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Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules.

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

To maintain the lipid asymmetry of the cell envelope in Gram-negative bacteria, the MlaC protein serves as a lipid transfer factor and delivers phospholipids from the outer to the inner membrane. A strategy of antibiotic discovery is to design a proper compound that can tightly bind to the MlaC protein and inhibit the MlaC function. In this study, we performed virtual screening on multiple MlaC structures obtained from molecular dynamics simulations to identify potential MlaC binders. Our results suggested that clorobiocin is a compound that could bind to the MlaC protein. Through the comparison of the bound geometry between clorobiocin and novobiocin, we pointed out that the methyl-pyrrole group of the noviose sugar in clorobiocin forms hydrophobic interactions with amino acids in the phospholipid binding pocket, which allows the compound to bind deep in the active site. This also explains why clorobiocin shows a tighter binding affinity than novobiocin. Our study highlights a practical path of antibiotic development against Gram-negative bacteria.

CRISPR-Cas9 is the state-of-the-art technology for editing and manipulating nucleic acids. However, the occurrence of off-target mutations can limit its applicability. Here, all-atom enhanced molecular dynamics (MD) simulations – using Gaussian accelerated MD (GaMD) – are used to decipher the mechanism of off-target binding at the molecular level. GaMD reveals that base pair mismatches in the target DNA at specific distal sites with respect to the Protospacer Adjacent Motif (PAM) induce an extended opening of the RNA:DNA heteroduplex, which leads to newly discovered interactions between the unwound nucleic acids and the protein counterpart. The conserved interactions between the target DNA strand and the L2 loop of the catalytic HNH domain constitute a “lock” effectively decreasing the conformational freedom of the HNH domain and its activation for cleavage. Remarkably, depending on their position at PAM distal sites, DNA mismatches leading to off-target cleavages are unable to “lock” the HNH domain, thereby identifying the ability to “lock” HNH as a key determinant. Consistently, off-target sequences hampering the catalysis have been shown to “trap” somehow the HNH domain in an inactive “conformational checkpoint” state (Dagdas et al. Sci Adv, 2017). As such, this mechanism identifies the molecular basis underlying off-target cleavages and contributes in clarifying a long-lasting open issue of the CRISPR-Cas9 function. It also poses the foundation for designing novel and more specific Cas9 variants, which could be obtained by magnifying the “locking” interactions between HNH and the target DNA in the presence of any incorrect off-target sequence, thus preventing undesired cleavages.

Fungal pathogens kill more people per year globally than malaria or tuberculosis and threaten international food security through crop destruction. New sophisticated strategies to inhibit fungal growth are thus urgently needed. Among the potential candidate molecules that strongly inhibit fungal spore germination are small cationic, cysteine-stabilized proteins of the AFP family secreted by a group of filamentous Ascomycetes. Its founding member, AFP from Aspergillus giganteus, is of particular interest since it selectively inhibits the growth of filamentous fungi without affecting the viability of mammalian, plant, or bacterial cells. AFPs are also characterized by their high efficacy and stability. Thus, AFP can serve as a lead compound for the development of novel antifungals. Notably, all members of the AFP family comprise a γ-core motif which is conserved in all antimicrobial proteins from pro- and eukaryotes and known to interfere with the integrity of cytoplasmic plasma membranes. In this study, we used classical molecular dynamics simulations combined with wet laboratory experiments and nuclear magnetic resonance (NMR) spectroscopy to characterize the structure and dynamical behavior of AFP isomers in solution and their interaction with fungal model membranes. We demonstrate that the γ-core motif of structurally conserved AFP is the key for its membrane interaction, thus verifying for the first time that the conserved γ-core motif of antimicrobial proteins is directly involved in protein-membrane interactions. Furthermore, molecular dynamic simulations suggested that AFP does not destroy the fungal membrane by pore formation but covers its surface in a well-defined manner, using a multistep mechanism to destroy the membranes integrity.IMPORTANCE Fungal pathogens pose a serious danger to human welfare since they kill more people per year than malaria or tuberculosis and are responsible for crop losses worldwide. The treatment of fungal infections is becoming more complicated as fungi develop resistances against commonly used fungicides. Therefore, discovery and development of novel antifungal agents are of utmost importance.

