Computational drug discovery under RNA times

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands

Modeling hERG and its interactions with drugs: recent advances in light of current potassium channel simulations

The hERG K(+) channel is responsible for the rapid delayed rectifier current in cardiac myocytes, and a block of its functioning may be related with the (inherited or drug-induced) long QT syndrome. For this reason, in recent times, some interest has arisen around computational studies aimed at developing hERG/drug models for the prediction of drug binding (docking) modes, in view of the assessment of the hERG blocking potential. On the other hand, voltage-gated K(+) channels have been the subject of molecular simulations for several years, and rigorous protocols for studying the main aspects of their functions (permeation, gating, voltage sensing) have been published. In this article, we briefly introduce these classical computational works on K(+) channels, and then review in depth the reports on the latest advanced modeling studies on hERG. The aim is to put the hERG modeling work in the more general context of the ion channel simulations field, to show the peculiarity of hERG on the one side, and also to indicate some possible new avenues in the use of modeling techniques to increase our knowledge of this important channel

In silico modelling--pharmacophores and hERG channel models

In computational drug design, modelling studies are undertaken following two main strategies that depend on which information is available. If experimental data exist only for the molecules displaying the biological property of interest, a so-called ligand-based approach is taken; if information is available on the macromolecular target(s) of the compounds (e.g. proteins' 3D structures), target-based studies can be carried out. Recently, in the field of hERG K+-channel blocking drugs, pharmacophoric (ligand-based) studies started appearing aimed at determining the physicochemical features associated with the channel block, and also at predicting the hERG blocking potential of compounds. However, partial homology models (target-based) of the hERG channel have also been built and used as working tools to interpret electrophysiological and mutagenesis studies. Here, we review some of the ligand- and target-based in silico studies carried out on hERG, focusing on both their main characteristics and their meaning. In addition, we discuss some methodological aspects of the computational work that in our opinion should be considered, in view of the construction of reliable models possibly able to predict the functional behaviour of the channel system and the blocking potential of drugs

Substrate Binding Process and Mechanistic Functioning of Type 1 11beta-Hydroxysteroid Dehydrogenase from Enhanced Sampling Methods

In humans, type 1 11b-hydroxysteroid dehydrogenase (11b-HSD-1) plays a key role in the regulation of the glucocorticoids balance by converting the inactive hormone cortisone into cortisol. Numerous functional aspects of 11b-HSD-1 have been understood thanks to the availability at the Worldwide Protein Data Bank of a number of X-ray structures of the enzyme either alone or in complex with inhibitors, and to several experimental data. However at present, a complete description of the dynamic behaviour of 11b-HSD-1 upon substrate binding is missing. To this aim we firstly docked cortisone into the catalytic site of 11b-HSD-1 (both wild type and Y177A mutant), and then we used steered molecular dynamics and metadynamics to simulate its undocking. This methodology helped shedding light at molecular level on the complex relationship between the enzyme and its natural substrate. In particular, the work highlights a) the reason behind the functional dimerisation of 11b-HSD-1, b) the key role of Y177 in the cortisone binding event, c) the fine tuning of the active site degree of solvation, and d) the role of the S228-P237 loop in ligand recognition

Applications of metadynamics to protein-ligand docking

In the lecture, applications of enhanced sampling approaches (e.g., metadynamics) to study protein-drug interactions were illustrated

Exploring complex protein-ligand recognition mechanisms with coarse metadynamics

The metadynamics method has been shown to be a valuable tool to study the mechanism of molecular recognition in atomistic detail [Gervasio, F. L.; et al. J. Am. Chem. Soc. 2005, 127, 2600]. However, it requires an a priori knowledge of all slow degrees of freedom relevant to the docking/undocking mechanism. Here we investigate a combination of docking/clustering with metadynamics performed with a subset of the necessary degrees of freedom (coarse metadynamics), and show that it provides a full mechanistic insight on the protein-ligand docking mechanism. Moreover, the proposed protocol is able to clearly distinguish between crystallographic and noncrystallographic poses of protein-ligand complexes, and also to find the transition state of the full undocking mechanism, thus giving an indication on the binding free energy

Three-dimensional model of the human aromatase enzyme and density functional parameterization of the iron-containing protoporphyrin IX for molecular dynamics study of heme-cysteinato cytochromes

Mammalian cytochromes P450 (CYP) are enzymes of great biological and pharmaco-toxicological relevance. Due to their membrane-bound nature, the structural characterization of these proteins is extremely difficult, and therefore computational techniques, such as comparative modeling, may help obtaining reliable structures of members of this family. An important feature of CYP is the presence of an iron-containing porphyrin group at the enzyme active site. This calls for quantum chemical calculations to derive charges and parameters suitable for classical force field-based investigations of this proteins family. In this report, we first carried out density functional theory (DFT) computations to derive suitable charges for the Fe2+-containing heme group of P450 enzymes. Then, by means of the homology modeling technique, and taking advantage of the recently published crystal structure of the human CYP2C9, we built a new model of the human aromatase (CYP19) enzyme. Furthermore, to study the thermal stability of the new model as well as to test the suitability of the new DFT-based heme parameters, molecular dynamics (MD) simulations were carried out on both CYP2C9 and CYP19. Finally, the last few ns of aromatase MD trajectories were investigated following the essential dynamics protocol that allowed the detection of some correlated motions among some protein domains

An Integrated Markov State Model and Path Metadynamics Approach to Characterize Drug Binding Processes

Unveiling the mechanistic features of drug-target binding is of central interest in biophysics and drug discovery. Herein, we address this challenge by combining two major computational approaches, namely, Molecular Dynamics (MD) simulations and Markov State Models (MSM), with a Path Collective Variables (PCVs) description coupled with metadynamics. We apply our methodology to reconstruct the binding process of the antagonist alprenolol to the beta(2)-adrenergic receptor, a well-established pharmaceutical target. The devised protocol allowed us to estimate the binding free energy and identify the minimum free energy path leading to the protein-ligand complex. In summary, we show that MSM and PCVs can be efficiently integrated to shed light upon mechanistic and energetic details underlying complex recognition processes in biological systems

Modeling lipid raft domains containing a mono-unsaturated phosphatidylethanolamine species

Several membrane proteins are preferentially partitioned in lipid microdomains called rafts. The hypothesis of an intimate relationship between proteins and their specific raft environment is nowadays widely accepted. Indeed, the raft-protein cross-talk would influence protein activity and trafficking either by specific lipid-protein interactions or changes in physico-chemical properties of the bilayer. Although lipid rafts used to be simply considered membrane patches enriched in sphingolipids, cholesterol, and saturated phosphocholine derivatives, the optimization of extraction procedures and recent lipidomic analyses challenged this established concept, highlighting a significant presence of phosphatidylethanolamine species. Relying on this evidence, we devised a generic coarse-grained raftlike model containing di-stearoyl phosphatidylcholine, cholesterol and palmitoyl-oleoyl phosphatidylethanolamine species. The model was validated against available experimental data by studying the lipid mixture at different molar ratios through extended molecular dynamics simulations. The agreement of structural and dynamical properties with those of a liquid-ordered crystalline phase suggests that our model can represent a reliable lipid environment especially suited for computational studies aimed at unraveling raft-protein functional interactions