Zoi I. Litou

PredSL: A Tool for the N-terminal Sequence-based Prediction of Protein Subcellular Localization

The ability to predict the subcellular localization of a protein from its sequence is of great importance, as it provides information about the protein’s function. We present a computational tool, PredSL, which utilizes neural networks, Markov chains, profile hidden Markov models, and scoring matrices for the prediction of the subcellular localization of proteins in eukaryotic cells from the N-terminal amino acid sequence. It aims to classify proteins into five groups: chloroplast, thylakoid, mitochondrion, secretory pathway, and “other”. When tested in a five-fold cross-validation procedure, PredSL demonstrates 86.7% and 87.1% overall accuracy for the plant and non-plant datasets, respectively. Compared with TargetP, which is the most widely used method to date, and LumenP, the results of PredSL are comparable in most cases. When tested on the experimentally verified proteins of the Saccharomyces cerevisiae genome, PredSL performs comparably if not better than any available algorithm for the same task. Furthermore, PredSL is the only method capable for the prediction of these subcellular localizations that is available as a stand-alone application through the URL: http://bioinformatics.biol.uoa.gr/PredSL/.

PRED-GPCR: GPCR recognition and family classification server

The vast cell-surface receptor family of G-protein coupled receptors (GPCRs) is the focus of both academic and pharmaceutical research due to their key role in cell physiology along with their amenability to drug intervention. As the data flow rate from the various genome and proteome projects continues to grow, so does the need for fast, automated and reliable screening for new members of the various GPCR families. PRED-GPCR is a free Internet service for GPCR recognition and classification at the family level. A submitted sequence or set of sequences, is queried against the PRED-GPCR library, housing 265 signature profile HMMs corresponding to 67 well-characterized GPCR families. Users query the server through a web interface and results are presented in HTML output format. The server returns all single-motif matches along with the combined results for the corresponding families. The service is available online since October 2003 at http://bioinformatics.biol.uoa.gr/PRED-GPCR.

Prediction of cell wall sorting signals in gram-positive bacteria with a hidden markov model: application to complete genomes.

Surface proteins in Gram-positive bacteria are frequently implicated in virulence. We have focused on a group of extracellular cell wall-attached proteins (CWPs), containing an LPXTG motif for cleavage and covalent coupling to peptidoglycan by sortase enzymes. A hidden Markov model (HMM) approach for predicting the LPXTG-anchored cell wall proteins of Gram-positive bacteria was developed and compared against existing methods. The HMM model is parsimonious in terms of the number of freely estimated parameters, and it has proved to be very sensitive and specific in a training set of 55 experimentally verified LPXTG-anchored cell wall proteins as well as in reliable data sets of globular and transmembrane proteins. In order to identify such proteins in Gram-positive bacteria, a comprehensive analysis of 94 completely sequenced genomes has been performed. We identified, in total, 860 LPXTG-anchored cell wall proteins, a number that is significantly higher compared to those obtained by other available methods. Of these proteins, 237 are hypothetical proteins according to the annotation of SwissProt, and 88 had no homologs in the SwissProt database--this might be evidence that they are members of newly identified families of CWPs. The prediction tool, the database with the proteins identified in the genomes, and supplementary material are available online at http://bioinformatics.biol.uoa.gr/CW-PRED/.

CW-PRED: a HMM-Based method for the classification of cell wall-anchored proteins of gram-positive bacteria

Gram-positive bacteria have surface proteins that are often implicated in virulence. A group of extracellular proteins attached to the cell wall contains an LPXTG-like motif that is target for cleavage and covalent coupling to peptidoglycan by sortase enzymes. A Hidden Markov Model (HMM) was developed for predicting the LPXTG and LPXTG-like cell-wall proteins of Gram-positive bacteria. The model is the first capable of predicting alternative (i.e. other than LPXTG-containing) substrates. Our analysis of 177 completely sequenced genomes identified 1456 cell-wall proteins, a number larger compared to the previously available methods. Among these, apart from the previously identified 1283 proteins carrying the LPXTG motif, we identified 39 newly identified proteins carrying NPXTG, 53 carrying LPXTA and 81 carrying the LAXTG motif. The tool is freely available for academic use at http://bioinformatics.biol.uoa.gr/CW-PRED/.

The human plasma membrane peripherome: visualization and analysis of interactions.

A major part of membrane function is conducted by proteins, both integral and peripheral. Peripheral membrane proteins temporarily adhere to biological membranes, either to the lipid bilayer or to integral membrane proteins with noncovalent interactions. The aim of this study was to construct and analyze the interactions of the human plasma membrane peripheral proteins (peripherome hereinafter). For this purpose, we collected a dataset of peripheral proteins of the human plasma membrane. We also collected a dataset of experimentally verified interactions for these proteins. The interaction network created from this dataset has been visualized using Cytoscape. We grouped the proteins based on their subcellular location and clustered them using the MCL algorithm in order to detect functional modules. Moreover, functional and graph theory based analyses have been performed to assess biological features of the network. Interaction data with drug molecules show that ~10% of peripheral membrane proteins are targets for approved drugs, suggesting their potential implications in disease. In conclusion, we reveal novel features and properties regarding the protein-protein interaction network created by peripheral proteins of the human plasma membrane.

N-terminal sequence-based prediction of subcellular location

Different compartments in a cell perform diverse tasks, thus knowledge of the localization of a protein would be highly indicative of its function. Many proteins have an Nterminal sequence of approximately 20–50 residues, which is responsible for their targeting to the appropriate location and is cleaved off, after the protein has been inserted into the organelle. Such information can be used in computational methods predicting protein localization. There are several methods for prediction of protein subcellular location available on the web, with TargetP (Emanuelsson et al 2000) being the most widely used. PredSL is a software tool, which aims to classify proteins to five subcellular locations: chloroplast, thylakoid, mitochondrion, secretory pathway and other. It combines neural networks, Markov chains, HMMs and scoring matrices in order to identify a targeting sequence at the N-terminal of a protein sequence, and determine its type (chloroplast-cTP, mitochondrial-mTP, secreted-SP, thylakoidallTP) and the precise location of the cleavage site. PredSL was tested on a set of 732 plant protein sequences and 637 non-plant sequences and the overall accuracy was 90.4% for the plant set and 93.3% for the non-plant set. Compared to the results obtained by TargetP when tested on the same datasets, PredSLs performance was better by 1.6% for the plant sequences and slightly worse (0.2%) for the non-plant sequences. For the prediction of thylakoid proteins PredSL was tested by cross-validation and achieved 87.3% accuracy compared to 87% by LumenP. PredSL is the only method for protein subcellular localization prediction that is available by the authors as a free, stand-alone tool, or through the URL: http://bioinformat ics.biol.uoa.gr/PredSL. from BioSysBio: Bioinformatics and Systems Biology Conference Edinburgh, UK, 14–15 July 2005