Biomedical subjects

Donald Petrey

Publications and source records attributed to Donald Petrey.

4 recordsLinked to original sources

PubMed2006

Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction.

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.

Computational Biology↗

PubMed2005

Protein structure prediction: inroads to biology.

In recent years, there has been significant progress in the ability to predict the three-dimensional structure of proteins from their amino acid sequence. Progress has been due to new methods to extract the growing amount of information in sequence and structure databases and improved computational descriptions of protein energetics. This review summarizes recent advances in these areas and describes a number of novel biological applications made possible by structure prediction. Despite remaining challenges, protein structure prediction is becoming an extremely useful tool in understanding phenomena in modern molecular and cell biology.

Algorithms↗

PubMed2003

Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling.

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST-a fast model building program that applies an "artificial evolution" algorithm to construct a model from a given template and alignment. c) GRASP2-a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided.

Algorithms↗

PubMed2003

GRASP2: visualization, surface properties, and electrostatics of macromolecular structures and sequences.

The widespread use of the original version of GRASP revealed the importance of the visualization of physicochemical and structural properties on the molecular surface. This chapter describes a new version of GRASP that contains many new capabilities. In terms of analysis tools, the most notable new features are sequence and structure analysis and alignment tools and the graphical integration of sequence and structural information. Not all the new GRASP2 could be described here and more capabilities are continually being added. An on-line manual, details on obtaining the software, and technical notes about the program and the Troll software library can be found at the Honig laboratory Web site (http://trantor.bioc.columbia.edu).

Computational Biology↗