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Diffusion is often the rate-determining step in many biological processes. Currently, the two main computational methods for studying diffusion are stochastic methods, such as Brownian dynamics, and continuum methods, such as the finite element method. A previous study introduced a new hybrid diffusion method that couples the strengths of each of these two methods, but was limited by the lack of interactions among the particles; the force on each particle had to be from an external field. This study further develops the method to allow charged particles. The method is derived for a general multidimensional system and is presented using a basic test case for a one-dimensional linear system with one charged species and a radially symmetric system with three charged species.

Dihedral conformational transitions are analyzed systematically in a model globular protein, cytochrome P450cam, to examine their structural and chemical dependences through combined conventional molecular dynamics (cMD), accelerated molecular dynamics (aMD) and adaptive biasing force (ABF) simulations. The aMD simulations are performed at two acceleration levels, using dihedral and dual boost, respectively. In comparison with cMD, aMD samples protein dihedral transitions approximately two times faster on average using dihedral boost, and ∼ 3.5 times faster using dual boost. In the protein backbone, significantly higher dihedral transition rates are observed in the bend, coil, and turn flexible regions, followed by the β bridge and β sheet, and then the helices. Moreover, protein side chains of greater length exhibit higher transition rates on average in the aMD-enhanced sampling. Side chains of the same length (particularly Nχ = 2) exhibit decreasing transition rates with residues when going from hydrophobic to polar, then charged and aromatic chemical types. The reduction of dihedral transition rates is found to be correlated with increasing energy barriers as identified through ABF free energy calculations. These general trends of dihedral conformational transitions provide important insights into the hierarchical dynamics and complex free energy landscapes of functional proteins.

Elucidating the detailed process of ligand binding to a receptor is pharmaceutically important for identifying druggable binding sites. With the ability to provide atomistic detail, computational methods are well poised to study these processes. Here, accelerated molecular dynamics (aMD) is proposed to simulate processes of ligand binding to a G-protein-coupled receptor (GPCR), in this case the M3 muscarinic receptor, which is a target for treating many human diseases, including cancer, diabetes and obesity. Long-timescale aMD simulations were performed to observe the binding of three chemically diverse ligand molecules: antagonist tiotropium (TTP), partial agonist arecoline (ARc) and full agonist acetylcholine (ACh). In comparison with earlier microsecond-timescale conventional MD simulations, aMD greatly accelerated the binding of ACh to the receptor orthosteric ligand-binding site and the binding of TTP to an extracellular vestibule. Further aMD simulations also captured binding of ARc to the receptor orthosteric site. Additionally, all three ligands were observed to bind in the extracellular vestibule during their binding pathways, suggesting that it is a metastable binding site. This study demonstrates the applicability of aMD to protein-ligand binding, especially the drug recognition of GPCRs.

A Gaussian accelerated molecular dynamics (GaMD) approach for simultaneous enhanced sampling and free energy calculation of biomolecules is presented. By constructing a boost potential that follows Gaussian distribution, accurate reweighting of the GaMD simulations is achieved using cumulant expansion to the second order. Here, GaMD is demonstrated on three biomolecular model systems: alanine dipeptide, chignolin folding, and ligand binding to the T4-lysozyme. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of these biomolecules. Furthermore, the free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low-energy states of the biomolecules and characterize the protein-folding and ligand-binding pathways quantitatively.

Folding of four fast-folding proteins, including chignolin, Trp-cage, villin headpiece and WW domain, was simulated via accelerated molecular dynamics (aMD). In comparison with hundred-of-microsecond timescale conventional molecular dynamics (cMD) simulations performed on the Anton supercomputer, aMD captured complete folding of the four proteins in significantly shorter simulation time. The folded protein conformations were found within 0.2-2.1 Å of the native NMR or X-ray crystal structures. Free energy profiles calculated through improved reweighting of the aMD simulations using cumulant expansion to the second-order are in good agreement with those obtained from cMD simulations. This allows us to identify distinct conformational states (e.g., unfolded and intermediate) other than the native structure and the protein folding energy barriers. Detailed analysis of protein secondary structures and local key residue interactions provided important insights into the protein folding pathways. Furthermore, the selections of force fields and aMD simulation parameters are discussed in detail. Our work shows usefulness and accuracy of aMD in studying protein folding, providing basic references in using aMD in future protein-folding studies.

The activation/deactivation processes for G-protein coupled receptors (GPCRs) have been computationally studied for several different classes, including rhodopsin, the β2 adrenergic receptor, and the M2 muscarinic receptor. Despite determined cocrystal structures of the adenosine A2A receptor (A2A AR) in complex with antagonists, agonists and an antibody, the deactivation process of this GPCR is not completely understood. In this study, we investigate the convergence of two apo simulations, one starting with an agonist-bound conformation (PDB: 3QAK)(14) and the other starting with an antagonist-bound conformation (PDB: 3EML)(11) . Despite the two simulations not completely converging, we were able to identify distinct intermediate steps of the deactivation process characterized by the movement of Y288(7.53) in the NPxxY motif. We find that Y288(7.53) contributes to the process by forming hydrogen bonds to residues in transmembrane helices 2 and 7 and losing these interactions upon full deactivation. Y197(5.58) also plays a role in the process by forming a hydrogen bond only once the side chain moves from the lipid interface to the middle of the helical bundle.

