Substrate binding process and mechanistic functioning of type 1 11β-hydroxysteroid dehydrogenase from enhanced sampling methods

Understanding the Differences of Danusertib's Residence Time in Aurora Kinases A/B: Dissociation Paths and Key Residues Identified using Conventional and Enhanced Molecular Dynamics Simulations

The accurate experimental estimation of protein-ligand systems' residence time (τ) has become very relevant in drug design projects due to its importance in the last stages of refinement of the drug's pharmacodynamics and pharmacokinetics. It is now well-known that it is not sufficient to estimate the affinity of a protein-drug complex in the thermodynamic equilibrium process in in vitro experiments (closed systems), where the concentrations of the drug and protein remain constant. On the contrary, it is mandatory to consider the conformational dynamics of the system in terms of the binding and unbinding processes between protein and drugs in in vivo experiments (open systems), where their concentrations are in constant flux. This last model has been proven to dictate much of several drugs' pharmacological activities in vivo. At the atomistic level, molecular dynamics simulations can explain why some drugs are more effective than others or unveil the molecular aspects that make some drugs work better in one molecular target. Here, the protein kinases Aurora A/B, complexed with its inhibitor Danusertib, were studied using conventional and enhanced molecular dynamics (MD) simulations to estimate the dissociation paths and, therefore, the computational τ values and their comparison with experimental ones. Using classical molecular dynamics (cMD), three differential residues within the Aurora A/B active site, which seems to play an essential role in the observed experimental Danusertib's residence time against these kinases, were characterized. Then, using WT-MetaD, the relative Danusertib's residence times against Aurora A/B kinases were measured in a nanosecond time scale and were compared to those τ values observed experimentally. In addition, the potential dissociation paths of Danusertib in Aurora A and B were characterized, and differences that might be explained by the differential residues in the enzyme's active sites were found. In perspective, it is expected that this computational protocol can be applied to other protein-ligand complexes to understand, at the molecular level, the differences in residence times and amino acids that may contribute to it.

Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin

It 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.

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.

Discovery of 1'-(1-phenylcyclopropane-carbonyl)-3H-spiro[isobenzofuran-1,3'-pyrrolidin]-3-one as a novel steroid mimetic scaffold for the potent and tissue-specific inhibition of 11β-HSD1 using a scaffold-hopping approach

11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) has been identified as the primary enzyme responsible for the activation of hepatic cortisone to cortisol in specific peripheral tissues resulting in the concomitant antagonism of insulin action within these tissues. Dysregulation of 11β-HSD1, particularly in adipose tissues, has been associated with metabolic syndrome and type 2 diabetes mellitus. Therefore, inhibition of 11β-HSD1 with a small nonsteroidal molecule is therapeutically desirable. Implementation of a scaffold-hopping approach revealed a three-point pharmacophore for 11β-HSD1 that was utilized to design a steroid mimetic scaffold. Reiterative optimization provided valuable insight into the bioactive conformation of our novel scaffold and led to the discovery of INCB13739. Clinical evaluation of INCB13739 confirmed for the first time that tissue-specific inhibition of 11β-HSD1 in patients with type 2 diabetes mellitus was efficacious in controlling glucose levels and reducing cardiovascular risk factors.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Probing the transport of Ni(II) ions through the internal tunnels of the Helicobacter pylori UreDFG multimeric protein complex

The 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 "bucket-brigade" mechanism whereby Ni(II) can be transported through a series of stations found along these internal pathways.

Mapping Ligand-Shape Space for Protein-Ligand Systems: Distinguishing Key-in-Lock and Hand-in-Glove Proteins

Many of the recently developed methods to study the shape of molecules permit one conformation of one molecule to be compared to another conformation of the same or a different molecule: a relative shape. Other methods provide an absolute description of the shape of a conformation that does not rely on comparisons or overlays. Any absolute description of shape can be used to generate a self-organizing map (shape map) that places all molecular shapes relative to one another; in the studies reported here, the shape fingerprint and ultrafast shape recognition methods are employed to create such maps. In the shape maps, molecules that are near one another have similar shapes, and the maps for the 102 targets in the DUD-E set have been generated. By examining the distribution of actives in comparison with their physical-property-matched decoys, we show that the proteins of key-in-lock type (relatively rigid receptor and ligand) can be distinguished from those that are more of a hand-in-glove type (more flexible receptor and ligand). These are linked to known differences in protein flexibility and binding-site size.

