Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.

Function2Form Bridge-Toward synthetic protein holistic performance prediction

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Computational chemistry methods to investigate the effects caused by DNA variants linked with disease

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (r_p = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (r_p = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.

Probing the Existence of a Metastable Binding Site at the β2-Adrenergic Receptor with Homobivalent Bitopic Ligands

Herein, we report the development of bitopic ligands aimed at targeting the orthosteric binding site (OBS) and a metastable binding site (MBS) within the same receptor unit. Previous molecular dynamics studies on ligand binding to the β₂-adrenergic receptor (β₂AR) suggested that ligands pause at transient, less-conserved MBSs. We envisioned that MBSs can be regarded as allosteric binding sites and targeted by homobivalent bitopic ligands linking two identical pharmacophores. Such ligands were designed based on docking of the antagonist (S)-alprenolol into the OBS and an MBS and synthesized. Pharmacological characterization revealed ligands with similar potency and affinity, slightly increased β₂/β₁AR-selectivity, and/or substantially slower β₂AR off-rates compared to (S)-alprenolol. Truncated bitopic ligands suggested the major contribution of the metastable pharmacophore to be a hydrophobic interaction with the β₂AR, while the linkers alone decreased the potency of the orthosteric fragment. Altogether, the study underlines the potential of targeting MBSs for improving the pharmacological profiles of ligands.

Utilization of Biased G Protein-Coupled ReceptorSignaling towards Development of Safer andPersonalized Therapeutics

G protein-coupled receptors (GPCRs) are involved in a wide variety of physiological processes. Therefore, approximately 40% of currently prescribed drugs have targeted this receptor family. Discovery of β-arrestin mediated signaling and also separability of G protein and β-arrestin signaling pathways have switched the research focus in the GPCR field towards development of biased ligands, which provide engagement of the receptor with a certain effector, thus enriching a specific signaling pathway. In this review, we summarize possible factors that impact signaling profiles of GPCRs such as oligomerization, drug treatment, disease conditions, genetic background, etc. along with relevant molecules that can be used to modulate signaling properties of GPCRs such as allosteric or bitopic ligands, ions, aptamers and pepducins. Moreover, we also discuss the importance of inclusion of pharmacogenomics and molecular dynamics simulations to achieve a holistic understanding of the relation between genetic background and structure and function of GPCRs and GPCR-related proteins. Consequently, specific downstream signaling pathways can be enriched while those that bring unwanted side effects can be prevented on a patient-specific basis. This will improve studies that centered on development of safer and personalized therapeutics, thus alleviating the burden on economy and public health.

How peptide/MHC presence affects the dynamics of the LC13 T-cell receptor

The interaction between T-cell receptors (TCRs) of T-cells and potentially immunogenic peptides presented by MHCs of antigen presenting cells is one of the most important mechanisms of the adaptive human immune system. A large number of structural simulations of the TCR/peptide/MHC system have been carried out. However, to date no study has investigated the differences of the dynamics between free TCRs and pMHC bound TCRs on a large scale. Here we present a study totalling 37 100 ns investigating the LC13 TCR in its free form as well as in complex with HLA-B*08:01 and different peptides. Our results show that the dynamics of the bound and unbound LC13 TCR differ significantly. This is reflected in (a) expected results such as an increased flexibility and increased solvent accessible surface of the CDRs of unbound TCR simulations but also in (b) less expected results such as lower CDR distances and compactness as well as alteration in the hydrogen bond network around CDR3α of unbound TCR simulations. Our study further emphasises the structural flexibility of TCRs and confirms the importance of the CDR3 loops for the adoption to MHC.

Aminergic GPCR-Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data

The aminergic family of G protein-coupled receptors (GPCRs) plays an important role in various diseases and represents a major drug discovery target class. Structure determination of all major aminergic subfamilies has enabled structure-based ligand design for these receptors. Site-directed mutagenesis data provides an invaluable complementary source of information for elucidating the structural determinants of binding of different ligand chemotypes. The current study provides a comparative analysis of 6692 mutation data points on 34 aminergic GPCR subtypes, covering the chemical space of 540 unique ligands from mutagenesis experiments and information from experimentally determined structures of 52 distinct aminergic receptor-ligand complexes. The integrated analysis enables detailed investigation of structural receptor-ligand interactions and assessment of the transferability of combined binding mode and mutation data across ligand chemotypes and receptor subtypes. An overview is provided of the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.