G-protein-coupled receptors (GPCRs) are important membrane proteins that mediate cellular signaling and represent primary targets for about one-third of currently marketed drugs. Recent x-ray crystallographic studies identified distinct conformations of GPCRs in the active and inactive states. An allosteric sodium ion was found bound to a highly conserved D2.50 residue in inactive GPCRs, whereas the D2.50 allosteric pocket became collapsed in active GPCR structures. However, the dynamic mechanisms underlying these observations remain elusive. In this study, we aimed to understand the mechanistic effects of sodium ion binding on dynamic activation of the M3 muscarinic GPCR through long-timescale accelerated molecular dynamics (aMD) simulations. Results showed that with the D2.50 residue deprotonated, the M3 receptor is bound by an allosteric sodium ion and confined mostly in the inactive state with remarkably reduced flexibility. In contrast, the D2.50-protonated receptor does not exhibit sodium ion binding to the D2.50 allosteric site and samples a significantly larger conformational space. The receptor activation is captured and characterized by large-scale structural rearrangements of the transmembrane helices via dynamic hydrogen bond and salt bridge interactions. The residue motions are highly correlated during receptor activation. Further network analysis revealed that the allosteric signaling between residue D2.50 and key residues in the intracellular, extracellular, and orthosteric pockets is significantly weakened upon sodium ion binding.

Accelerated molecular dynamics (aMD) simulations greatly improve the efficiency of conventional molecular dynamics (cMD) for sampling biomolecular conformations, but they require proper reweighting for free energy calculation. In this work, we systematically compare the accuracy of different reweighting algorithms including the exponential average, Maclaurin series, and cumulant expansion on three model systems: alanine dipeptide, chignolin, and Trp-cage. Exponential average reweighting can recover the original free energy profiles easily only when the distribution of the boost potential is narrow (e.g., the range ≤20kBT) as found in dihedral-boost aMD simulation of alanine dipeptide. In dual-boost aMD simulations of the studied systems, exponential average generally leads to high energetic fluctuations, largely due to the fact that the Boltzmann reweighting factors are dominated by a very few high boost potential frames. In comparison, reweighting based on Maclaurin series expansion (equivalent to cumulant expansion on the first order) greatly suppresses the energetic noise but often gives incorrect energy minimum positions and significant errors at the energy barriers (∼2-3kBT). Finally, reweighting using cumulant expansion to the second order is able to recover the most accurate free energy profiles within statistical errors of ∼kBT, particularly when the distribution of the boost potential exhibits low anharmonicity (i.e., near-Gaussian distribution), and should be of wide applicability. A toolkit of Python scripts for aMD reweighting "PyReweighting" is distributed free of charge at http://mccammon.ucsd.edu/computing/amdReweighting/.

G-protein coupled receptors (GPCRs) mediate cellular responses to various hormones and neurotransmitters and are important targets for treating a wide spectrum of diseases. They are known to adopt multiple conformational states (e.g., inactive, intermediate and active) during their modulation of various cell signaling pathways. Here, the free energy landscape of GPCRs is explored using accelerated molecular dynamics (aMD) simulations as demonstrated on the M2 muscarinic receptor, a key GPCR that regulates human heart rate and contractile forces of cardiomyocytes. Free energy profiles of important structural motifs that undergo conformational transitions upon GPCR activation and allosteric signaling are analyzed in detail, including the Arg(3.50)-Glu(6.30) ionic lock, the Trp(6.48) toggle switch and the hydrogen interactions between Tyr(5.58)-Tyr(7.53).

G-protein-coupled receptors (GPCRs) are key cellular signaling proteins and have been targeted by approximately 30-40% of marketed drugs for treating many human diseases including cancer and heart failure. Recently, we directly observed activation of the M2 muscarinic receptor through long-timescale accelerated molecular dynamics (aMD) simulation, which revealed distinct inactive, intermediate and active conformers of the receptor. Here, FTMAP is applied to search for 'hot spots' in these activation-associated conformers using a library of 16 organic probe molecules that represent fragments of potential drugs. Seven allosteric (non-orthosteric) binding sites are identified in the M2 receptor through the FTMAP analysis. These sites are distributed in the solvent-exposed extracellular and intracellular mouth regions, as well as the lipid-exposed pockets formed by the transmembrane α helices TM3-TM4, TM5-TM6 and TM7-TM1/TM2. They serve as promising target sites for designing novel allosteric modulators as receptor-selective drugs.

