Structural and thermodynamic framework for PIEZO1 modulation by small molecules.

Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1's mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics-based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1's binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure-activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, µex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating µex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced µex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.

CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field.

Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM-GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre-equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF-based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 µs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. In addition, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. The capabilities and availability of Drude Prepper in CHARMM-GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.

Simple Synthesis of a Heterocyclophane Exhibiting Anti-c-Met Activity by Acting as a Hatch Blocking Access to the Active Site*.

A simple approach to the synthesis of heterocyclophane consisting of two 4,4'-bithiazoles has been developed in mild conditions. The heterocyclophane with two short chains was conveniently prepared by Hantzsch thiazoles synthesis using the reaction of 3-tert-butoxycarbonyl-3-azapentanethiocarboxamide with 1,4-dibromobutane-2,3-dione in methanol under reflux for only 15 min. Amino groups at the linkers of this heterocyclophane can be functionalized to give acylated and carbamate derivatives. Their properties as protein kinase inhibitors were investigated, and one of the heterocyclophanes exhibited specific anti-activity for c-mesenchymal epithelial transition factor (IC50 =603 nm), among seven types of protein kinases investigated. The computational site identification by ligand competitive saturation method was used to determine why the one heterocyclophane exhibited strong anti-activity for c-mesenchymal epithelial transition factor.

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach.

Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B22 and kD and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.

Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology.

Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.

CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates.

Characterizing glycans and glycoconjugates in the context of three-dimensional structures is important in understanding their biological roles and developing efficient therapeutic agents. Computational modeling and molecular simulation have become an essential tool complementary to experimental methods. Here, we present a computational tool, Glycan Modeler for in silico N-/O-glycosylation of the target protein and generation of carbohydrate-only systems. In our previous study, we developed Glycan Reader, a web-based tool for detecting carbohydrate molecules from a PDB structure and generation of simulation system and input files. As integrated into Glycan Reader in CHARMM-GUI, Glycan Modeler (Glycan Reader & Modeler) enables to generate the structures of glycans and glycoconjugates for given glycan sequences and glycosylation sites using PDB glycan template structures from Glycan Fragment Database (http://glycanstructure.org/fragment-db). Our benchmark tests demonstrate the universal applicability of Glycan Reader & Modeler to various glycan sequences and target proteins. We also investigated the structural properties of modeled glycan structures by running 2-µs molecular dynamics simulations of HIV envelope protein. The simulations show that the modeled glycan structures built by Glycan Reader & Modeler have the similar structural features compared to the ones solved by X-ray crystallography. We also describe the representative examples of glycoconjugate modeling with video demos to illustrate the practical applications of Glycan Reader & Modeler. Glycan Reader & Modeler is freely available at http://charmm-gui.org/input/glycan.

Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization.

Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.

Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank.

MOTIVATION: Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader. RESULTS: Glycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure.Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB. AVAILABILITY AND IMPLEMENTATION: http://www.charmm-gui.org/input/glycan and http://www.glycanstructure.org. CONTACT: wonpil@lehigh.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules.

Reading ligand structures into any simulation program is often nontrivial and time consuming, especially when the force field parameters and/or structure files of the corresponding molecules are not available. To address this problem, we have developed Ligand Reader & Modeler in CHARMM-GUI. Users can upload ligand structure information in various forms (using PDB ID, ligand ID, SMILES, MOL/MOL2/SDF file, or PDB/mmCIF file), and the uploaded structure is displayed on a sketchpad for verification and further modification. Based on the displayed structure, Ligand Reader & Modeler generates the ligand force field parameters and necessary structure files by searching for the ligand in the CHARMM force field library or using the CHARMM general force field (CGenFF). In addition, users can define chemical substitution sites and draw substituents in each site on the sketchpad to generate a set of combinatorial structure files and corresponding force field parameters for throughput or alchemical free energy simulations. Finally, the output from Ligand Reader & Modeler can be used in other CHARMM-GUI modules to build a protein-ligand simulation system for all supported simulation programs, such as CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Ligand Reader & Modeler is available as a functional module of CHARMM-GUI at http://www.charmm-gui.org/input/ligandrm. © 2017 Wiley Periodicals, Inc.

