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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.
Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. Simple models defined by contact topology, known as elastic network models, have been used to model a variety of systems, but the validatio
SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algor
Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The design of large scale and complex biological structur
Large, complex, multi-scale, multi-physics simulation codes, running on high performance com-puting (HPC) platforms, have become essential to advancing science and engineering. These codes simulate multi-scale, multi-physics phenomena with unpreceden
The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less t