Kreshna Gopal

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.

DETERMINING RELEVANT FEATURES TO RECOGNIZE ELECTRON DENSITY PATTERNS IN X-RAY PROTEIN CRYSTALLOGRAPHY

High-throughput computational methods in X-ray protein crystallography are indispensable to meet the goals of structural genomics. In particular, automated interpretation of electron density maps, especially those at mediocre resolution, can significantly speed up the protein structure determination process. TEXTAL(TM) is a software application that uses pattern recognition, case-based reasoning and nearest neighbor learning to produce reasonably refined molecular models, even with average quality data. In this work, we discuss a key issue to enable fast and accurate interpretation of typically noisy electron density data: what features should be used to characterize the density patterns, and how relevant are they? We discuss the challenges of constructing features in this domain, and describe SLIDER, an algorithm to determine the weights of these features. SLIDER searches a space of weights using ranking of matching patterns (relative to mismatching ones) as its evaluation function. Exhaustive search being intractable, SLIDER adopts a greedy approach that judiciously restricts the search space only to weight values that cause the ranking of good matches to change. We show that SLIDER contributes significantly in finding the similarity between density patterns, and discuss the sensitivity of feature relevance to the underlying similarity metric.

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.

Efficient retrieval of electron density patterns for modeling proteins by X-ray crystallography

Inefficient case retrieval is a major problem in many case-based reasoning systems, especially when case matching is expensive and the case-base is large. In this paper, we present a two-phase approach where an inexpensive feature-based method is used to jind a set of potential matches and a more expensive and accurate case matching method is used to make the jinal selection. This approach has been successfully employed in TEXTALTM, a system that retrieves previously solved 3D patterns of electron density from a database to determine the structure of proteins. Electron density patterns are characterized by numeric features and an appropriate distance measure is used to efficiently jilter good matches through an exhaustive search of the database. These matches are then examined using a computationally expensive density correlation procedure based on jinding an optimal superposition between 3D patterns. We provide an empirical and theoretical analysis of some of the keys issues related to this method. In particular, we dejine a model for estimating how approximate various featurebased similarity measures are (relative to an objective matching metric), and determine its relation to the number of cases that should be jiltered from a given database to make the approach effective.

Distance Metric Learning through Optimization of Ranking

The integration of climate, satellite, ocean, and biophysical data holds considerable potential for enhancing our drought monitoring and prediction capabilities beyond the tools that currently exist. Improvements in meteorological observations and prediction methods, increased accuracy of seasonal forecasts using oceanic indicators, and advancements in satellite-based remote sensing have greatly enhanced our capability to monitor vegetation conditions and develop better drought early warning and knowledge-based decision support systems. In this paper, a new prediction tool called the Vegetation Outlook (VegOut) is presented. The VegOut integrates climate, oceanic, and satellite-based vegetation indicators and utilizes a regression tree data mining technique to identify historical patterns between drought intensity and vegetation conditions and predict future vegetation conditions based on these patterns at multiple time steps (2-, 4-, and 6-week outlooks). Cross-validation (withholding years) revealed that the seasonal VegOut models had relatively high prediction accuracy. Correlation coefficient (R ) values ranged from 0.94 to 0.98 for 2-week, 0.86 to 0.96 for 4-week, and 0.79 to 0.94 for 6-week predictions. The spatial patterns of predicted vegetation conditions also had relatively strong agreement with the observed patterns from satellite at each of the time steps evaluated.Data preprocessing is important in machine learning, data mining, and pattern recognition. In particular, selecting relevant features in high- dimensional data is often necessary to efficiently construct models that accurately describe the data. For example, many lazy learning algorithms (like k- Nearest Neighbor) rely on feature-based distance metrics to compare input patterns for the purpose of classification or retrieval from a database. In previous work, we introduced Slider, a distance metric learning method that optimizes the weights of features in a protein model-building application (where features are used to describe patterns of electron density around protein macromolecules). In this work, we demonstrate the usefulness of Slider as a general method for classification, ranking and retrieval, with results on several benchmark datasets. We also compare it to other well-known feature selection or weighting methods.

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

Weighting features to recognize 3D patterns of electron density in X-ray protein crystallography

Feature selection and weighting are central problems in pattern recognition and instance-based learning. In this work, we discuss the challenges of constructing and weighting features to recognize 3D patterns of electron density to determine protein structures. We present SLIDER, a feature-weighting algorithm that adjusts weights iteratively such that patterns that match query instances are better ranked than mismatching ones. Moreover, SLIDER makes judicious choices of weight values to be considered in each iteration, by examining specific weights at which matching and mismatching patterns switch as nearest neighbors to query instances. This approach reduces the space of weight vectors to be searched. We make the following two main observations: (1) SLIDER efficiently generates weights that contribute significantly in the retrieval of matching electron density patterns; (2) the optimum weight vector is sensitive to the distance metric i.e. feature relevance can be, to a certain extent, sensitive to the underlying metric used to compare patterns.

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.

Database Approaches and Data Representation in Structural Bioinformatics

Database approaches are widely used in structural bioinformatics, since ab initio techniques are often computationally prohibitive, and the structure of biological macromolecules are typically derived from a limited set of motifs. There are several issues and challenges that arise when developing methods to enable efficient database retrieval. For example, how can complex data be represented efficiently, and what should be the size and composition of the database? In this work, we discuss some of these challenges, based on a crystallographic protein model-building program called TEXTAL. In particular, we discuss how structural information on amino acids is represented (as numeric features), how difficult it is to recognize amino acids (based on 3D electron density patterns), and what types of examples (and how many of them) need to be stored in the database. These insights are potentially useful in many other related applications, such as structure-based drug design, protein-protein interaction, discriminating nucleic acids and proteins in hybrid complexes, etc.