Analysis by metadynamics simulation of binding pathway of influenza virus M2 channel blockers

Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.

The potential of some functional group compounds substituted 8-Manzamine A as RSK1 inhibitors: molecular docking and molecular dynamics simulations

Cancer, an incurable global disease, demands urgent anti-cancer drug development. Marine alkaloids like Manzamine and its derivatives show promise as RSK inhibitors against cancer cell invasion. Replacing the hydrogen at the 8-position of Manzamine A with a hydroxyl group has been shown to significantly enhance its biological activity. In this article, we designed various functional group compounds (A1-A21) substituted 8-Manzamine A by docking, MM-GBSA, molecular dynamics (MD) simulation, and well-tempered metadynamics (WT-MetaD) simulations to evaluate their potential as RSK1 inhibitors. Ligands A1-A21 were docked in the RSK1 N-terminal kinase domain (PDB ID: 2Z7Q) using the Glide module. The calculation of binding energy was performed using Prime MM-GB/SA, while MD simulations were conducted with the Desmond module of Schrodinger suite 2023. Compound A5 exhibits the highest G-score (-7.01) compared to 8-Hydroxymanzamine A (-6.08). Additionally, compounds A6, A10, A12, A17, A11, A4, and A13 demonstrate increased activity against RSK1 when compared to both 8-Hydroxymanzamine A and Manzamine A. Residues LEU68, VAL76, LEU141, PHE143, LEU144, PHE150, ASP148, GLU191, and LEU194 of RSK1 protein play a key role in binding with ligands. An MD simulation of Compound A5 was carried out to explore the dynamic interactions within the protein-ligand complex. Furthermore, WT-MetaD simulations validated the docking study results and identified the most energetically favored conformations for the A5/RSK1 complex. Ligands A5, A6, A10, A12, A17, A11, A4, and A13, featuring diverse functional groups and good Glide scores, may have the potential for significant RSK1 activity and merit further development.Communicated by Ramaswamy H. Sarma.

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.

Structural biology of coronavirus ion channels

Viral infection compromises specific organelles of the cell and readdresses its functional resources to satisfy the needs of the invading body. Around 70% of the coronavirus positive-sense single-stranded RNA encodes proteins involved in replication, and these viruses essentially take over the biosynthetic and transport mechanisms to ensure the efficient replication of their genome and trafficking of their virions. Some coronaviruses encode genes for ion-channel proteins - the envelope protein E (orf4a), orf3a and orf8 - which they successfully employ to take control of the endoplasmic reticulum-Golgi complex intermediate compartment or ERGIC. The E protein, which is one of the four structural proteins of SARS-CoV-2 and other coronaviruses, assembles its transmembrane protomers into homopentameric channels with mild cationic selectivity. Orf3a forms homodimers and homotetramers. Both carry a PDZ-binding domain, lending them the versatility to interact with more than 400 target proteins in infected host cells. Orf8 is a very short 29-amino-acid single-passage transmembrane peptide that forms cation-selective channels when assembled in lipid bilayers. This review addresses the contribution of biophysical and structural biology approaches that unravel different facets of coronavirus ion channels, their effects on the cellular machinery of infected cells and some structure-functional correlations with ion channels of higher organisms.

Integrating molecular modelling methods to advance influenza A virus drug discovery

Since the discovery of the anti-influenza drugs oseltamivir and zanamivir using computer-aided drug design methods, there have been significant applications of molecular modelling methodologies applied to influenza A virus drug discovery, such as molecular dynamics (MD) simulation, molecular docking, and virtual screening (VS). In this review, we provide a brief general introduction to molecular modelling in the context of drug discovery and then focus on the advances and impact of integrating these methods with specific reference to potential influenza A antiviral drug targets.

Conventional Molecular Dynamics and Metadynamics Simulation Studies of the Binding and Unbinding Mechanism of TTR Stabilizers AG10 and Tafamidis

Amyloid transthyretin (ATTR) amyloidosis is a widespread and fatal systemic amyloidosis characterized by the misfolding and amyloid aggregation of transthyretin (TTR). Studies suggest that dissociation of the TTR tetramer is the key step for its misfolding. Because of the importance of tetramer dissociation on ATTR amyloidosis, many TTR stabilizers have been discovered to stabilize the tetramer structure. This paper describes the application conventional molecular dynamics and metadynamics simulations to investigate the binding and unbinding mechanisms of two TTR stabilizers, including AG10 and tafamidis. AG10 has been granted an orphan drug designation by the U.S. Food and Drug Administration (FDA), and tafamidis was the first FDA-approved treatment for ATTR cardiomyopathy. The conventional molecular dynamics simulations reveal that both AG10 and tafamidis can stabilize the TTR tetramer through different mechanisms. AG10 stabilizes TTR tetramer by forming H-bonds with S117 to mimic the protective effect of T119M. Tafamidis stabilizes the tetramer by forming H-bond with S52 in the flexible CD loop to increase its structural stability. Despite the strong binding affinity of tafamidis, the free-energy surface constructed from metadynamics simulation suggests that tafamidis unbinds more readily than AG10 with lower free-energy barriers between the binding state and other intermediates. Finally, by performing pharmacophore analysis, we found two common important moieties of the studied compounds for their binding on the pockets, which can provide valuable guidance for future lead compounds' optimization in designing drugs for ATTR amyloidosis.

An effective molecular blocker of ion channel of M2 protein as anti-influenza a drug

Design of a drug compound that can effectively bind to the M2 ion channel and block the diffusion of hydrogen ions (H⁺) through and inhibit influenza A virus replication is an important task. Known anti-influenza drugs amantadine and rimantadine have a weak effect on influenza A virus. A new class of positively charged, +2 e.u., molecules is proposed here to block diffusion of H⁺ ion through the M2 channel. Several drug candidates, derivatives of a lead compound (diazabicyclooctane), were proposed and investigated. Molecular dynamics of thermal fluctuations of M2 protein structure and ionization-conformation coupling of all the ionizable residues were simulated at physiological pH. The influence of the most probable mutations of key drug-binding amino acid residues in the M2 ion channel were investigated too. It is shown that the suggested new blocker has high binding affinity for the M2 channel. There are two in-channel binding sites of high affinity, the first one has H-bonds with two of four serine residues Ser-31A (B) or Ser-31C(D), and the second one has H-bonds with two of four histidine residues His-37A (B), or His-37C(D). The main advantage of the new drug molecule is the positive charge, +2 e.u., which creates a positive electrostatic potential barrier (in addition to a steric one) for a transfer of H⁺ ion through M2 channel and may serve as an effective anti-influenza A virus drug.Communicated by Ramaswamy H. Sarma.

Ligand-Binding Calculations with Metadynamics

All-atom molecular dynamics simulations can capture the dynamic degrees of freedom that characterize molecular recognition, the knowledge of which constitutes the cornerstone of rational approaches to drug design and optimization. In particular, enhanced sampling algorithms, such as metadynamics, are powerful tools to dramatically reduce the computational cost required for a mechanistic description of the binding process. Here, we describe the essential details characterizing these simulation strategies, focusing on the critical step of identifying suitable reaction coordinates, as well as on the different analysis algorithms to estimate binding affinity and residence times. We conclude with a survey of published applications that provides explicit examples of successful simulations for several targets.