The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19. Author summary: Our work explores the intricate process of ligand dissociation from the spike glycoprotein of SARS-CoV-2, the virus responsible for the ongoing COVID-19 pandemic. The spike glycoprotein plays a crucial role in the virus's ability to infect human cells by interacting with the ACE2 receptor. Our study investigates the molecular interactions involving the spike glycoprotein and linoleate, a small molecule with the potential to hinder viral entry by blocking ACE2 interaction. Using advanced computational simulations, we uncover the dynamic mechanisms governing linoleate dissociation from the spike protein, unveiling two distinct unbinding pathways. Our findings illuminate the structural changes of the spike glycoprotein during the dissociation process. Notably, we highlight the crucial roles of a gate formed by two α-helices and an N-linked glycan at position N343, which control the dissociation process and determine the unbinding pathway. These insights have significant implications for the development of novel antiviral therapies and improved COVID-19 vaccines. By unraveling the molecular details of this interaction, our work contributes to the global efforts aimed at combatting the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulation using enhanced sampling methods is one of the powerful computational tools used to explore protein conformations and free energy landscapes. Enhanced sampling methods often employ either an increase in temperature or a flattening of the potential energy surface to rapidly sample phase space, and a corresponding reweighting algorithm is used to recover the Boltzmann statistics. However, potential energies of complex biomolecules usually involve large fluctuations on a magnitude of hundreds of kcal/mol despite minimal structural changes during simulation. This leads to noisy reweighting statistics and complicates the obtainment of accurate final results. To overcome this common issue in enhanced conformational sampling, we propose a scaled molecular dynamics method, which modifies the biomolecular potential energy surface and employs a reweighting scheme based on configurational populations. Statistical mechanical theory is applied to derive the reweighting formula, and the canonical ensemble of simulated structures is recovered accordingly. Test simulations on alanine dipeptide and the fast folding polypeptide Chignolin exhibit sufficiently enhanced conformational sampling and accurate recovery of free energy surfaces and thermodynamic properties. The results are comparable to long conventional molecular dynamics simulations and exhibit better recovery of canonical statistics over methods which employ a potential energy term in reweighting.

Adhesion G protein-coupled receptors (ADGRGs) play critical roles in the reproductive, neurological, cardiovascular, and endocrine systems. In particular, ADGRG2 plays a significant role in Ewing sarcoma cell proliferation, parathyroid cell function, and male fertility. In 2022, a cryo-EM structure was reported for the active ADGRG2 bound by an optimized peptide agonist IP15 and the Gs protein. The IP15 peptide agonist was also modified to antagonists 4PH-E and 4PH-D with mutations of the 4PH residue to Glu and Asp, respectively. However, experimental structures of inactive antagonist-bound ADGRs remain to be resolved, and the activation mechanism of ADGRs such as ADGRG2 is poorly understood. Here, we applied Gaussian accelerated molecular dynamics (GaMD) simulations to probe conformational dynamics of the agonist- and antagonist-bound ADGRG2. By performing GaMD simulations, we were able to identify important low-energy conformations of ADGRG2 in the active, intermediate, and inactive states, as well as explore the binding conformations of each peptide. Moreover, our simulations revealed critical peptide-receptor residue interactions during the deactivation of ADGRG2. In conclusion, through GaMD simulations, we uncovered mechanistic insights into peptide (agonist and antagonist) binding and deactivation of the ADGRG2. These findings will potentially facilitate rational design of new peptide modulators of ADGRG2 and other ADGRs. [ABSTRACT FROM AUTHOR]

Peptides mediate up to 40% of known protein–protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method "Pep-GaMD," which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics. [ABSTRACT FROM AUTHOR]

α1-adrenergic receptors (α1-ARs) play critical roles in the cardiovascular and nervous systems where they regulate blood pressure, cognition, and metabolism. However, the lack of specific agonists for all α1 subtypes has limited our understanding of the physiological roles of different α1-AR subtypes, and led to the stagnancy in agonist-based drug development for these receptors. Here we report cryo-EM structures of α1A-AR in complex with heterotrimeric G-proteins and either the endogenous common agonist epinephrine or the α1A-AR-specific synthetic agonist A61603. These structures provide molecular insights into the mechanisms underlying the discrimination between α1A-AR and α1B-AR by A61603. Guided by the structures and corresponding molecular dynamics simulations, we engineer α1A-AR mutants that are not responsive to A61603, and α1B-AR mutants that can be potently activated by A61603. Together, these findings advance our understanding of the agonist specificity for α1-ARs at the molecular level, opening the possibility of rational design of subtype-specific agonists. α1-adrenergic receptors (α1- AR) play critical roles in the cardiovascular and nervous systems. Here, the authors report molecular insights into the mechanisms underlying the discrimination between α1A-AR and α1B-AR by the agonist A61603. [ABSTRACT FROM AUTHOR]