VMD: Visual molecular dynamics

The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.

/pdf/ucsf-chimera-a-visualization-system-for-exploratory-research-59xu54fhxg.pdf

UCSF Chimera--a visualization system for exploratory research and analysis.

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

The X-ray crystal structure of the nucleosome core particle of chromatin shows in atomic detail how the histone protein octamer is assembled and how 146 base pairs of DNA are organized into a superhelix around it. Both histone/histone and histone/DNA interactions depend on the histone fold domains and additional, well ordered structure elements extending from this motif. Histone amino-terminal tails pass over and between the gyres of the DNA superhelix to contact neighbouring particles. The lack of uniformity between multiple histone/DNA-binding sites causes the DNA to deviate from ideal superhelix geometry.

Crystal structure of the nucleosome core particle at 2.8 Å resolution

We describe the development, current features, and some directions for future development of the Amber package of computer programs. This package evolved from a program that was constructed in the late 1970s to do Assisted Model Building with Energy Refinement, and now contains a group of programs embodying a number of powerful tools of modern computational chemistry, focused on molecular dynamics and free energy calculations of proteins, nucleic acids, and carbohydrates.

/pdf/the-amber-biomolecular-simulation-programs-e2nyq1ef44.pdf

The Amber biomolecular simulation programs

Category: genotype

Project: Pharmacogenetics of Membrane Transporters

[Table 1][1] provides HUGO Gene Nomenclature Committee (HGNC) symbols, PharmGKB submission URLs, submission dates, and release dates. [Table 2][2] provides HGNC symbols, HGNC names, synonyms, GenBank accession numbers, and locus

PharmGKB submission update: VI. PMT submissions of genetic variations in neurotransmitter transporters (SLC6, SLC17, and SLC18) to the PharmGKB network.

Gene knockouts in a model organism such as mouse provide a valuable resource for the study of basic biology and human disease. Determining which gene has been inactivated by an untargeted gene trapping event poses a challenging annotation problem because gene trap sequence tags, which represent sequence near the vector insertion site of a trapped gene, are typically short and often contain unresolved residues. To understand better the localization of these sequences on the mouse genome, we compared stand-alone versions of the alignment programs BLAT, SSAHA, and MegaBLAST. A set of 3,369 sequence tags was aligned to build 34 of the mouse genome using default parameters for each algorithm. Known genome coordinates for the cognate set of full-length genes (1,659 sequences) were used to evaluate localization results. In general, all three programs performed well in terms of localizing sequences to a general region of the genome, with only relatively subtle errors identified for a small proportion of the sequence tags. However, large differences in performance were noted with regard to correctly identifying exon boundaries. BLAT correctly identified the vast majority of exon boundaries, while SSAHA and MegaBLAST missed the majority of exon boundaries. SSAHA consistently reported the fewest false positives and is the fastest algorithm. MegaBLAST was comparable to BLAT in speed, but was the most susceptible to localizing sequence tags incorrectly to pseudogenes. The differences in performance for sequence tags and full-length reference sequences were surprisingly small. Characteristic variations in localization results for each program were noted that affect the localization of sequence at exon boundaries, in particular.

/pdf/comparison-of-methods-for-genomic-localization-of-gene-trap-36jepjerf8.pdf

Comparison of methods for genomic localization of gene trap sequences

The MacroMolecular WorkBench (MMWB) is a software platform designed to integrate a diverse set of software and computer hardware to meet the needs of scientist performing molecular modeling. The authors used the study of collagen to test the functionality of MMWB. >

http://ieeexplore.ieee.org/iel2/882/4721/00183936.pdf

The Macromolecular Workbench and its application to the study of collagen

Wrappy generates a Python extension module from annotated C++ header files. Wrappy’s power comes from how it translates C++ features to Python features: standard C++ container classes are mapped to Python lists and dictionaries; C++ classes are converted to Python types (or a Python-subclassable extension class [Ful98]); public member data and accessor member functions are mapped to attributes; C++ exceptions are mapped to Python exceptions; overloaded functions are supported; function argument names are used for Python keyword arguments. Annotating C++ header files is necessary to designate output arguments in functions and to choose how C++ classes are wrapped. Some C++ source code modifications are necessary to harmonize C++ and Python object semantics and to work around limitations in wrappy. The impetus behind wrappy was to generate wrapper code for our (growing) C++ molecular graphics library with 500 member functions in 30 classes. Writing and maintaining wrapper code by hand for that many functions was unappealing. We also found that existing wrapper generators didn’t support all the C++ features we use. Wrappy generates code that is similar to hand-crafted wrapper code, but more robust. The C++ header files generate about 30 times their ‘‘weight’’ in Python interface code (in our case, from 850 lines in C++ header files to over 25,000 lines of Python interface code).

/pdf/wrappy-a-python-wrapper-generator-for-c-classes-3nex474hu1.pdf

Wrappy — a Python Wrapper Generator for C++ Classes

Visualization interfaces for high performance computing systems pose special problems due to the complexity and volume of data these systems manipulate. In the post-genomic era, scientists must be able to quickly gain insight into structure-function problems, and require flexible computing environments to quickly create interfaces that link the relevant tools. Feature, a program for analyzing protein sites, takes a set of 3-dimensional structures and creates statistical models of sites of structural or functional significance. Until now, Feature has provided no support for visualization, which can make understanding its results difficult. We have developed an extension to the molecular visualization program Chimera that integrates Feature's statistical models and site predictions with 3-dimensional structures viewed in Chimera. We call this extension ViewFeature, and it is designed to help users understand the structural Features that define a site of interest. We applied ViewFeature in an analysis of the enolase superfamily; a functionally distinct class of proteins that share a common fold, the alpha/beta barrel, in order to gain a more complete understanding of the conserved physical properties of this superfamily. In particular, we wanted to define the structural determinants that distinguish the enolase superfamily active site scaffold from other alpha/beta barrel superfamilies and particularly from other metal-binding alpha/beta barrel proteins. Through the use of ViewFeature, we have found that the C-terminal domain of the enolase superfamily does not differ at the scaffold level from metal-binding alpha/beta barrels. We are, however, able to differentiate between the metal-binding sites of alpha/beta barrels and those of other metal-binding proteins. We describe the overall architectural Features of enolases in a radius of 10 Angstroms around the active site.

/pdf/viewfeature-integrated-feature-analysis-and-visualization-awvjxz045y.pdf

Conrad C. Huang

Papers

PharmGKB submission update: VI. PMT submissions of genetic variations in neurotransmitter transporters (SLC6, SLC17, and SLC18) to the PharmGKB network.

Comparison of methods for genomic localization of gene trap sequences

The Macromolecular Workbench and its application to the study of collagen

Wrappy — a Python Wrapper Generator for C++ Classes

ViewFeature: integrated feature analysis and visualization.