Structural Basis for Binding of Allosteric Drug Leads in the Adenosine A1 Receptor

Despite intense interest in designing positive allosteric modulators (PAMs) as selective drugs of the adenosine A₁ receptor (A₁AR), structural binding modes of the receptor PAMs remain unknown. Using the first X-ray structure of the A₁AR, we have performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) technique to determine binding modes of the A₁AR allosteric drug leads. Two prototypical PAMs, PD81723 and VCP171, were selected. Each PAM was initially placed at least 20 Å away from the receptor. Extensive GaMD simulations using the AMBER and NAMD simulation packages at different acceleration levels captured spontaneous binding of PAMs to the A₁AR. The simulations allowed us to identify low-energy binding modes of the PAMs at an allosteric site formed by the receptor extracellular loop 2 (ECL2), which are highly consistent with mutagenesis experimental data. Furthermore, the PAMs stabilized agonist binding in the receptor. In the absence of PAMs at the ECL2 allosteric site, the agonist sampled a significantly larger conformational space and even dissociated from the A₁AR alone. In summary, the GaMD simulations elucidated structural binding modes of the PAMs and provided important insights into allostery in the A₁AR, which will greatly facilitate the receptor structure-based drug design.

Gaussian accelerated molecular dynamics for elucidation of drug pathways

INTRODUCTION

Understanding pathways and mechanisms of drug binding to receptors is important for rational drug design. Remarkable advances in supercomputing and methodological developments have opened a new era for application of computer simulations in predicting drug-receptor interactions at an atomistic level. Gaussian accelerated molecular dynamics (GaMD) is a computational enhanced sampling technique that works by adding a harmonic boost potential to reduce energy barriers. GaMD enables free energy calculations without the requirement of predefined collective variables. GaMD has proven useful in biomolecular simulations, in particular, the prediction of drug-receptor interactions. Areas covered: Herein, the authors review recent GaMD simulation studies that elucidated pathways of drug binding to proteins including the G-protein-coupled receptors and HIV protease. Expert opinion: GaMD is advantageous for enhanced simulations of, amongst many biological processes, drug binding to target receptors. Compared with conventional molecular dynamics, GaMD speeds up biomolecular simulations by orders of magnitude. GaMD enables routine drug binding simulations using personal computers with GPUs or common computing clusters. GaMD and, more broadly, enhanced sampling simulations are expected to dramatically increase our capabilities to determine the mechanisms of drug binding to a wide range of receptors in the near future. This will greatly facilitate computer-aided drug design.

Molecular Dynamics Simulation for All

The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of biomolecular simulation to experimentalists-a trend particularly noticeable in, although certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small molecules, peptides, and proteins. Here we describe, in practical terms, the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

Recent Trends and Applications of Molecular Modeling in GPCR⁻Ligand Recognition and Structure-Based Drug Design

G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.

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 G_{b0_theo} = -11.5 kcal mol^-1, Δ G_{b0_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.

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.

A molecular dynamics simulation study decodes the Zika virus NS5 methyltransferase bound to SAH and RNA analogue

Since 2015, widespread Zika virus outbreaks in Central and South America have caused increases in microcephaly cases, and this acute problem requires urgent attention. We employed molecular dynamics and Gaussian accelerated molecular dynamics techniques to investigate the structure of Zika NS5 protein with S-adenosyl-L-homocysteine (SAH) and an RNA analogue, namely 7-methylguanosine 5'-triphosphate (m7GTP). For the binding motif of Zika virus NS5 protein and SAH, we suggest that the four Zika NS5 substructures (residue orders: 101-112, 54-86, 127-136 and 146-161) and the residues (Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148) might be responsible for the selectivity of the new Zika virus drugs. For the binding motif of Zika NS5 protein and m7GTP, we suggest that the three Zika NS5 substructures (residue orders: 11-31, 146-161 and 207-218) and the residues (Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215) might be responsible for the selectivity of the new Zika virus drugs.

