows exploration of the unbinding pathways, estimation of the rates, and determination of the rate-limiting steps in the paradigmatic case of the trypsin-benzamidine system. Protein, ligand, and solvent are described with full atomic resolution. Using metadynamics, multiple unbinding trajectories that start with the ligand in the crystallographic binding pose and end with the ligand in the fully solvated state are generated. The unbinding rate k off is computed from the mean residence time of the ligand. Using our previously computed binding affinity we also obtain the binding rate k on. Both rates are in agreement with reported experimental values. We uncover the complex pathways of unbinding trajectories and describe the critical rate-limiting steps with unprecedented detail. Our findings illuminate the role played by the coupling between subtle protein backbone fluctuations and the solvation by water molecules that enter the binding pocket and assist in the breaking of the shielded hydrogen bonds. We expect our approach to be useful in calculating rates for general protein-ligand systems and a valid support for drug design
DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for Docking and Molecular Dynamics analysis
No full textThe 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
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning
Full text linkPredicting structural and energetic properties of a molecular system is one
of the fundamental tasks in molecular simulations, and it has use cases in
chemistry, biology, and medicine. In the past decade, the advent of machine
learning algorithms has impacted 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, possessing a significantly larger number of
atoms and degrees of freedom. In particular, we focus on the classification of
high and low free-energy states. Our approach relies on utilizing (i) a novel
hypergraph representation of molecules, encoding all relevant information for
characterizing the potential energy of a conformation, and (ii) novel message
passing and pooling layers for processing and making predictions on such
hypergraph-structured data. Despite the complexity of the problem, our results
show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the
deca-alanine system. Moreover, we show that the very same transfer learning
approach can be used to group, in an unsupervised way, various 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
Modeling of Cdc25B Dual Specifity Protein Phosphatase Inhibitors: Docking of Ligands and Enzymatic Inhibition Mechanism
No full textThe Cdc25 dual specificity phosphatases have central roles in coordinating cellular signalling processes and cell proliferation. It has been reported that an improper amplification or activation of these enzymes is a distinctive feature of a number of human cancers, including breast cancers. Thus, the inhibition of Cdc25 phosphatases might provide a novel approach for the discovery of new and selective antitumor agents. By using the crystal structure of the catalytic domain of Cdc25B, structural models for the interaction of various Cdc25B inhibitors (1-13) with the enzyme were generated by computational docking. The parallel use of two efficient and predictive docking programs, AutoDock and GOLD, allowed mutual validation of the predicted binding poses. To evaluate their quality, the models were validated with known structure-activity relationships and site-directed mutagenesis data. The results provide an improved basis for structure-based ligand design and suggest a possible explanation for the inhibition mechanism of the examined Cdc25B ligands. We suggest that the recurring motif of a tight interaction between the inhibitor and the two arginine residues, 482 and 544, is of prime importance for reversible enzyme inhibition. In contrast, the irreversible inhibition mechanism of 1-4 seems to be associated with the close vicinity of the quinone ring and the Cys473 catalytic thiolate. We believe that this extensive study might provide useful hints to guide the development of new potent Cdc25B inhibitors as novel anticancer drugs
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