CHARMM-GUI 10 years for biomolecular modeling and simulation.

CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface that prepares complex biomolecular systems for molecular simulations. CHARMM-GUI creates input files for a number of programs including CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Since its original development in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to set up a broad range of simulations: (1) PDB Reader & Manipulator, Glycan Reader, and Ligand Reader & Modeler for reading and modifying molecules; (2) Quick MD Simulator, Membrane Builder, Nanodisc Builder, HMMM Builder, Monolayer Builder, Micelle Builder, and Hex Phase Builder for building all-atom simulation systems in various environments; (3) PACE CG Builder and Martini Maker for building coarse-grained simulation systems; (4) DEER Facilitator and MDFF/xMDFF Utilizer for experimentally guided simulations; (5) Implicit Solvent Modeler, PBEQ-Solver, and GCMC/BD Ion Simulator for implicit solvent related calculations; (6) Ligand Binder for ligand solvation and binding free energy simulations; and (7) Drude Prepper for preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM-GUI, such as Glycolipid Modeler for generation of various glycolipid structures, and LPS Modeler for generation of lipopolysaccharide structures from various Gram-negative bacteria. These new features together with existing modules are expected to facilitate advanced molecular modeling and simulation thereby leading to an improved understanding of the structure and dynamics of complex biomolecular systems. Here, we briefly review these capabilities and discuss potential future directions in the CHARMM-GUI development project. © 2016 Wiley Periodicals, Inc.

Preferred conformations of N-glycan core pentasaccharide in solution and in glycoproteins.

N-linked glycans are on protein surfaces and have direct and water/ion-mediated interactions with surrounding amino acids. Such contacts could restrict their conformational freedom compared to the same glycans free in solution. In this work, we have examined the conformational freedom of the N-glycan core pentasaccharide moiety in solution using standard molecular dynamics (MD) simulations as well as temperature replica-exchange MD simulations. Both simulations yield the comparable conformational variability of the pentasaccharide in solution, indicating the convergence of both simulations. The glycoprotein crystal structures are analyzed to compare the conformational freedom of the N-glycan on the protein surface with the simulation result. Surprisingly, the pentasaccharide free in solution shows more restricted conformational variability than the N-glycan on the protein surface. The interactions between the carbohydrate and the protein side chain appear to be responsible for the increased conformational diversity of the N-glycan on the protein surface. Finally, the transfer entropy analysis of the simulation trajectory also reveals an unexpected causality relationship between intramolecular hydrogen bonds and the conformational states in that the hydrogen bonds play a role in maintaining the conformational states rather than driving the change in glycosidic torsional states.

Roles of glycans in interactions between gp120 and HIV broadly neutralizing antibodies.

Many novel broadly neutralizing antibodies against human immunodeficiency virus (HIV) have been identified during the past decade, providing promising templates for the development of an effective HIV-1 vaccine. Structural studies reveal that the epitopes of some of these antibodies involve one or more crucial glycans, without which the binding is completely abolished. In this study, we have investigated the critical roles of glycans in interactions between HIV-1 gp120 and two broadly neutralizing antibodies PG9 (targeting V1/V2) and PGT128 (targeting V3) that are able to neutralize more than 70% of HIV-1 isolates. We have performed molecular dynamics simulations of a number of systems including antibody-gp120 complex with and without glycans, antibody, gp120 with and without glycans, and glycan-only systems. The simulation results show that the complex structures are stabilized by the glycans, and the multivalent interactions between the antibody and gp120 promote cooperativities to further enhance the binding. In the free gp120, the glycans increase the flexibility of the V1/V2 and V3 loops, which likely increases the entropy cost of the antibody recognition. However, the antibodies are able to bind the flexible interface by recognizing the preexisting glycan conformation, and penetrating the glycan shield with flexible complementarity determining region loops that sample the bound conformations occasionally.

GS-align for glycan structure alignment and similarity measurement.