Increases in CO2 concentration in plant leaves due to respiration in the dark and the continuing atmospheric [CO2] rise cause closing of stomatal pores, thus affecting plant-water relations globally. However, the underlying CO2/bicarbonate (CO2/HCO3-) sensing mechanisms remain unknown. [CO2] elevation in leaves triggers stomatal closure by anion efflux mediated via the SLAC1 anion channel localized in the plasma membrane of guard cells. Previous reconstitution analysis has suggested that intracellular bicarbonate ions might directly up-regulate SLAC1 channel activity. However, whether such a CO2/HCO3- regulation of SLAC1 is relevant for CO2 control of stomatal movements in planta remains unknown. Here, we computationally probe for candidate bicarbonate-interacting sites within the SLAC1 anion channel via long-timescale Gaussian accelerated molecular dynamics (GaMD) simulations. Mutations of two putative bicarbonate-interacting residues, R256 and R321, impaired the enhancement of the SLAC1 anion channel activity by CO2/HCO3- in Xenopus oocytes. Mutations of the neighboring charged amino acid K255 and residue R432 and the predicted gate residue F450 did not affect HCO3- regulation of SLAC1. Notably, gas-exchange experiments with slac1-transformed plants expressing mutated SLAC1 proteins revealed that the SLAC1 residue R256 is required for CO2 regulation of stomatal movements in planta, but not for abscisic acid (ABA)-induced stomatal closing. Patch clamp analyses of guard cells show that activation of S-type anion channels by CO2/HCO3-, but not by ABA, was impaired, indicating the relevance of R256 for CO2 signal transduction. Together, these analyses suggest that the SLAC1 anion channel is one of the physiologically relevant CO2/HCO3- sensors in guard cells.

Abstract CRISPR-Cas9 is the state-of-the-art technology for editing and manipulating nucleic acids. However, the occurrence of off-target mutations can limit its applicability. Here, all-atom enhanced molecular dynamics (MD) simulations – using Gaussian accelerated MD (GaMD) – are used to decipher the mechanism of off-target binding at the molecular level. GaMD reveals that base pair mismatches in the target DNA at specific distal sites with respect to the Protospacer Adjacent Motif (PAM) induce an extended opening of the RNA:DNA heteroduplex, which leads to newly discovered interactions between the unwound nucleic acids and the protein counterpart. The conserved interactions between the target DNA strand and the L2 loop of the catalytic HNH domain constitute a “lock” effectively decreasing the conformational freedom of the HNH domain and its activation for cleavage. Remarkably, depending on their position at PAM distal sites, DNA mismatches leading to off-target cleavages are unable to “lock” the HNH domain, thereby identifying the ability to “lock” HNH as a key determinant. Consistently, off-target sequences hampering the catalysis have been shown to “trap” somehow the HNH domain in an inactive “conformational checkpoint” state (Dagdas et al. Sci Adv, 2017). As such, this mechanism identifies the molecular basis underlying off-target cleavages and contributes in clarifying a long-lasting open issue of the CRISPR-Cas9 function. It also poses the foundation for designing novel and more specific Cas9 variants, which could be obtained by magnifying the “locking” interactions between HNH and the target DNA in the presence of any incorrect off-target sequence, thus preventing undesired cleavages.

Ensemble docking corresponds to the generation of an "ensemble" of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.

Through adding a harmonic boost potential to smooth the system potential energy surface, Gaussian accelerated molecular dynamics (GaMD) provides enhanced sampling and free energy calculation of biomolecules without the need of predefined reaction coordinates. This work continues to improve the acceleration power and energy reweighting of the GaMD by combining the GaMD with replica exchange algorithms. Two versions of replica exchange GaMD (rex-GaMD) are presented: force constant rex-GaMD and threshold energy rex-GaMD. During simulations of force constant rex-GaMD, the boost potential can be exchanged between replicas of different harmonic force constants with fixed threshold energy. However, the algorithm of threshold energy rex-GaMD tends to switch the threshold energy between lower and upper bounds for generating different levels of boost potential. Testing simulations on three model systems, including the alanine dipeptide, chignolin, and HIV protease, demonstrate that through continuous exchanges of the boost potential, the rex-GaMD simulations not only enhance the conformational transitions of the systems but also narrow down the distribution width of the applied boost potential for accurate energetic reweighting to recover biomolecular free energy profiles.

