Molecular Dynamics Flexible Fitting
The molecular dynamics flexible fitting (MDFF) method can be used to flexibly fit atomic structures into density maps. The method consists of adding external forces proportional to the gradient of the density map into a molecular dynamics (MD) simulation of the atomic structure.xMDFF for X-ray Crystallography
Recently, we have developed a new MDFF-based approach, xMDFF, for determining structures from such low-resolution crystallographic data. xMDFF employs a real-space refinement scheme that flexibly fits atomic models into an iteratively updating electron density map. It addresses significant large-scale deformations of the initial model to fit the low-resolution density.
Use the menu above to navigate the MDFF website. For examples of MDFF applications, visit the websites on Mechanisms of Protein Synthesis by the Ribosome, Dynamics of Protein Translocation, Molecular Dynamics of Viruses, and Intrinsic Curvature Properties of Photosynthetic Proteins in Chromatophore.
Recent News and Announcements: VMD 1.9.2 Released: UPDATE (February 2015)
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February UPDATE: The MDFF tutorial has now been updated to cover the latest MDFF features outlined below, including the MDFF GUI, interactive MDFF, and Timeline cross correlation analysis. VMD 1.9.2 contains several updates for the Molecular Dynamics Flexible Fitting (MDFF) Method. The mdff plugin contains new options for setting up MDFF simulations with implicit solvent and the new MDFF method for low-resolution x-ray crystallography (xMDFF, recently detailed in this article). Complete details for setting up xMDFF simulations with this plugin can be found in the MDFF tutorial. Additionally, a new graphical user interface for the mdff plugin is now available in the Modeling section of the Extensions menu. This interface allows for easier setup of MDFF and xMDFF simulations, as well as running, connecting to, and analyzing interactive MD simulations. VMD 1.9.2 also contains new multi-core CPU and GPU-accelerated analysis capabilities for MDFF, as described in a recent article. The mdffi cc command can be used to quickly compute the cross correlation of a structure to a target density map much faster than previous methods. This speedup allows for the analysis of very large structures (millions of atoms) and very long (microsecond range) trajectories, and at a much finer level of detail than was previously feasible. The Timeline plugin uses this new fast cross correlation method to visually present the results of the analysis, making it easier than ever to determine the quality of fit for a MDFF simulation and to quickly identify poorly fitting regions of a structure that require further simulation. More information about other latest features of VMD version 1.9.2 can be found on the release page.
Spotlight: Computing the Bacterial Brain (Mar 2016)
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Motile bacteria position themselves within their habitats optimally, seeking proximity to favorable growth conditions while avoiding unfavorable ones. Cues used for this placement come in the form of small chemicals, so-called attractors and repellants, as well as physical factors such as favorable visible light and unfavorable UV radiation. To balance such a broad range of factors, bacteria monitor their environments and respond by way of a fundamental sensory capability known as chemotaxis. Chemotactic responses in bacteria involve large complexes of sensory proteins, known as chemosensory arrays, that process the information obtained from the bacteria's habitat to determine its swimming pattern. In this sense, the chemosensory array functions as a bacterial brain, transforming sensory input into motile output. Despite great strides in the understanding of how the chemosensory array's constituent proteins fit and work together, a high-resolution description of the kind needed to explore in detail the molecular mechanisms underlying sensory signal transduction within the array has remained elusive. A new study, utilizing cryo-electron microscopy and molecular dynamics simulations with NAMD, reports the highest resolution images yet of the bacterial brain's molecular anatomy. Using computational techniques, structural data from X-ray crystallography and electron microscopy are compared to derive an atomically resolved model of the chemosensory array's extended molecular structure that involves millions of atoms. Subsequent simulations of the model revealed a novel conformational change in a key sensory protein, that is interpreted as a key signaling event in the translation of chemosensory information into swimming pattern. More details on this work can be found in a recent news release as well as on our bacterial chemotaxis website.