Exploring Conformational Dynamics of the Extracellular Venus flytrap Domain of the GABAB Receptor: A Path-Metadynamics Study

γ-Aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system (CNS). Dysfunctional GABAergic neurotransmission is associated with numerous neurological and neuropsychiatric disorders. The GABAB receptor (GABAB-R) is a heterodimeric class C G protein-coupled receptor (GPCR) comprised of GABAB1a/b and GABAB2 subunits. The orthosteric binding site for GABA is located in the extracellular Venus flytrap (VFT) domain of the GABAB1a/b. Knowledge about molecular mechanisms and druggable receptor conformations associated with activation is highly important to understand the receptor function and for rational drug design. Currently, the conformational changes of the receptor upon activation are not well described. On the basis of other class C members, the VFT is proposed to fluctuate between an open/inactive and closed/active state and one of these conformations is stabilized upon ligand binding. In the present study, we investigated the dynamics of the GABAB1b-R VFT in the apo form by combining unbiased molecular dynamics with path-metadynamics. Our simulations confirmed the open/inactive and closed/active state as the main conformations adopted by the receptor. Sizeable energy barriers were found between stable minima, suggesting a relatively slow interconversion. Previously undisclosed metastable states were also identified, which might hold potential for future drug discovery efforts.

Design, synthesis, molecular docking and in vitro evaluation of benzothiazole derivatives as 11β-hydroxysteroid dehydrogenase type 1 inhibitors

11-Beta hydroxysteroid dehydrogenase type 1 (11β-HSD1) regulates cortisol levels mainly in adipose, hepatic and brain tissues. There is a relationship between the high activity of this enzyme and the development of obesity and metabolic disorders. The inhibition of 11β-HSD1 has been shown to attenuate the development of type 2 diabetes mellitus, insulin resistance, metabolic syndrome and other diseases mediated by excessive cortisol production. In this work, fifteen benzothiazole derivatives substituted with electron-withdrawing and electron-donating groups were designed to explore their affinity for 11β-HSD1 using in silico methods. The results show that (E)-5-((benzo[d]thiazol-2-ylimino)(methylthio)methylamino)-2-hydroxybenzoic acid (C1) has good physicochemical properties and favorable interactions with 11β-HSD1 through hydrogen bonding and hydrophobic interactions in the catalytic site formed by Y183, S170 and Y177. Furthermore, C1 was synthesized and evaluated in vitro and ex vivo using clobenzorex (CLX) as a reference drug in obese Zucker rats. The in vitro results showed that C1 was a better inhibitor of human 11β-HSD1 than CLX. The ex vivo assay results demonstrated that C1 was capable of reducing 11β-HSD1 overexpression in mesenteric adipose tissue. Therefore, C1 was able to decrease the activity and expression of 11β-HSD1 better than CLX.

Fully Flexible Docking via Reaction-Coordinate-Independent Molecular Dynamics Simulations

Predicting the geometry of protein-ligand binding complexes is of primary importance for structure-based drug discovery. Molecular dynamics (MD) is emerging as a reliable computational tool for use in conjunction with, or an alternative to, docking methods. However, simulating the protein-ligand binding process often requires very expensive simulations. This drastically limits the practical application of MD-based approaches. Here, we propose a general framework to accelerate the generation of putative protein-ligand binding modes using potential-scaled MD simulations. The proposed dynamical protocol has been applied to two pharmaceutically relevant systems (GSK-3β and the N-terminal domain of HSP90α). Our approach is fully independent of any predefined reaction coordinate (or collective variable). It identified the correct binding mode of several ligands and can thus save valuable computational time in dynamic docking simulations.

Estimation of kinetic and thermodynamic ligand-binding parameters using computational strategies

Kinetic and thermodynamic ligand–protein binding parameters are gaining growing importance as key information to consider in drug discovery. The determination of the molecular structures, using particularly x-ray and NMR techniques, is crucial for understanding how a ligand recognizes its target in the final binding complex. However, for a better understanding of the recognition processes, experimental studies of ligand–protein interactions are needed. Even though several techniques can be used to investigate both thermodynamic and kinetic profiles for a ligand–protein complex, these procedures are very often laborious, time consuming and expensive. In the last 10 years, computational approaches have enormous potential in providing insights into each of the above effects and in parsing their contributions to the changes in both kinetic and thermodynamic binding parameters. The main purpose of this review is to summarize the state of the art of computational strategies for estimating the kinetic and thermodynamic parameters of a ligand–protein binding.