Protein-protein binding is key in cellular signaling processes. Molecular dynamics (MD) simulations of protein-protein binding, however, are challenging due to limited timescales. In particular, binding of the medically important G-protein-coupled receptors (GPCRs) with intracellular signaling proteins has not been simulated with MD to date. Here, we report a successful simulation of the binding of a G-protein mimetic nanobody to the M2 muscarinic GPCR using the robust Gaussian accelerated MD (GaMD) method. Through long-timescale GaMD simulations over 4,500 ns, the nanobody was observed to bind the receptor intracellular G-protein-coupling site, with a minimum rmsd of 2.48 Å in the nanobody core domain compared with the X-ray structure. Binding of the nanobody allosterically closed the orthosteric ligand-binding pocket, being consistent with the recent experimental finding. In the absence of nanobody binding, the receptor orthosteric pocket sampled open and fully open conformations. The GaMD simulations revealed two low-energy intermediate states during nanobody binding to the M2 receptor. The flexible receptor intracellular loops contribute remarkable electrostatic, polar, and hydrophobic residue interactions in recognition and binding of the nanobody. These simulations provided important insights into the mechanism of GPCR-nanobody binding and demonstrated the applicability of GaMD in modeling dynamic protein-protein interactions.

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.

The A2A adenosine receptor (A2A AR) is a G protein-coupled receptor that is pharmacologically targeted for the treatment of inflammation, sepsis, cancer, neurodegeneration, and Parkinson's disease. Recently, we applied long-timescale molecular dynamics simulations on two ligand-free receptor conformations, starting from the agonist-bound (PDB ID: 3QAK) and antagonist-bound (PDB ID: 3EML) X-ray structures. This analysis revealed four distinct conformers of the A2A AR: the active, intermediate 1, intermediate 2, and inactive. In this study, we apply the fragment-based mapping algorithm, FTMap, on these receptor conformations to uncover five non-orthosteric sites on the A2A AR. Two sites that are identified in the active conformation are located in the intracellular region of the transmembrane helices (TM) 3/TM4 and the G protein-binding site in the intracellular region between TM2/TM3/TM6/TM7. Three sites are identified in the intermediate 1 and intermediate 2 conformations, annexing a site in the lipid interface of TM5/TM6. Five sites are identified in the inactive conformation, comprising a site in the intracellular region of TM1/TM7 and in the extracellular region of TM3/TM4 of the A2A AR. We postulate that these sites on the A2A AR be screened for allosteric modulators for the treatment of inflammatory and neurological diseases.

CRISPR-Cas9 has become a facile genome editing technology, yet the structural and mechanistic features underlying its function are unclear. Here, we perform extensive molecular simulations in an enhanced sampling regime, using a Gaussian-accelerated molecular dynamics (GaMD) methodology, which probes displacements over hundreds of microseconds to milliseconds, to reveal the conformational dynamics of the endonuclease Cas9 during its activation toward catalysis. We disclose the conformational transition of Cas9 from its apo form to the RNA-bound form, suggesting a mechanism for RNA recruitment in which the domain relocations cause the formation of a positively charged cavity for nucleic acid binding. GaMD also reveals the conformation of a catalytically competent Cas9, which is prone for catalysis and whose experimental characterization is still limited. We show that, upon DNA binding, the conformational dynamics of the HNH domain triggers the formation of the active state, explaining how the HNH domain exerts a conformational control domain over DNA cleavage [Sternberg SH et al. (2015) Nature, 527, 110-113]. These results provide atomic-level information on the molecular mechanism of CRISPR-Cas9 that will inspire future experimental investigations aimed at fully clarifying the biophysics of this unique genome editing machinery and at developing new tools for nucleic acid manipulation based on CRISPR-Cas9.

Activation of the first sphingosine-1-phosphate receptor (S1PR1 ) promotes permeability of the blood brain barrier, astrocyte and neuronal protection, and lymphocyte egress from secondary lymphoid tissues. Although an agonist often activates the S1PR1 , the receptor exhibits high levels of basal activity. In this study, we performed long-timescale molecular dynamics and accelerated molecular dynamics (aMD) simulations to investigate activation mechanisms of the ligand-free (apo) S1PR1 . In the aMD enhanced sampling simulations, we observed four independent events of activation, which is characterized by close interaction between Y3117.53 and Y2215.58 and increased distance between the intracellular ends of transmembrane (TM) helices 3 and 6. Although TM helices TM3, TM6, TM5 and, TM7 are associated with GPCR activation, we discovered that their movements are not necessarily correlated during activation. Instead, TM5 showed a decreased correlation with each of these regions during activation. During activation of the apo receptor, Y2215.58 and Y3117.53 became more solvated, because a water channel formed in the intracellular pocket. Additionally, a lipid molecule repeatedly entered the receptor between the extracellular ends of TM1 and TM7, providing important insights into the pathway of ligand entry into the S1PR1 .