MOTIVATION: Glycans play critical roles in many biological processes, and their structural diversity is key for specific protein-glycan recognition. Comparative structural studies of biological molecules provide useful insight into their biological relationships. However, most computational tools are designed for protein structure, and despite their importance, there is no currently available tool for comparing glycan structures in a sequence order- and size-independent manner. RESULTS: A novel method, GS-align, is developed for glycan structure alignment and similarity measurement. GS-align generates possible alignments between two glycan structures through iterative maximum clique search and fragment superposition. The optimal alignment is then determined by the maximum structural similarity score, GS-score, which is size-independent. Benchmark tests against the Protein Data Bank (PDB) N-linked glycan library and PDB homologous/non-homologous N-glycoprotein sets indicate that GS-align is a robust computational tool to align glycan structures and quantify their structural similarity. GS-align is also applied to template-based glycan structure prediction and monosaccharide substitution matrix generation to illustrate its utility. AVAILABILITY AND IMPLEMENTATION: http://www.glycanstructure.org/gsalign. CONTACT: wonpil@ku.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Solid-State NMR-Restrained Ensemble Dynamics of a Membrane Protein in Explicit Membranes.

Solid-state NMR has been used to determine the structures of membrane proteins in native-like lipid bilayer environments. Most structure calculations based on solid-state NMR observables are performed using simulated annealing with restrained molecular dynamics and an energy function, where all nonbonded interactions are represented by a single, purely repulsive term with no contributions from van der Waals attractive, electrostatic, or solvation energy. To our knowledge, this is the first application of an ensemble dynamics technique performed in explicit membranes that uses experimental solid-state NMR observables to obtain the refined structure of a membrane protein together with information about its dynamics and its interactions with lipids. Using the membrane-bound form of the fd coat protein as a model membrane protein and its experimental solid-state NMR data, we performed restrained ensemble dynamics simulations with different ensemble sizes in explicit membranes. For comparison, a molecular dynamics simulation of fd coat protein was also performed without any restraints. The average orientation of each protein helix is similar to a structure determined by traditional single-conformer approaches. However, their variations are limited in the resulting ensemble of structures with one or two replicas, as they are under the strong influence of solid-state NMR restraints. Although highly consistent with all solid-state NMR observables, the ensembles of more than two replicas show larger orientational variations similar to those observed in the molecular dynamics simulation without restraints. In particular, in these explicit membrane simulations, Lys(40), residing at the C-terminal side of the transmembrane helix, is observed to cause local membrane curvature. Therefore, compared to traditional single-conformer approaches in implicit environments, solid-state NMR restrained ensemble simulations in explicit membranes readily characterize not only protein dynamics but also protein-lipid interactions in detail.

Exploring the conformational transitions of biomolecular systems using a simple two-state anisotropic network model.

Biomolecular conformational transitions are essential to biological functions. Most experimental methods report on the long-lived functional states of biomolecules, but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect experimentally. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed experimentally. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a physically reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the experimental structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the minimum energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biological interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom molecular dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides experimentally testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results.

Glycan fragment database: a database of PDB-based glycan 3D structures.

The glycan fragment database (GFDB), freely available at http://www.glycanstructure.org, is a database of the glycosidic torsion angles derived from the glycan structures in the Protein Data Bank (PDB). Analogous to protein structure, the structure of an oligosaccharide chain in a glycoprotein, referred to as a glycan, can be characterized by the torsion angles of glycosidic linkages between relatively rigid carbohydrate monomeric units. Knowledge of accessible conformations of biologically relevant glycans is essential in understanding their biological roles. The GFDB provides an intuitive glycan sequence search tool that allows the user to search complex glycan structures. After a glycan search is complete, each glycosidic torsion angle distribution is displayed in terms of the exact match and the fragment match. The exact match results are from the PDB entries that contain the glycan sequence identical to the query sequence. The fragment match results are from the entries with the glycan sequence whose substructure (fragment) or entire sequence is matched to the query sequence, such that the fragment results implicitly include the influences from the nearby carbohydrate residues. In addition, clustering analysis based on the torsion angle distribution can be performed to obtain the representative structures among the searched glycan structures.

Lipid-linked oligosaccharides in membranes sample conformations that facilitate binding to oligosaccharyltransferase.