Exploring Flexibility of Progesterone Receptor Ligand Binding Domain Using Molecular Dynamics

Progesterone receptor (PR), a member of nuclear receptor (NR) superfamily, plays a vital role for female reproductive tissue development, differentiation and maintenance. PR ligand, such as progesterone, induces conformation changes in PR ligand binding domain (LBD), thus mediates subsequent gene regulation cascades. PR LBD may adopt different conformations upon an agonist or an antagonist binding. These different conformations would trigger distinct transcription events. Therefore, the dynamics of PR LBD would be of general interest to biologists for a deep understanding of its structure-function relationship. However, no apo-form (non-ligand bound) of PR LBD model has been proposed either by experiments or computational methods so far. In this study, we explored the structural dynamics of PR LBD using molecular dynamics simulations and advanced sampling tools in both ligand-bound and the apo-forms. Resolved by the simulation study, helix 11, helix 12 and loop 895–908 (the loop between these two helices) are quite flexible in antagonistic conformation. Several residues, such as Arg899 and Glu723, could form salt-bridging interaction between helix 11 and helix 3, and are important for the PR LBD dynamics. And we also propose that helix 12 in apo-form PR LBD, not like other NR LBDs, such as human estrogen receptor α (ERα) LBD, may not adopt a totally extended conformation. With the aid of umbrella sampling and metadynamics simulations, several stable conformations of apo-form PR LBD have been sampled, which may work as critical structural models for further large scale virtual screening study to discover novel PR ligands for therapeutic application.

Conformational flexibility of human casein kinase catalytic subunit explored by metadynamics

Casein kinase CK2 is an essential enzyme in higher organisms, catalyzing the transfer of the γ phosphate from ATP to serine and threonine residues on protein substrates. In a number of animal tumors, CK2 activity has been shown to escape normal cellular control, making it a potential target for cancer therapy. Several crystal structures of human CK2 have been published with different conformations for the CK2α catalytic subunit. This variability reflects a high flexibility for two regions of CK2α: the interdomain hinge region, and the glycine-rich loop (p-loop). Here, we present a computational study simulating the equilibrium between three conformations involving these regions. Simulations were performed using well-tempered metadynamics combined with a path collective variables approach. This provides a reference pathway describing the conformational changes being studied, based on analysis of free energy surfaces. The free energies of the three conformations were found to be close and the paths proposed had low activation barriers. Our results indicate that these conformations can exist in water. This information should be useful when designing inhibitors specific to one conformation.

Unbinding free energy of acetylcholinesterase bound oxime drugs along the gorge pathway from metadynamics-umbrella sampling investigation

Because of the pivotal role that the nerve enzyme, acetylcholinesterase plays in terminating nerve impulses at cholinergic synapses. Its active site, located deep inside a 20 Å gorge, is a vulnerable target of the lethal organophosphorus compounds. Potent reactivators of the intoxicated enzyme are nucleophiles, such as bispyridinium oxime that binds to the peripheral anionic site and the active site of the enzyme through suitable cation-π interactions. Atomic scale molecular dynamics and free energy calculations in explicit water are used to study unbinding pathways of two oxime drugs (Ortho-7 and Obidoxime) from the gorge of the enzyme. The role of enzyme-drug cation-π interactions are explored with the metadynamics simulation. The metadynamics discovered potential of mean force (PMF) of the unbinding events is refined by the umbrella sampling (US) corrections. The bidimensional free energy landscape of the metadynamics runs are further subjected to finite temperature string analysis to obtain the transition tube connecting the minima and bottlenecks of the unbinding pathway. The PMF is also obtained from US simulations using the biasing potential constructed from the transition tube and are found to be consistent with the metadynamics-US corrected results. Although experimental structural data clearly shows analogous coordination of the two drugs inside the gorge in the bound state, the PMF of the drug trafficking along the gorge pathway point, within an equilibrium free energy context, to a multistep process that differs from one another. Routes, milestones and subtlety toward the unbinding pathway of the two oximes at finite temperature are identified.