NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on central processing unit (CPU)-and graphics processing unit (GPU)-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics, controlling the temperature, pressure and pH, applying external potentials on tailored grids, leveraging massively parallel resources in multiple-copy simulations, as well as hybrid QM/MM descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations, and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts towards achieving optimal performance on GPUbased architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

A software framework is introduced that facilitates the application of biasing algorithms to collective variables of the type commonly employed to drive massively parallel molecular dynamics (MD) simulations. The modular framework that is presented enables one to combine existing collective variables into new ones, and combine any chosen collective variable with available biasing methods. The latter include the classic time-dependent biases referred to as steered MD and targeted MD, the temperature-accelerated MD algorithm, as well as the adaptive free-energy biases called metadynamics and adaptive biasing force. The present modular software is extensible, and portable between commonly used MD simulation engines.

The M2 proton channel from influenza A virus is an essential protein that mediates transport of protons across the viral envelope. This protein has a single transmembrane helix, which tetramerizes into the active channel. At the heart of the conduction mechanism is the exchange of protons between the His37 imidazole moieties of M2 and waters confined to the M2 bundle interior. Protons are conducted as the total charge of the four His37 side chains passes through 2 þ and 3 þ with a pK a near 6. A 1.65 Å resolution X-ray structure of the transmembrane protein (residues 25-46), crystallized at pH 6.5, reveals a pore that is lined by alternating layers of sidechains and well-ordered water clusters, which offer a pathway for proton conduction. The His37 residues form a box-like structure, bounded on either side by water clusters with wellordered oxygen atoms at close distance. The conformation of the protein, which is intermediate between structures previously solved at higher and lower pH, suggests a mechanism by which conformational changes might facilitate asymmetric diffusion through the channel in the presence of a proton gradient. Moreover, protons diffusing through the channel need not be localized to a single His37 imidazole, but instead may be delocalized over the entire His-box and associated water clusters. Thus, the new crystal structure provides a possible unification of the discrete site versus continuum conduction models.ion channels | M2 proton channel | membrane proteins | water clusters | histidine protonation W ater molecules confined at interfaces or in cavities behave differently from those in the bulk. Studies of water clusters have shed light not only on their fundamental properties (1-4) but also on the mechanism employed by various nano-bio systems to fine-tune water and proton transport (5-9). A relevant example from biology is the M2 protein of the influenza A virus (10, 11), which is the target of the influenza drugs amantadine and rimantadine (12-17). Tetrameric M2 (18) transports protons across the viral envelope to acidify the virion interior and trigger uncoating of the viral RNA prior to fusion of the viral envelope with the endosomal bilayer (19). M2 is one of the smallest bona fide channel/transporter proteins (96 residues), capable of pHdependent activation and highly selective conduction of protons vs. other ions (20)(21)(22)(23)(24)(25). A narrow pore leads to the highly conserved His37 and Trp41 residues (16,17,(26)(27)(28)(29), which are respectively responsible for proton selectivity (30) and asymmetry in the magnitude of conductance when the proton gradient is reversed (31). Thus the control of proton diffusion across the membrane relies on the ability of the imidazole moieties of His37 to accept and store protons from water molecules in the pore.An M2 peptide (residues 22-46), slightly longer than the transmembrane domain (32), associates into a functional four-helix bundle (33). Solid state 15 N nuclear magnetic resonance (ssNMR) experiments indicate that the first protons ...

Abstract:A new implementation of the adaptive biasing force (ABF) method is described. This implementation supports a wide range of collective variables and can be applied to the computation of multidimensional energy profiles. It is provided to the community as part of a code that implements several analogous methods, including metadynamics. ABF and metadynamics have not previously been tested side by side on identical systems. Here, numerical tests are carried out on processes including conformational changes in model peptides and translocation of a halide ion across a lipid membrane through a peptide nanotube. On the basis of these examples, we discuss similarities and differences between the ABF and metadynamics schemes. Both approaches provide enhanced sampling and free energy profiles in quantitative agreement with each other in different applications. The method of choice depends on the dimension of the reaction coordinate space, the height of the barriers, and the relaxation times of degrees of freedom in the orthogonal space, which are not explicitly described by the chosen collective variables.