Ligand Binding Pathways and Conformational Transitions of the HIV Protease

It is important to determine the binding pathways and mechanisms of ligand molecules to target proteins to effectively design therapeutic drugs. Molecular dynamics (MD) is a promising computational tool that allows us to simulate protein-drug binding at an atomistic level. However, the gap between the time scales of current simulations and those of many drug binding processes has limited the usage of conventional MD, which has been reflected in studies of the HIV protease. Here, we have applied a robust enhanced simulation method, Gaussian accelerated molecular dynamics (GaMD), to sample binding pathways of the XK263 ligand and associated protein conformational changes in the HIV protease. During two of 10 independent GaMD simulations performed over 500-2500 ns, the ligand was observed to successfully bind to the protein active site. Although GaMD-derived free energy profiles were not fully converged because of insufficient sampling of the complex system, the simulations still allowed us to identify relatively low-energy intermediate conformational states during binding of the ligand to the HIV protease. Relative to the X-ray crystal structure, the XK263 ligand reached a minimum root-mean-square deviation (RMSD) of 2.26 Å during 2.5 μs of GaMD simulation. In comparison, the ligand RMSD reached a minimum of only ∼5.73 Å during an earlier 14 μs conventional MD simulation. This work highlights the enhanced sampling power of the GaMD approach and demonstrates its wide applicability to studies of drug-receptor interactions for the HIV protease and by extension many other target proteins.

Membrane-embedded substrate recognition by cytochrome P450 3A4

Cytochrome P450 3A4 (CYP3A4) is the dominant xenobiotic-metabolizing enzyme in the liver and intestine and is involved in the disposition of more than 50% of drugs. Because of its ability to bind multiple substrates, its reaction kinetics are complex, and its association with the microsomal membrane confounds our understanding of how this enzyme recognizes and recruits diverse substrates. Testosterone (TST) hydroxylation is the prototypical CYP3A4 reaction, displaying positive homotropic cooperativity with three binding sites. Here, exploiting the capability of accelerated molecular dynamics (aMD) to sample events in the millisecond regime, I performed >25-μs aMD simulations in the presence of three TST molecules. These simulations identified high-occupancy surface-binding sites as well as a pathway for TST ingress into the CYP3A4 active site originating in the membrane. Adaptive biasing force analysis of the latter pathway revealed a metastable intermediate that could constitute a third binding site at high TST concentrations. Prompted by the observation that interactions between TST and the G'-helix mobilize the ligand into the active site, a free-energy analysis of TST distribution in the membrane was conducted and revealed that the depth of the G'-helix is optimal for extracting TST. In summary, these simulations confirm separate, but adjacent substrate-binding sites within the enzyme and the existence of an auxiliary TST-binding site. The broader impact of these simulations is that they support a mechanism in which cytochromes P450 directly recruit membrane-solubilized substrates.

Molecular Basis of Ligand Dissociation from G Protein-Coupled Receptors and Predicting Residence Time

G protein-coupled receptors (GPCRs) are integral membrane proteins and represent the largest class of drug targets. During the past decades progress in structural biology has enabled the crystallographic elucidation of the architecture of these important macromolecules. It also provided atomic-level visualization of ligand-receptor interactions, dramatically boosting the impact of structure-based approaches in drug discovery. However, knowledge obtained through crystallography is limited to static structural information. Less information is available showing how a ligand associates with or dissociates from a given receptor, whose importance is in fact increasingly recognized by the drug research community. Owing to recent advances in computer power and algorithms, molecular dynamics stimulations have become feasible that help in analyzing the kinetics of the ligand binding process. Here, we review what is currently known about the dynamics of GPCRs in the context of ligand association and dissociation, as determined through both crystallography and computer simulations. We particularly focus on the molecular basis of ligand dissociation from GPCRs and provide case studies that predict ligand dissociation pathways and residence time.

