E. Gail Hutchinson

Protein folds and functions

BACKGROUND The recent rapid increase in the number of available three-dimensional protein structures has further highlighted the necessity to understand the relationship between biological function and structure. Using structural classification schemes such as SCOP, CATH and DALI, it is now possible to explore global relationships between protein fold and function, something which was previously impractical. RESULTS Using a relational database of CATH data we have generated fold distributions for arbitrary selections of proteins automatically. These distributions have been examined in the light of protein function and bound ligand. Different enzyme classes are not clearly reflected in distributions of protein class and architecture, whereas the type of bound ligand has a much more dramatic effect. CONCLUSIONS The availability of structural classification data has enabled this novel overview analysis. We conclude that function at the top level of the EC number enzyme classification is not related to fold, as only a very few specific residues are actually responsible for enzyme activity. Conversely, the fold is much more closely related to ligand type.

Contemporary approaches to protein structure classification

In a similar manner to sequence database searching, it is also possible to compare three‐dimensional protein structures. Such methods can be extremely useful because a structural similarity may represent a distant evolutionary relationship that is undetectable by sequence analysis. In this review, we summarise the most popular structure comparison methods, show how they can be used for database searching, and then describe some of the most advanced attempts to develop comprehensive protein structure classifications. With such data, it is possible to identify distant evolutionary relationships, provide libraries of unique folds for structure prediction, estimate the total number of folds that exist, and investigate the preference for certain types of structures over others. BioEssays 20:884–891, 1998.

Toward predicting protein topology: An approach to identifying β hairpins

Although secondary structure prediction methods have recently improved, progress from secondary to tertiary structure prediction has been limited. A promising but largely unexplored route to this goal is to predict structure motifs from secondary structure knowledge. Here we present a novel method for the recognition of β hairpins that combines secondary structure predictions and threading methods by using a database search and a neural network approach. The method successfully predicts 48 and 77%, respectively, of all of hairpin and nonhairpin β-coil-β motifs in a protein database. We find that the main contributors to motif recognition are predicted accessibility and turn propensities.