A computer system to perform structure comparison using TOPS representations of protein structure

We describe the design and implementation of a fast topology–based method for protein structure comparison. The approach uses the TOPS topological representation of protein structure, aligning two structures using a common discovered pattern and generating measure of distance derived from an insert score. Heavy use is made of a constraint-based pattern matching algorithm for TOPS diagrams that we have designed and described elsewhere Gilbert et al. (1999). The comparison system is maintained at the European Bioinformatics Institute and is available over the Web via the at tops.ebi.ac.uk/tops. Users submit a structure description in Protein Data Bank (PDB) format and can compare it with structures in the entire PDB or a representative subset of protein domains, receiving the results by email

FFAS server: novel features and applications.

The Fold and Function Assignment System (FFAS) server [Jaroszewski et al. (2005) FFAS03: a server for profile-profile sequence alignments. Nucleic Acids Research, 33, W284-W288] implements the algorithm for protein profile-profile alignment introduced originally in [Rychlewski et al. (2000) Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Science: a Publication of the Protein Society, 9, 232-241]. Here, we present updates, changes and novel functionality added to the server since 2005 and discuss its new applications. The sequence database used to calculate sequence profiles was enriched by adding sets of publicly available metagenomic sequences. The profile of a user's protein can now be compared with ∼20 additional profile databases, including several complete proteomes, human proteins involved in genetic diseases and a database of microbial virulence factors. A newly developed interface uses a system of tabs, allowing the user to navigate multiple results pages, and also includes novel functionality, such as a dotplot graph viewer, modeling tools, an improved 3D alignment viewer and links to the database of structural similarities. The FFAS server was also optimized for speed: running times were reduced by an order of magnitude. The FFAS server, http://ffas.godziklab.org, has no log-in requirement, albeit there is an option to register and store results in individual, password-protected directories. Source code and Linux executables for the FFAS program are available for download from the FFAS server

CATHEDRAL: A Fast and Effective Algorithm to Predict Folds and Domain Boundaries from Multidomain Protein Structures

We present CATHEDRAL, an iterative protocol for determining the location of previously observed protein folds in novel multidomain protein structures. CATHEDRAL builds on the features of a fast secondary-structure–based method (using graph theory) to locate known folds within a multidomain context and a residue-based, double-dynamic programming algorithm, which is used to align members of the target fold groups against the query protein structure to identify the closest relative and assign domain boundaries. To increase the fidelity of the assignments, a support vector machine is used to provide an optimal scoring scheme. Once a domain is verified, it is excised, and the search protocol is repeated in an iterative fashion until all recognisable domains have been identified. We have performed an initial benchmark of CATHEDRAL against other publicly available structure comparison methods using a consensus dataset of domains derived from the CATH and SCOP domain classifications. CATHEDRAL shows superior performance in fold recognition and alignment accuracy when compared with many equivalent methods. If a novel multidomain structure contains a known fold, CATHEDRAL will locate it in 90% of cases, with <1% false positives. For nearly 80% of assigned domains in a manually validated test set, the boundaries were correctly delineated within a tolerance of ten residues. For the remaining cases, previously classified domains were very remotely related to the query chain so that embellishments to the core of the fold caused significant differences in domain sizes and manual refinement of the boundaries was necessary. To put this performance in context, a well-established sequence method based on hidden Markov models was only able to detect 65% of domains, with 33% of the subsequent boundaries assigned within ten residues. Since, on average, 50% of newly determined protein structures contain more than one domain unit, and typically 90% or more of these domains are already classified in CATH, CATHEDRAL will considerably facilitate the automation of protein structure classification

Accurate Protein Structure Annotation through Competitive Diffusion of Enzymatic Functions over a Network of Local Evolutionary Similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks