NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and 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; and hybrid quantum-mechanical/molecular-mechanical 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 toward achieving optimal performance on GPU-based 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.

The integration of quantum chemistry and molecular dynamics programs coupled with a graphical user interface provides a streamlined tool for powerful simulations of biomolecular reaction mechanisms. Hybrid methods that combine quantum mechanics (QM) and molecular mechanics (MM) can be applied to studies of reaction mechanisms in locations ranging from active sites of small enzymes to multiple sites in large bioenergetic complexes. By combining the widely used molecular dynamics and visualization programs NAMD and VMD with the quantum chemistry packages ORCA and MOPAC, we created an integrated, comprehensive, customizable, and easy-to-use suite ( http://www.ks.uiuc.edu/Research/qmmm ). Through the QwikMD interface, setup, execution, visualization, and analysis are streamlined for all levels of expertise.

How a pathogen holds on to its host: Staphylococcus epidermidis and Staphylococcus aureus are pathogens that can form biofilms on implants and medical devices. Central to biofilm formation is a very tight interaction between microbial surface proteins called adhesins and components of the extracellular matrix of the host. Milles et al. used atomic force microscopy-based single-molecule force spectroscopy combined with steered molecular dynamics to explore how the bond between staphylococcal adhesin SdrG and its target fibrinogen peptide can withstand forces greater than 2 nanonewtons (see the Perspective by Herman-Bausier and Dufrêne). The peptide is confined in a coiled geometry in a deep and rigid pocket through hydrogen bonds between SdrG and the peptide backbone. If pulled, the load is distributed over all hydrogen bonds so that all bonds must be broken at once to break the interaction. Science , this issue p. 1527 ; see also p. 1464

Molecular interactions are essential for regulation of cellular processes from the formation of multi-protein complexes to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the dynamical network analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is frequently limited to relatively small biomolecular systems. In this work, we provide an evolution of the method, application, and interface. All data processing and analysis are conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination of optimal paths. Using the popular visualization program visual molecular dynamics (VMD), high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5M atoms, namely, the OMP-decarboxylase, the leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.

Single-molecule force spectroscopy reveals how the mechanical stability of a widely used biomolecular complex varies fourfold.