Touring protein space with Matt.

Using the Matt structure alignment program, we take a tour of protein space, producing a hierarchical clustering scheme that divides protein structural domains into clusters based on geometric dissimilarity. While it was known that purely structural, geometric, distance-based measures of structural similarity, such as Dali/FSSP, could largely replicate hand-curated schemes such as SCOP at the family level, it was an open question as to whether any such scheme could approximate SCOP at the more distant superfamily and fold levels. We partially answer this question in the affirmative, by designing a clustering scheme based on Matt that approximately matches SCOP at the superfamily level, and demonstrates qualitative differences in performance between Matt and DaliLite. Implications for the debate over the organization of protein fold space are discussed. Based on our clustering of protein space, we introduce the Mattbench benchmark set, a new collection of structural alignments useful for testing sequence aligners on more distantly homologous proteins.

Molli: interactive visualization for exploratory protein analysis.

Many programs have been designed to view the 3D structures of protein molecules in 2D. However, three types of linked information haven't been previously defined in a systematic way that highlights the interface design challenge. Specifically, a scientist must have sequence, structure, and homology information in working memory to manipulate and understand a protein structure or related protein structures. Categorizing information types enables the application of classical interaction principles to the design of an intuitive interface for both expert and novice users. In a comparative user evaluation, their Molli system enhances the exploratory process of manipulating proteins of varying complexity by preserving the underlying data's linkages and relations.

Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system.

The recent explosion in newly sequenced bacterial genomes is outpacing the capacity of researchers to try to assign functional annotation to all the new proteins. Hence, computational methods that can help predict structural motifs provide increasingly important clues in helping to determine how these proteins might function. We introduce a Markov Random Field approach tailored for recognizing proteins that fold into mainly beta-structural motifs, and apply it to build recognizers for the beta-propeller shapes. As an application, we identify a potential class of hybrid two-component sensor proteins, that we predict contain a double-propeller domain.