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.

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.

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.

G-protein coupled receptors (GPCRs), the largest family of human membrane proteins, mediate cellular signaling and represent primary targets of about one third of currently marketed drugs. GPCRs undergo highly dynamic structural transitions during signal transduction, from binding of extracellular ligands to coupling with intracellular effector proteins. Molecular dynamics (MD) simulations have been utilized to investigate GPCR signaling mechanisms (such as pathways of ligand binding and receptor activation/deactivation) and to design novel small-molecule drug candidates. Future research directions point towards modeling cooperative binding of multiple orthosteric and allosteric ligands to GPCRs, GPCR oligomerization and interactions of GPCRs with different intracellular signaling proteins. Through methodological and supercomputing advances, MD simulations will continue to provide important insights into GPCR signaling mechanisms and further facilitate structure-based drug design.

The CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 system recently emerged as a transformative genome-editing technology that is innovating basic bioscience and applied medicine and biotechnology. The endonuclease Cas9 associates with a guide RNA to match and cleave complementary sequences in double stranded DNA, forming an RNA:DNA hybrid and a displaced non-target DNA strand. Although extensive structural studies are ongoing, the conformational dynamics of Cas9 and its interplay with the nucleic acids during association and DNA cleavage are largely unclear. Here, by employing multi-microsecond time scale molecular dynamics, we reveal the conformational plasticity of Cas9 and identify key determinants that allow its large-scale conformational changes during nucleic acid binding and processing. We show how the "closure" of the protein, which accompanies nucleic acid binding, fundamentally relies on highly coupled and specific motions of the protein domains, collectively initiating the prominent conformational changes needed for nucleic acid association. We further reveal a key role of the non-target DNA during the process of activation of the nuclease HNH domain, showing how the nontarget DNA positioning triggers local conformational changes that favor the formation of a catalytically competent Cas9. Finally, a remarkable conformational plasticity is identified as an intrinsic property of the HNH domain, constituting a necessary element that allows for the HNH repositioning. These novel findings constitute a reference for future experimental studies aimed at a full characterization of the dynamic features of the CRISPR-Cas9 system, and-more importantly-call for novel structure engineering efforts that are of fundamental importance for the rational design of new genome-engineering applications.

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.

Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [(3)H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.

Free energy calculations are central to understanding the structure, dynamics and function of biomolecules. Yet insufficient sampling of biomolecular configurations is often regarded as one of the main sources of error. Many enhanced sampling techniques have been developed to address this issue. Notably, enhanced sampling methods based on biasing collective variables (CVs), including the widely used umbrella sampling, adaptive biasing force and metadynamics, have been discussed in a recent excellent review (Abrams and Bussi, Entropy, 2014). Here, we aim to review enhanced sampling methods that do not require predefined system-dependent CVs for biomolecular simulations and as such do not suffer from the hidden energy barrier problem as encountered in the CV-biasing methods. These methods include, but are not limited to, replica exchange/parallel tempering, self-guided molecular/Langevin dynamics, essential energy space random walk and accelerated molecular dynamics. While it is overwhelming to describe all details of each method, we provide a summary of the methods along with the applications and offer our perspectives. We conclude with challenges and prospects of the unconstrained enhanced sampling methods for accurate biomolecular free energy calculations.

The composition of the outer membrane in Gram-negative bacteria is asymmetric, with the lipopolysaccharides found in the outer leaflet and phospholipids in the inner leaflet. The MlaC protein transfers phospholipids from the outer to inner membrane to maintain such lipid asymmetry in the Mla pathway. In this work, we have performed molecular dynamics simulations on apo and phospholipid-bound systems to study the dynamical properties of MlaC. Our simulations show that the phospholipid forms hydrophobic interactions with the protein. Residues surrounding the entrance of the binding site exhibit correlated motions to control the site opening and closing. Lipid binding leads to increase of the binding pocket volume and precludes entry of the water molecules. However, in the absence of the phospholipid, water molecules can freely move in and out of the binding site when the pocket is open. Dehydration occurs when the pocket closes. This study provides dynamic information of the MlaC protein and may facilitate the design of antibiotics against the Mla pathway of Gram-negative bacteria.