Abstract
Publisher Summary TEXTAL is a new computer program designed to build protein structures automatically from electron density maps. It uses artificial intelligence (AI) and pattern recognition techniques to try to emulate the intuitive decision making of experts in solving protein structures. TEXTAL can be a great time-saving benefit to crystallographers by automatically building accurate models for electron density maps. TEXTAL is aimed at solving maps in the 2.5–3.5 A range that are just at the range of human interpretability, but in practice, TEXTAL is aimed at solving maps constructed from multiwavelength anomalous dispersion (MAD) data. TEXTAL has the potential to reduce one of the bottlenecks of high-throughput structural genomics. As an automated model-building system, it takes an electron-density map as input and ultimately outputs a protein model (with atomic coordinates). The basic idea behind pattern recognition (at least for supervised learning) is to train the system by giving it labeled examples in several competing categories (such as tanks vs. civilian cars and trucks). Each example is represented by a set of descriptive features, which are measurements derived from the data source that characterize unique aspects of each one (for example, color and size).
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call