Using accelerated molecular dynamics simulation to shed light on the mechanism of activation/deactivation upon mutations for CCR5

In this work, accelerated molecular dynamics (aMD) simulations were used to study different effects of G286F and R126 mutations on the activity of CCR5. Potential of Mean Force (PMF) results indicate that there are stable inactive-like states and active-like ones existing in the conformation space of the wild type (WT), confirming that CCR5 could possess to some extent constitutive activity. But the R126N mutation could constrain CCR5 in the inactive state through influencing the TXP motif and limiting the movements of TM5 and TM6. In contrast, the G286F mutation promotes the activity of the receptor by increasing the distance of TM2–TM6 and the flexibility of the intracellular part of TM5 and changing the H-bonding in the TXP motif. The observations from the cross correlation analysis further show that the R126N mutation dramatically reduces the motion correlations between TMs, which should partly contribute to the deactivation of CCR5. Compared with the WT system, TM6 and TM7 in the G286F mutant are loosely correlated with other regions, which should be conducive to drive the movement of TM6 and TM7 toward the active conformation. In addition, the result from the protein structure network (PSN) analysis reveals that the shortest pathways connecting the extracellular and the intracellular domains are highly conserved in the three systems despite the different mutations, in which the hydrogen bond plays a pivotal role. However, the G286F mutation shortens the lifetime of the pathway with respect to the R126N mutation, which may be associated with the different activities of the two mutants. The pathway connecting the ligand-binding site and the G-protein region reveals that the allosteric communication between TM6 and TM7 is enhanced by the R126N mutation while the G286F mutation induces the activation of the G-protein pocket by arousing more residues in the NPxxY region to participate in the pathway.

In this work, accelerated molecular dynamics (aMD) simulations were used to study different effects of G286F and R126 mutations on the activity of CCR5.

Molecular Dynamics Simulation and Prediction of Druggable Binding Sites

Binding site identification and druggability evaluation are two essential steps in structure-based drug design. A druggable binding site tends to have high binding affinity to drug-like molecules. Predicting such sites can have a significant impact on a drug design campaign. This chapter focuses on summarizing the different methods that are used to predict druggable binding sites. The chapter also discusses the importance of including protein flexibility in the search process and the use of molecular dynamics simulations to address this aspect. Case studies from the literature are also summarized and discussed. We hope that this chapter would provide an overview on the different methods employed in binding site identification evaluation.

Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations

An increasing number of G protein-coupled receptor (GPCR) crystal structures provide important-albeit static-pictures of how small molecules or peptides interact with their receptors. These high-resolution structures represent a tremendous opportunity to apply molecular dynamics (MD) simulations to capture atomic-level dynamical information that is not easy to obtain experimentally. Understanding ligand binding and unbinding processes, as well as the related responses of the receptor, is crucial to the design of better drugs targeting GPCRs. Here, we discuss possible ways to study the dynamics involved in the binding of small molecules to GPCRs, using long timescale MD simulations or metadynamics-based approaches.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

Temporal Bias: Time-Encoded Dynamic GPCR Signaling

Evidence suggests that cells can time-encode signals for secure transport and perception of information, and it appears that this dynamic signaling is a common principle of nature to code information in time. G-protein-coupled receptor (GPCR) signaling networks are no exception as their composition and signal transduction appear temporally flexible. In this review, we discuss the potential mechanisms by which GPCRs code biological information in time to create 'temporal bias.' We highlight dynamic signaling patterns from the second messenger to the receptor-ligand level and shed light on the dynamics of G-protein cycles, the kinetics of ligand-receptor interaction, and the occurrence of distinct signaling waves within the cell. A dynamic feature such as temporal bias adds to the complexity of GPCR signaling bias and gives rise to the question whether this trait could be exploited to gain control over time-encoded cell physiology.

Identification of a Different Agonist-Binding Site and Activation Mechanism of the Human P2Y1 Receptor

The human P2Y1 receptor (P2Y1R) is a purinergic G-protein-coupled receptor (GPCR) that functions as a receptor for adenosine 5'-diphosphate (ADP). An antagonist of P2Y1R might potentially have antithrombotic effects, whereas agonists might serve as antidiabetic agents. On the basis of the antagonist-bound MRS2500-P2Y1R crystal structure, we constructed computational models of apo-P2Y1R and the agonist-receptor complex 2MeSADP-P2Y1R. We then performed conventional molecular dynamics (cMD) and accelerated molecular dynamics (aMD) simulations to study the conformational dynamics after binding with agonist/antagonist as well as the P2Y1R activation mechanism. We identified a new agonist-binding site of P2Y1R that is consistent with previous mutagenesis data. This new site is deeper than those of the agonist ADP in the recently simulated ADP-P2Y1R structure and the antagonist MRS2500 in the MRS2500-P2Y1R crystal structure. During P2Y1R activation, the cytoplasmic end of helix VI shifts outward 9.1 Å, the Ser1463.47-Tyr2375.58 hydrogen bond breaks, a Tyr2375.58-Val2626.37 hydrogen bond forms, and the conformation of the χ1 rotamer of Phe2696.44 changes from parallel to perpendicular to helix VI. The apo-P2Y1R system and the MRS2500-P2Y1R system remain inactive. The newly identified agonist binding site and activation mechanism revealed in this study may aid in the design of P2Y1R antagonists/agonists as antithrombotic/antidiabetic agents, respectively.