Lipid-linked oligosaccharides (LLOs) are the substrates of oligosaccharyltransferase (OST), the enzyme that catalyzes the en bloc transfer of the oligosaccharide onto the acceptor asparagine of nascent proteins during the process of N-glycosylation. To explore LLOs' preferred location, orientation, structure, and dynamics in membrane bilayers of three different lipid types (dilauroylphosphatidylcholine, dimyristoylphosphatidylcholine, and dioleoylphosphatidylcholine), we have modeled and simulated both eukaryotic (Glc3-Man9-GlcNAc2-PP-Dolichol) and bacterial (Glc1-GalNAc5-Bac1-PP-Undecaprenol) LLOs, which are composed of an isoprenoid moiety and an oligosaccharide, linked by pyrophosphate. The simulations show no strong impact of different bilayer hydrophobic thicknesses on the overall orientation, structure, and dynamics of the isoprenoid moiety and the oligosaccharide. The pyrophosphate group stays in the bilayer head group region. The isoprenoid moiety shows high flexibility inside the bilayer hydrophobic core, suggesting its potential role as a tentacle to search for OST. The oligosaccharide conformation and dynamics are similar to those in solution, but there are preferred interactions between the oligosaccharide and the bilayer interface, which leads to LLO sugar orientations parallel to the bilayer surface. Molecular docking of the bacterial LLO to a bacterial OST suggests that such orientations can enhance binding of LLOs to OST.

ST-analyzer: a web-based user interface for simulation trajectory analysis.

Molecular dynamics (MD) simulation has become one of the key tools to obtain deeper insights into biological systems using various levels of descriptions such as all-atom, united-atom, and coarse-grained models. Recent advances in computing resources and MD programs have significantly accelerated the simulation time and thus increased the amount of trajectory data. Although many laboratories routinely perform MD simulations, analyzing MD trajectories is still time consuming and often a difficult task. ST-analyzer, http://im.bioinformatics.ku.edu/st-analyzer, is a standalone graphical user interface (GUI) toolset to perform various trajectory analyses. ST-analyzer has several outstanding features compared to other existing analysis tools: (i) handling various formats of trajectory files from MD programs, such as CHARMM, NAMD, GROMACS, and Amber, (ii) intuitive web-based GUI environment--minimizing administrative load and reducing burdens on the user from adapting new software environments, (iii) platform independent design--working with any existing operating system, (iv) easy integration into job queuing systems--providing options of batch processing either on the cluster or in an interactive mode, and (v) providing independence between foreground GUI and background modules--making it easier to add personal modules or to recycle/integrate pre-existing scripts utilizing other analysis tools. The current ST-analyzer contains nine main analysis modules that together contain 18 options, including density profile, lipid deuterium order parameters, surface area per lipid, and membrane hydrophobic thickness. This article introduces ST-analyzer with its design, implementation, and features, and also illustrates practical analysis of lipid bilayer simulations.

CHARMM-GUI Membrane Builder toward realistic biological membrane simulations.

CHARMM-GUI Membrane Builder, http://www.charmm-gui.org/input/membrane, is a web-based user interface designed to interactively build all-atom protein/membrane or membrane-only systems for molecular dynamics simulations through an automated optimized process. In this work, we describe the new features and major improvements in Membrane Builder that allow users to robustly build realistic biological membrane systems, including (1) addition of new lipid types, such as phosphoinositides, cardiolipin (CL), sphingolipids, bacterial lipids, and ergosterol, yielding more than 180 lipid types, (2) enhanced building procedure for lipid packing around protein, (3) reliable algorithm to detect lipid tail penetration to ring structures and protein surface, (4) distance-based algorithm for faster initial ion displacement, (5) CHARMM inputs for P21 image transformation, and (6) NAMD equilibration and production inputs. The robustness of these new features is illustrated by building and simulating a membrane model of the polar and septal regions of E. coli membrane, which contains five lipid types: CL lipids with two types of acyl chains and phosphatidylethanolamine lipids with three types of acyl chains. It is our hope that CHARMM-GUI Membrane Builder becomes a useful tool for simulation studies to better understand the structure and dynamics of proteins and lipids in realistic biological membrane environments.