To facilitate flexible and efficient structural bioinformatics analyses, new functionality for three-dimensional structure processing and analysis has been introduced into PyCogent -- a popular feature-rich framework for sequence-based bioinformatics
, but one which has lacked equally powerful tools for handling stuctural/coordinate-based data. Extensible Python modules have been developed, which provide object-oriented abstractions (based on a hierarchical representation of macromolecules), efficient data structures (e.g. kD-trees), fast implementations of common algorithms (e.g. surface-area calculations), read/write support for Protein Data Bank-related file formats and wrappers for external command-line applications (e.g. Stride). Integration of this code into PyCogent is symbiotic, allowing sequence-based work to benefit from structure-derived data and, reciprocally, enabling structural studies to leverage PyCogents versatile tools for phylogenetic and evolutionary analyses.
The PyMOL molecular graphics program has been modified to introduce a new putty cartoon representation, akin to the sausage-style representation of the MOLMOL molecular visualization (MolVis) software package. This document outlines the development and implementation of the putty representation.
The sequence-dependent structural variability and conformational dynamics of DNA play pivotal roles in many biological milieus, such as in the site-specific binding of transcription factors to target regulatory elements. To better understand DNA stru
cture, function, and dynamics in general, and protein-DNA recognition in the kB family of genetic regulatory elements in particular, we performed molecular dynamics simulations of a 20-base pair DNA encompassing a cognate kB site recognized by the proto-oncogenic c-Rel subfamily of NF-kB transcription factors. Simulations of the kB DNA in explicit water were extended to microsecond duration, providing a broad, atomically-detailed glimpse into the structural and dynamical behavior of double helical DNA over many timescales. Of particular note, novel (and structurally plausible) conformations of DNA developed only at the long times sampled in this simulation -- including a peculiar state arising at ~ 0.7 us and characterized by cross-strand intercalative stacking of nucleotides within a longitudinally-sheared base pair, followed (at ~ 1 us) by spontaneous base flipping of a neighboring thymine within the A-rich duplex. Results and predictions from the us-scale simulation include implications for a dynamical NF-kB recognition motif, and are amenable to testing and further exploration via specific experimental approaches that are suggested herein.
One need only compare the number of three-dimensional molecular illustrations in the first (1990) and third (2004) editions of Voet & Voets Biochemistry in order to appreciate this fields profound communicative value in modern biological sciences --
ranging from medicine, physiology, and cell biology, to pharmaceutical chemistry and drug design, to structural and computational biology. The cliche about a picture being worth a thousand words is quite poignant here: The information content of an effectively-constructed piece of molecular graphics can be immense. Because biological function arises from structure, it is difficult to overemphasize the utility of visualization and graphics in molding our current understanding of the molecular nature of biological systems. Nevertheless, creating effective molecular graphics is not easy -- neither conceptually, nor in terms of effort required. The present collection of Rules is meant as a guide for those embarking upon their first molecular illustrations.
PaPy, which stands for parallel pipelines in Python, is a highly flexible framework that enables the construction of robust, scalable workflows for either generating or processing voluminous datasets. A workflow is created from user-written Python fu
nctions (nodes) connected by pipes (edges) into a directed acyclic graph. These functions are arbitrarily definable, and can make use of any Python modules or external binaries. Given a user-defined topology and collection of input data, functions are composed into nested higher-order maps, which are transparently and robustly evaluated in parallel on a single computer or on remote hosts. Local and remote computational resources can be flexibly pooled and assigned to functional nodes, thereby allowing facile load-balancing and pipeline optimization to maximize computational throughput. Input items are processed by nodes in parallel, and traverse the graph in batches of adjustable size -- a trade-off between lazy-evaluation, parallelism, and memory consumption. The processing of a single item can be parallelized in a scatter/gather scheme. The simplicity and flexibility of distributed workflows using PaPy bridges the gap between desktop -> grid, enabling this new computing paradigm to be leveraged in the processing of large scientific datasets.
The biomolecules in and around a living cell -- proteins, nucleic acids, lipids, carbohydrates -- continuously sample myriad conformational states that are thermally accessible at physiological temperatures. Simultaneously, a given biomolecule also s
amples (and is sampled by) a rapidly fluctuating local environment comprised of other biopolymers, small molecules, water, ions, etc. that diffuse to within a few nanometers, leading to inter-molecular contacts that stitch together large supramolecular assemblies. Indeed, all biological systems can be viewed as dynamic networks of molecular interactions. As a complement to experimentation, molecular simulation offers a uniquely powerful approach to analyze biomolecular structure, mechanism, and dynamics; this is possible because the molecular contacts that define a complicated biomolecular system are governed by the same physical principles (forces, energetics) that characterize individual small molecules, and these simpler systems are relatively well-understood. With modern algorithms and computing capabilities, simulations are now an indispensable tool for examining biomolecular assemblies in atomic detail, from the conformational motion in an individual protein to the diffusional dynamics and inter-molecular collisions in the early stages of formation of cellular-scale assemblies such as the ribosome. This text introduces the physicochemical foundations of molecular simulations and docking, largely from the perspective of biomolecular interactions.
