1,721,046 research outputs found
Protein flexibility in drug discovery: From theory to computation
No full textNowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery
Integrated Approach Including Docking, MD Simulations, and Network Analysis Highlights the Action Mechanism of the Cardiac hERG Activator RPR260243
Full text linkhERG is a voltage-gatedpotassium channel involved inthe heartcontraction whose defections are associated with the cardiac arrhythmiaLong QT Syndrome type 2. The activator RPR260243 (RPR) representsa possible candidate to pharmacologically treat LQTS2 because it enhancesthe opening of the channel. However, the molecular detail of its actionmechanism remains quite elusive. Here, we address the problem usinga combination of docking, molecular dynamics simulations, and networkanalysis. We show that the drug preferably binds at the interfacebetween the voltage sensor and the pore, enhancing the canonical activationpath and determining a whole-structure rearrangement of the channelthat slightly impairs inactivation
Implicit solvent methods for free energy estimation
No full textSolvation is a fundamental contribution in many biological processes and especially in molecular binding. Its estimation can be performed by means of several computational approaches. The aim of this review is to give an overview of existing theories and methods to estimate solvent effects giving a specific focus on the category of implicit solvent models and their use in Molecular Dynamics. In many of these models, the solvent is considered as a continuum homogenous medium, while the solute can be represented at the atomic detail and at different levels of theory. Despite their degree of approximation, implicit methods are still widely employed due to their trade-off between accuracy and efficiency. Their derivation is rooted in the statistical mechanics and integral equations disciplines, some of the related details being provided here. Finally, methods that combine implicit solvent models and molecular dynamics simulation, are briefly described
Role of Molecular Dynamics and Related Methods in Drug Discovery
Full text linkMolecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug–target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug–target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand–target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods’ utility in discovering novel drug candidates
Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin
No full textIt is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets
Probing allosteric communication with combined molecular dynamics simulations and network analysis
Full text linkUnderstanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic
Probing the transport of Ni(II) ions through the internal tunnels of the Helicobacter pylori UreDFG multimeric protein complex
No full textThe survival of several pathogenic bacteria, such as Helicobacter pylori (Hp), relies on the activity of the nickel dependent enzyme urease. Nickel insertion into urease is mediated by a multimeric chaperone complex (HpUreDFG) that is responsible for the transport of Ni(II) from a conserved metal binding motif located in the UreG dimer (CPH motif) to the catalytic site of the enzyme. The X-ray structure of HpUreDFG revealed the presence of water-filled tunnels that were proposed as a route for Ni(II) translocation. Here, we probe the transport of Ni(II) through the internal tunnels of HpUreDFG, from the CPH motif to the external surface of the complex, using microsecond-long enhanced molecular dynamics simulations. The results suggest a “bucketbrigade” mechanism whereby Ni(II) can be transported through a series of stations found along these internal pathways
Enhanced Molecular Dynamics Simulations of Intrinsically Disordered Proteins
No full textMolecular dynamics simulations represent a powerful tool to gain insights into structural and dynamical features of biomolecular systems. Nevertheless, their recognized limitation in terms of achievable timescales becomes particularly severe when dealing with slow processes. In such cases, the employment of enhanced sampling methods, which allow accelerating the characterization of rare events in a timeframe consistent with conventional computational resources, results as crucial. In particular, such advanced techniques have proven highly valuable in the context of protein folding and, specifically, to explore the conformational ensemble spanned by intrinsically disordered proteins (IDPs). Here, we describe how to set up molecular dynamics simulations with one of these enhanced sampling approaches (namely, Parallel Tempering Metadynamics in the Well-Tempered Ensemble) using the NTAIL peptide as a test case
Data-Driven Molecular Dynamics: A Multifaceted Challenge
Full text linkThe big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental dat
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