Structural and functional characterization of TgpA, a critical protein for the viability of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen associated with severe diseases, such as cystic fibrosis. During an extensive search for novel essential genes, we identified tgpA (locus PA2873) in P. aeruginosa PAO1, as a gene playing a critical role in bacterial viability. TgpA, the translated protein, is an internal membrane protein with a periplasmic soluble domain, predicted to be endowed with a transglutaminase-like fold, hosting the Cys404, His448, and Asp464 triad. We report here that Cys404 mutation hampers the essential role of TgpA in granting P. aeruginosa viability. Moreover, we present the crystal structure of the TgpA periplasmic domain at 1.6 Å resolution as a first step towards structure-activity analysis of a new potential target for the discovery of antibacterial compounds.

A Novel Algorithm for Enhanced Structural Motif Matching in Proteins

As widely discussed in literature, spatial patterns of amino acids, so-called structural motifs, play an important role in protein function. The functionally responsible part of proteins often lies in an evolutionarily highly conserved spatial arrangement of only a few amino acids, which are held in place tightly by the rest of the structure. Those recurring amino acid arrangements can be seen as patterns in the three-dimensional space and are known as structural motifs. In general, these motifs can mediate various functional interactions, such as DNA/RNA targeting and binding, ligand interactions, substrate catalysis, and stabilization of the protein structure. Hence, characterizing and identifying such conserved structural motifs can contribute to the understanding of structure-function relationships. Therefore, and because of the rapidly increasing number of solved protein structures, it is highly desirable to identify, understand, and moreover to search for structurally scattered amino acid motifs. This work aims at the development and the implementation of a novel and robust matching algorithm to detect structural motifs in large sets of target structures. The proposed methods were combined and implemented to a feature-rich and easy-to-use command line software tool written in Java.

Proteins comparison through probabilistic optimal structure local alignment

Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.

BetaSearch: a new method for querying β-residue motifs

BACKGROUND

Searching for structural motifs across known protein structures can be useful for identifying unrelated proteins with similar function and characterising secondary structures such as β-sheets. This is infeasible using conventional sequence alignment because linear protein sequences do not contain spatial information. β-residue motifs are β-sheet substructures that can be represented as graphs and queried using existing graph indexing methods, however, these approaches are designed for general graphs that do not incorporate the inherent structural constraints of β-sheets and require computationally-expensive filtering and verification procedures. 3D substructure search methods, on the other hand, allow β-residue motifs to be queried in a three-dimensional context but at significant computational costs.

FINDINGS

We developed a new method for querying β-residue motifs, called BetaSearch, which leverages the natural planar constraints of β-sheets by indexing them as 2D matrices, thus avoiding much of the computational complexities involved with structural and graph querying. BetaSearch exhibits faster filtering, verification, and overall query time than existing graph indexing approaches whilst producing comparable index sizes. Compared to 3D substructure search methods, BetaSearch achieves 33 and 240 times speedups over index-based and pairwise alignment-based approaches, respectively. Furthermore, we have presented case-studies to demonstrate its capability of motif matching in sequentially dissimilar proteins and described a method for using BetaSearch to predict β-strand pairing.

CONCLUSIONS

We have demonstrated that BetaSearch is a fast method for querying substructure motifs. The improvements in speed over existing approaches make it useful for efficiently performing high-volume exploratory querying of possible protein substructural motifs or conformations. BetaSearch was used to identify a nearly identical β-residue motif between an entirely synthetic (Top7) and a naturally-occurring protein (Charcot-Leyden crystal protein), as well as identifying structural similarities between biotin-binding domains of avidin, streptavidin and the lipocalin gamma subunit of human C8.