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Kyle A Beauchamp

Publications and source records attributed to Kyle A Beauchamp.

3 recordsLinked to original sources

Ensembler: Enabling high-throughput molecular simulations at the superfamily scale

The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences--from a single sequence to an entire superfamily-- and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics--such as Markov state models (MSMs)--which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase family, using all available kinase catalytic domain structures from any organism as structural templates.\n\nEnsembler is free and open source software licensed under the GNU General Public License (GPL) v2. It is compatible with Linux and OS X. The latest release can be installed via the conda package manager, and the latest source can be downloaded from https://github.com/choderalab/ensembler.

Bioinformatics

MDTraj: a modern, open library for the analysis of molecular dynamics trajectories

Summary: MDTraj is a modern, lightweight and efficient software package for analyzing molecular dynamics simulations. MDTraj reads trajectory data from a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including RMSD, DSSP secondary structure assignment and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between molecular dynamics data and the rapidly-growing collection of industry-standard statistical analysis and visualization tools in Python. Availability: Package downloads, detailed examples and full documentation are available at http://mdtraj.org. The source code is distributed under the GNU Lesser General Public License at https://github.com/simtk/mdtraj.

Bioinformatics

A Fast 3 x N Matrix Multiply Routine for Calculation of Protein RMSD

The bottleneck for the rapid calculation of the root-mean-square deviation in atomic coordinates (RMSD) between pairs of protein structures for large numbers of conformations is the evaluation of a 3 x N x N x 3 matrix product over conformation pairs. Here we describe two matrix multiply routines specialized for the 3 x N case that are able to significantly outperform (by up to 3X) off-the-shelf high-performance linear algebra libraries for this computation, reaching machine limits on performance. The routines are implemented in C and Python libraries, and are available at https://github.com/simtk/IRMSD.

Biophysics