Adaptive simulations, towards interactive protein-ligand modeling

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.

Gaussian Accelerated Molecular Dynamics: Theory, Implementation, and Applications

A novel Gaussian Accelerated Molecular Dynamics (GaMD) method has been developed for simultaneous unconstrained enhanced sampling and free energy calculation of biomolecules. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of the biomolecules. Furthermore, by constructing a boost potential that follows a Gaussian distribution, accurate reweighting of GaMD simulations is achieved via cumulant expansion to the second order. The free energy profiles obtained from GaMD simulations allow us to identify distinct low energy states of the biomolecules and characterize biomolecular structural dynamics quantitatively. In this chapter, we present the theory of GaMD, its implementation in the widely used molecular dynamics software packages (AMBER and NAMD), and applications to the alanine dipeptide biomolecular model system, protein folding, biomolecular large-scale conformational transitions and biomolecular recognition.

Understanding the molecular basis of agonist/antagonist mechanism of human mu opioid receptor through gaussian accelerated molecular dynamics method

The most powerful analgesic and addictive properties of opiate alkaloids are mediated by the μ opioid receptor (MOR). The MOR has been extensively investigated as a drug target in the twentieth century, with numerous compounds of varying efficacy being identified. We employed molecular dynamics and Gaussian accelerated molecular dynamics techniques to identify the binding mechanisms of MORs to BU72 (agonist) and β-funaltrexamine (antagonist). Our approach theoretically suggests that the 34 residues (Lys209–Phe221 and Ile301–Cys321) of the MORs were the key regions enabling the two compounds to bind to the active site of the MORs. When the MORs were in the holo form, the key region was in the open conformation. When the MORs were in the apo form, the key region was in the closed conformation. The key region might be responsible for the selectivity of new MOR agonists and antagonists.

Non-active site mutations disturb the loop dynamics, dimerization, viral budding and egress of VP40 of the Ebola virus

The first account of the dynamic features of the loop region of VP40 of the Ebola virus (EboV) using accelerated molecular dynamics (aMD) simulations is reported herein. Due to its major role in the Ebola life cycle, VP40 is considered a promising therapeutic target. The available experimental data on the N-terminal domain (NTD) loop indicates that mutations K127A, T129A and N130A demonstrate an unrecognized role for NTD-plasma membrane (PM) interaction for efficient VP40-PM localization, oligomerization, matrix assembly and egress. Despite experimental results, the molecular description of VP40 and the information it can provide still remain vague. Therefore, to gain further molecular insight into the effect of mutations on the loop region of VP40 and its effects on the overall protein conformation and VP40 dimerization, aMD simulations and post-dynamic analyses were employed for wildtype (WT) and mutant systems. The results showed significant variations in the presence of mutations as per RMSF, RMSD, R_g, PCA and distance calculations in comparison to the WT. These results could provide researchers with insight with regards to the conformational aspects concerning VP40 and its close relation to the experimental data. We believe that the results presented in this study will ultimately provide a useful understanding of the structural landscape of the loop region of VP40, which would contribute towards the discovery of novel EboV inhibitors.

