Reetal Pai

Recent developments in the PHENIX software for automated crystallographic structure determination

A new software system called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) is being developed for the automation of crystallographic structure solution. This will provide the necessary algorithms to proceed from reduced intensity data to a refined molecular model, and facilitate structure solution for both the novice and expert crystallographer. Here, the features of PHENIXare reviewed and the recent advances in infrastructure and algorithms are briefly described.

Automated structure determination with phenix

A new software system called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) has been developed for the automation of crystallographic structure solution. This provides algorithms to go from reduced intensity data to a refined molecular model, and facilitates structure solution for both the novice and expert crystallographer. Here, we review the major features of PHENIX, including the different user interfaces, and briefly describe the recent advances in infrastructure and algorithms.

TEXTAL: AI-based structural determination for X-ray protein crystallography

TEXTAL is a successfully deployed system for automated model-building in protein X-ray crystallography. It represents a novel solution to an important, complex real-world, problem using various AI and pattern recognition algorithms. TEXTAL takes a model-building approach based on real-space density pattern recognition, similar to how a human crystallographer would work. TEXTAL first tries to predict the coordinates of the alpha-carbon (C/spl alpha/) atoms in the proteins connected backbone using a neural network. It then analyzes the density patterns around each C/spl alpha/ atom and searches a database of previously solved structures for regions with similar patterns. TEXTAL determines the best match, retrieves the coordinates for that region, and fits them to the unknown density. TEXTAL concatenates these local models into a global model and subjects them to various subsequent refinements to produce a complete protein model automatically.

Crystallographic protein model-building on the web

UNLABELLED X-ray crystallography is the most widely used method to determine the 3D structure of protein molecules. One of the most difficult steps in protein crystallography is model-building, which consists of constructing a backbone and then amino acid side chains into an electron density map. Interpretation of electron density maps represents a major bottleneck in protein structure determination pipelines, and thus, automated techniques to interpret maps can greatly improve the throughput. We have developed WebTex, a simple and yet powerful web interface to TEXTAL, a program that automates this process of fitting atoms into electron density maps. TEXTAL can also be downloaded for local installation. AVAILABILITY Web interface, downloadable binaries and documentation at http://textal.tamu.edu

Identifying non-crystallographic symmetry in protein electron-density maps: a feature-based approach

Non-crystallographic symmetry (NCS) averaging is a well known method for improving the quality of an electron-density map and thus aiding structure determination. Prior methods of NCS-operator determination based on estimated heavy-atom positions are prone to errors arising from inaccuracies in these coordinates or differences in the relative orientations of domains between molecules. In this paper, two real-space methods to determine NCS relationships from initial electron-density maps are presented. A brute-force method identifies matching regions in a map by local density correlation. A feature-based algorithm uses rotation-invariant features to reduce the computational time taken by the brute-force algorithm by filtering out regions that are likely to have dissimilar density patterns. This makes the feature-based algorithm faster and as accurate as the brute-force approach. Neither method requires the positions of heavy atoms or any information regarding the protein sequence. Both methods have been tested on a diverse range of experimentally phased maps and the correct NCS relationships were accurately identified for almost all of the test cases. The NCS operators obtained by the feature-based algorithm were used to perform NCS averaging and an improvement in map correlation was observed for some cases.

TEXTAL: Crystallographic Protein Model Building Using AI and Pattern Recognition

TEXTAL is a computer program that automatically interprets electron density maps to determine the atomic structures of proteins through X-ray crystallography. Electron density maps are traditionally interpreted by visually fitting atoms into density patterns. This manual process can be time-consuming and error prone, even for expert crystallographers. Noise in the data and limited resolution make map interpretation challenging. To automate the process, TEXTAL employs a variety of AI and pattern-recognition techniques that emulate the decision-making processes of domain experts. In this article, we discuss the various ways AI technology is used in TEXTAL, including neural networks, case-based reasoning, nearest neighbor learning and linear discriminant analysis. The AI and pattern-recognition approaches have proven to be effective for building protein models even with medium resolution data. TEXTAL is a successfully deployed application; it is being used in more than 100 crystallography labs from 20 countries.