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 molecular details 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.

We establish a reliable and robust standardization of settings for practical molecular dynamics (MD) simulations of pure and mixed (single-and multi-component) lipid bilayer membranes. In lipid membranes research, particle-based molecular simulations are a powerful tool alongside continuum theory, lipidomics, and model, in vitro, and in vivo experiments. Molecular simulations can provide precise and reproducible spatiotemporal (atomic-and femtosecond-level) information about membrane structure, mechanics, thermodynamics, kinetics, and dynamics. Yet the simulation of lipid membranes can be a daunting task, given the uniqueness of lipid membranes relative to conventional liquid-liquid and solid-liquid interfaces, the immense and complex thermodynamic and statistical mechanical theory, the diversity of multiscale lipid models, limitations of modern computing power, the difficulty and ambiguity of simulation controls, finite size effects, competitive continuum simulation alternatives, and the desired application, including vesicle experiments and biological membranes. These issues can complicate an essential understanding of the field of lipid membranes, and create major bottlenecks to simulation advancement. In this article, we clarify these issues and present a consistent, thorough, and user-friendly framework for the design of state-of-the-art lipid membrane MD simulations. We hope to allow early-career researchers to quickly overcome common obstacles in the field of lipid membranes and reach maximal impact in their simulations.

and, as a monounsaturated comparison, 1-stearoyl-2-oleoylphosphatidylcholine (SOPC, 18:0-18:1PC) in the absence and presence of cholesterol. They are run at 37 C under constant pressure for 200 ns on bilayers containing 98 lipid molecules (single component membranes) or 100 lipid molecules (80 phospholipids and 20 cholesterols) that are hydrated in the ratio of 20 water molecules per lipid molecule. As validation, order parameter profiles along the sn-1 chain calculated from the trajectories will be compared with results from solid state 2H NMR experiments. Our analysis will identify the difference in flexibility between EPA, DPA and DHA chains. The impact of the difference in flexibility of each n-3 PUFA on the conformation of the adjacent stearic acid chain at the sn-1 position and on the interaction with cholesterol will then be investigated.

Nucleic acid probes are used for diverse applications in vitro, in situ, and in vivo. In any setting, their power is limited by imperfect selectivity (binding of undesired targets) and incomplete affinity (binding is reversible, and not all desired targets are bound). These difficulties are fundamental, stemming from reliance on base pairing alone to provide both selectivity and affinity. Shielded covalent (SC) probes eliminate the longstanding trade-off between selectivity and durable target capture, achieving selectivity via programmable molecular conformation change and durable target capture via activatable covalent cross-linking (Vieregg et al, J. Am. Chem. Soc. 2013). In pure and mixed samples, SC probes covalently capture complementary DNA or RNA oligonucleotide targets and reject two-nucleotide mismatched targets with near-quantitative yields at room temperature, achieving discrimination ratios of 2À3 orders of magnitude. Semi-quantitative studies with full-length mRNA targets demonstrate selective covalent capture comparable to that for RNA oligo targets. Single-nucleotide DNA or RNA mismatches, including nearly isoenergetic RNA wobble pairs, can be efficiently rejected with discrimination ratios of 1À2 orders of magnitude. Covalent capture yields appear consistent with the thermodynamics of probe/target hybridization, facilitating rational probe design. If desired, cross-links can be reversed to release the target after capture. In contrast to existing probe chemistries, SC probes achieve the high sequence selectivity of a structured probe, yet durably retain their targets even under denaturing conditions. This previously incompatible combination of properties suggests diverse applications in vitro and in vivo; this talk will present our latest results on SC probe applications.

Storage of large molecular dynamics (MD) simulation measurements in standard databases is a challenging task. The requirements on disk space, input/output (I/O) and data transfer bandwidth are excessively high due to the large volume, possibly terabytes or petabytes, of data generated. Storage of data in compressed form has been a popular approach to address such issues. In this paper, we present a lossy compression framework that yields significant performance gain by combining the strength of the principal component analysis (PCA) and discrete cosine transform (DCT).