Drug Binding Poses Relate Structure with Efficacy in the μ Opioid Receptor

The μ-opioid receptor (MOPr) is a clinically important G protein-coupled receptor that couples to Gi/o proteins and arrestins. At present, the receptor conformational changes that occur following agonist binding and activation are poorly understood. This study has employed molecular dynamics simulations to investigate the binding mode and receptor conformational changes induced by structurally similar opioid ligands of widely differing intrinsic agonist efficacy, norbuprenorphine, buprenorphine, and diprenorphine. Bioluminescence resonance energy transfer assays for Gi activation and arrestin-3 recruitment in human embryonic kidney 293 cells confirmed that norbuprenorphine is a high efficacy agonist, buprenorphine a low efficacy agonist, and diprenorphine an antagonist at the MOPr. Molecular dynamics simulations revealed that these ligands adopt distinct binding poses and engage different subsets of residues, despite sharing a common morphinan scaffold. Notably, norbuprenorphine interacted with sodium ion-coordinating residues W2936.48 and N1503.35, whilst buprenorphine and diprenorphine did not. Principal component analysis of the movements of the receptor transmembrane domains showed that the buprenorphine-bound receptor occupied a distinct set of conformations to the norbuprenorphine-bound receptor. Addition of an allosteric sodium ion caused the receptor and ligand to adopt an inactive conformation. The differences in ligand-residue interactions and receptor conformations observed here may underlie the differing efficacies for cellular signalling outputs for these ligands.

An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands

A generally applicable metadynamics scheme for predicting the free energy profile of ligand binding to G-protein-coupled receptors (GPCRs) is described. A common and effective collective variable (CV) has been defined using the ideally placed and highly conserved Trp6.48 as a reference point for ligand-GPCR distance measurement and the common orientation of GPCRs in the cell membrane. Using this single CV together with well-tempered multiple-walker metadynamics with a funnel-like boundary allows an efficient exploration of the entire ligand binding path from the extracellular medium to the orthosteric binding site, including vestibule and intermediate sites. The protocol can be used with X-ray structures or high-quality homology models (based on a high-quality template and after thorough refinement) for the receptor and is universally applicable to agonists, antagonists, and partial and reverse agonists. The root-mean-square error (RMSE) in predicted binding free energies for 12 diverse ligands in five receptors (a total of 23 data points) is surprisingly small (less than 1 kcal mol^-1). The RMSEs for simulations that use receptor X-ray structures and homology models are very similar.

Kinetics for Drug Discovery: an industry-driven effort to target drug residence time

A considerable number of approved drugs show non-equilibrium binding characteristics, emphasizing the potential role of drug residence times for in vivo efficacy. Therefore, a detailed understanding of the kinetics of association and dissociation of a target-ligand complex might provide crucial insight into the molecular mechanism-of-action of a compound. This deeper understanding will help to improve decision making in drug discovery, thus leading to a better selection of interesting compounds to be profiled further. In this review, we highlight the contributions of the Kinetics for Drug Discovery (K4DD) Consortium, which targets major open questions related to binding kinetics in an industry-driven public-private partnership.

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.

Coarse-Grained Prediction of Peptide Binding to G-Protein Coupled Receptors

In this study, we used the Martini Coarse-Grained model with no applied restraints to predict the binding mode of some peptides to G-Protein Coupled Receptors (GPCRs). Both the Neurotensin-1 and the chemokine CXCR4 receptors were used as test cases. Their ligands, NTS_8-13 and CVX15 peptides, respectively, were initially positioned in the surrounding water box. Using a protocol based on Replica Exchange Molecular Dynamics (REMD), both opening of the receptors and entry of the peptides into their dedicated pockets were observed on the μs time-scale. After clustering, the most statistically representative orientations were closely related to the X-ray structures of reference, sharing both RMSD lower than 3 Å and most of the native contacts. These results demonstrate that such a model, that does not require access to tremendous computational facilities, can be helpful in predicting peptide binding to GPCRs as well as some of the receptor's conformational changes required for this key step. We also discuss how such an approach can now help to predict, de novo, the interactions of GPCRs with other intra- or extracellular peptide/protein partners.

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.

