Biomedical subjects

Tod D Romo

Publications and source records attributed to Tod D Romo.

4 recordsLinked to original sources

PubMed2006

Improving amino-acid identification, fit and C(alpha) prediction using the Simplex method in automated model building.

Automated methods for protein model building in X-ray crystallography typically use a two-phased approach that involves first modeling the protein backbone followed by building in the side chains. The latter phase requires the identification of the amino-acid side-chain type as well as fitting of the side-chain model into the observed electron density. While mistakes in identification of individual side chains are common for a number of reasons, sequence alignment can sometimes be used to correct errors by mapping fragments into the true (expected) amino-acid sequence and exploiting contiguity constraints among neighbors. However, side chains cannot always be confidently aligned; this depends on having sufficient accuracy in the initial calls. The recognition of amino-acid side-chains based on the surrounding pattern of electron density, whether by features, density correlation or free atoms, can be sensitive to inaccuracies in the coordinates of the predicted backbone C(alpha) atoms to which they are anchored. By incorporating a Nelder-Mead Simplex search into the side-chain identification and model-building routines of TEXTAL, it is demonstrated that this form of residue-by-residue rigid-body real-space refinement (in which the C(alpha) itself is allowed to shift) can improve the initial accuracy of side-chain selection by over 25% on average (from 25% average identity to 32% on a test set of five representative proteins, without corrections by sequence alignment). This improvement in amino-acid selection accuracy in TEXTAL is often sufficient to bring the pairwise amino-acid identity of chains in the model out of the so-called ;twilight zone' for sequence-alignment methods. When coupled with sequence alignment, use of the Simplex search yielded improvements in side-chain accuracy on average by over 13 percentage points (from 64 to 77%) and up to 38 percentage points (from 40 to 78%) in one case compared with using sequence alignment alone.

Amino Acid Sequence↗

PubMed2005

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.

Absorptiometry, Photon↗

PubMed2004

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.

Absorptiometry, Photon↗

PubMed2003

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.

Algorithms↗