From "wet biology" to statistical analysis of structural features with bioinformatics tools

Many new Escherichia coli outer membrane proteins have recently been identified by proteomics techniques. However, poorly expressed proteins and proteins expressed only under certain conditions may escape detection when wild-type cells are grown under standard conditions. Here, we have taken a complementary approach where candidate outer membrane proteins have been identified by bioinformatics prediction, cloned and overexpressed, and finally localized by cell fractionation experiments. Out of eight predicted outer membrane proteins, we have confirmed the outer membrane localization for five—YftM, YaiO, YfaZ, CsgF, and YliI—and also provide preliminary data indicating that a sixth—YfaL—may be an outer membrane autotransporter.

Structural and functional characterization of Group B Streptococcus class C sortases

In Group B Streptococcus (GBS) three structurally distinct types of pili have been discovered as potential virulence factors and vaccine candidates. The pilus-forming proteins are assembled into high-molecular weight polymers via a transpeptidation mechanism mediated by specific class C sortases. Using a multidisciplinary approach including bioinformatics, structural and biochemical studies and in vivo mutagenesis we performed a broad characterization of GBS sortase C. The high resolution X-ray structure of the enzymes revealed that the active site, located into the β-barrel core of the enzyme, is made of the catalytic triad His157-Cys219-Arg228 and covered by a loop, known as the “lid”. We show that the catalytic triad and the predicted N- and C-terminal trans-membrane regions are required for the enzyme activity. Interestingly, by in vivo complementation mutagenesis studies we found that the deletion of the entire lid loop or mutations in specific lid key residues had no effect on catalytic activity of the enzyme. In addition, kinetic characterizations of recombinant enzymes indicate that the lid mutants can still recognize and cleave the substrate-mimicking peptide at least as well as the wild type protein.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.