VP40 of the Ebola Virus as a Target for EboV Therapy: Comprehensive Conformational and Inhibitor Binding Landscape from Accelerated Molecular Dynamics

The first account of the dynamic features of the loop region of VP40 of the Ebola virus was studied using accelerated molecular dynamics simulations and reported herein. Among the proteins of the Ebola virus, the matrix protein (VP40) plays a significant role in the virus lifecycle thereby making it a promising therapeutic target. Of interest is the newly elucidated N-terminal domain loop region of VP40 comprising residues K127, T129, and N130 which when mutated to alanine have demonstrated an unrecognized role for N-terminal domain-plasma membrane interaction for efficient VP40-plasma membrane localization, oligomerization, matrix assembly, and egress. The molecular understanding of the conformational features of VP40 in complex with a known inhibitor still remains elusive. Using accelerated molecular dynamics approaches, we conducted a comparative study on VP40 apo and bound systems to understand the conformational features of VP40 at the molecular level and to determine the effect of inhibitor binding with the aid of a number of post-dynamic analytical tools. Significant features were seen in the presence of an inhibitor as per molecular mechanics/generalized born surface area binding free energy calculations. Results revealed that inhibitor binding to VP40 reduces the flexibility and mobility of the protein as supported by root mean square fluctuation and root mean square deviation calculations. The study revealed a characteristic "twisting" motion and coiling of the loop region of VP40 accompanied by conformational changes in the dimer interface upon inhibitor binding. We believe that results presented in this study will ultimately provide useful insight into the binding landscape of VP40 which could assist researchers in the discovery of potent Ebola virus inhibitors for anti-Ebola therapies.

Gaussian Accelerated Molecular Dynamics in NAMD

Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that provides efficient free energy calculations of biomolecules. Like the previous accelerated molecular dynamics (aMD), GaMD allows for “unconstrained” enhanced sampling without the need to set predefined collective variables and so is useful for studying complex biomolecular conformational changes such as protein folding and ligand binding. Furthermore, because the boost potential is constructed using a harmonic function that follows Gaussian distribution in GaMD, cumulant expansion to the second order can be applied to recover the original free energy profiles of proteins and other large biomolecules, which solves a long-standing energetic reweighting problem of the previous aMD method. Taken together, GaMD offers major advantages for both unconstrained enhanced sampling and free energy calculations of large biomolecules. Here, we have implemented GaMD in the NAMD package on top of the existing aMD feature and validated it on three model systems: alanine dipeptide, the chignolin fast-folding protein, and the M₃ muscarinic G protein-coupled receptor (GPCR). For alanine dipeptide, while conventional molecular dynamics (cMD) simulations performed for 30 ns are poorly converged, GaMD simulations of the same length yield free energy profiles that agree quantitatively with those of 1000 ns cMD simulation. Further GaMD simulations have captured folding of the chignolin and binding of the acetylcholine (ACh) endogenous agonist to the M₃ muscarinic receptor. The reweighted free energy profiles are used to characterize the protein folding and ligand binding pathways quantitatively. GaMD implemented in the scalable NAMD is widely applicable to enhanced sampling and free energy calculations of large biomolecules.

Graded activation and free energy landscapes of a muscarinic G-protein-coupled receptor

G-protein-coupled receptors (GPCRs) recognize ligands of widely different efficacies, from inverse to partial and full agonists, which transduce cellular signals at differentiated levels. However, the mechanism of such graded activation remains unclear. Using the Gaussian accelerated molecular dynamics (GaMD) method that enables both unconstrained enhanced sampling and free energy calculation, we have performed extensive GaMD simulations (∼19 μs in total) to investigate structural dynamics of the M₂ muscarinic GPCR that is bound by the full agonist iperoxo (IXO), the partial agonist arecoline (ARC), and the inverse agonist 3-quinuclidinyl-benzilate (QNB), in the presence or absence of the G-protein mimetic nanobody. In the receptor-nanobody complex, IXO binding leads to higher fluctuations in the protein-coupling interface than ARC, especially in the receptor transmembrane helix 5 (TM5), TM6, and TM7 intracellular domains that are essential elements for GPCR activation, but less flexibility in the receptor extracellular region due to stronger binding compared with ARC. Two different binding poses are revealed for ARC in the orthosteric pocket. Removal of the nanobody leads to GPCR deactivation that is characterized by inward movement of the TM6 intracellular end. Distinct low-energy intermediate conformational states are identified for the IXO- and ARC-bound M₂ receptor. Both dissociation and binding of an orthosteric ligand are observed in a single all-atom GPCR simulation in the case of partial agonist ARC binding to the M₂ receptor. This study demonstrates the applicability of GaMD for exploring free energy landscapes of large biomolecules and the simulations provide important insights into the GPCR functional mechanism.