Georgios N. Tsaousis

ExTopoDB: a database of experimentally derived topological models of transmembrane proteins

UNLABELLED ExTopoDB is a publicly accessible database of experimentally derived topological models of transmembrane proteins. It contains information collected from studies in the literature that report the use of biochemical methods for the determination of the topology of α-helical transmembrane proteins. Transmembrane protein topology is highly important in order to understand their function and ExTopoDB provides an up to date, complete and comprehensive dataset of experimentally determined topologies of α-helical transmembrane proteins. Topological information is combined with transmembrane topology prediction resulting in more reliable topological models. AVAILABILITY http://bioinformatics.biol.uoa.gr/ExTopoDB.

HMMpTM: improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction.

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.

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.

Molecular predictive markers in tumors of the gastrointestinal tract

Gastrointestinal malignancies are among the leading causes of cancer-related deaths worldwide. Like all human malignancies they are characterized by accumulation of mutations which lead to inactivation of tumor suppressor genes or activation of oncogenes. Advances in Molecular Biology techniques have allowed for more accurate analysis of tumors’ genetic profiling using new breakthrough technologies such as next generation sequencing (NGS), leading to the development of targeted therapeutical approaches based upon biomarker-selection. During the last 10 years tremendous advances in the development of targeted therapies for patients with advanced cancer have been made, thus various targeted agents, associated with predictive biomarkers, have been developed or are in development for the treatment of patients with gastrointestinal cancer patients. This review summarizes the advances in the field of molecular biomarkers in tumors of the gastrointestinal tract, with focus on the available NGS platforms that enable comprehensive tumor molecular profile analysis.

mpMoRFsDB: a database of molecular recognition features in membrane proteins

SUMMARY Molecular recognition features (MoRFs) are small, intrinsically disordered regions in proteins that undergo a disorder-to-order transition on binding to their partners. MoRFs are involved in protein-protein interactions and may function as the initial step in molecular recognition. The aim of this work was to collect, organize and store all membrane proteins that contain MoRFs. Membrane proteins constitute ∼30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. MoRFs were classified according to their secondary structure, after interacting with their partners. We identified MoRFs in transmembrane and peripheral membrane proteins. The position of transmembrane protein MoRFs was determined in relation to a proteins topology. All information was stored in a publicly available mySQL database with a user-friendly web interface. A Jmol applet is integrated for visualization of the structures. mpMoRFsDB provides valuable information related to disorder-based protein-protein interactions in membrane proteins. AVAILABILITY http://bioinformatics.biol.uoa.gr/mpMoRFsDB

Evidence for association of the rs605059 polymorphism of HSD17B1 gene with recurrent spontaneous abortions

Abstract Objective: To investigate whether the missense rs605059 polymorphism of HSD17B1 gene, which is expressed mainly in the placenta, is associated with recurrent spontaneous abortions (RSA). Methods: This study group consisted of 138 women with three or more unexplained spontaneous abortions, before the 20th week of gestation, with the same partner, while 140 healthy women served as controls. To genotype the individuals, we used the polymerase chain reaction-restriction fragment length polymorphism method. Results: The genotyping of the rs605059 polymorphism revealed the frequencies 0.22, 0.45 and 0.33, for AA, GA and GG genotypes, respectively, for the patient group and 0.37, 0.41 and 0.22, respectively, for the control group. The A allele frequencies were 0.44 and 0.57 for the patient and control group, respectively, and the G allele frequencies were 0.56 and 0.43 for the patient and control group, respectively. Statistical analysis of the results indicated the existence of significant differences in genotype and allele frequencies between the two groups. Conclusion: The rs605059 polymorphism of the HSD17B1 gene is associated with increased risk of RSA in our Caucasian Greek population. Thus it could be used as a prognostic genetic marker for RSA.

How Many 3D Structures Do We Need to Train a Predictor

It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both α-helical and β-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of α-helical membrane proteins perform slightly better than that of β-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a meta-analysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors.

MBPpred: Proteome-wide detection of membrane lipid-binding proteins using profile Hidden Markov Models.

A large number of modular domains that exhibit specific lipid binding properties are present in many membrane proteins involved in trafficking and signal transduction. These domains are present in either eukaryotic peripheral membrane or transmembrane proteins and are responsible for the non-covalent interactions of these proteins with membrane lipids. Here we report a profile Hidden Markov Model based method capable of detecting Membrane Binding Proteins (MBPs) from information encoded in their amino acid sequence, called MBPpred. The method identifies MBPs that contain one or more of the Membrane Binding Domains (MBDs) that have been described to date, and further classifies these proteins based on their position in respect to the membrane, either as peripheral or transmembrane. MBPpred is available online at http://bioinformatics.biol.uoa.gr/MBPpred. This method was applied in selected eukaryotic proteomes, in order to examine the characteristics they exhibit in various eukaryotic kingdoms and phyla.

Comprehensive BRCA mutation analysis in the Greek population. Experience from a single clinical diagnostic center

Germline mutations in the BRCA1 and BRCA2 genes are associated with hereditary predisposition to breast and ovarian cancer. Sensitive and accurate detection of BRCA1 and BRCA2 mutations is crucial for personalized clinical management of individuals affected by breast or ovarian cancer, and for the identification of at-risk healthy relatives. We performed molecular analysis of the BRCA1 and BRCA2 genes in 898 Greek families, using Sanger sequencing or Next Generation Sequencing for the detection of small insertion/deletion frameshift, nonsynonymous, truncating and splice-site alterations and MLPA for the detection of large genomic rearrangements. In total, a pathogenic mutation was identified in 12.9% of 898 families analyzed. Of the 116 mutations identified in total 9% were novel and 14.7% were large genomic rearrangements. Our results indicate that different types of mutational events in the BRCA1 and BRCA2 genes are responsible for the hereditary component of breast/ovarian cancer in the Greek population. Therefore the methodology used in the analysis of Greek patients must be able to detect both point and small frameshift mutations in addition to large genomic rearrangements across the entire coding region of the two genes.

Tumor molecular profiling of NSCLC patients using next generation sequencing

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and a tumor with a broad spectrum of targeted therapies already available or in clinical trials. Thus, molecular characterization of the tumor using next generation sequencing (NGS) technology, has become a key tool for facilitating treatment decisions and the clinical management of NSCLC patients. The performance of a custom 23 gene multiplex amplification hot spot panel, based on Ion AmpliSeq™ technology, was evaluated for the analysis of tumor DNA extracted from formalin-fixed and paraffin-embedded (FFPE) tissues. Furthermore, the Ion AmpliSeq™ RNA Fusion Lung Cancer Research Panel was used for fusion RNA transcript analysis. The mutation spectrum of the tumors was determined in a cohort of 502 patients with NSCLC using the aforementioned targeted gene panels. The panel used for tumor DNA analysis in this study exhibited high rates (100%) of sensitivity, specificity and reproducibility at a mutation allelic frequency of 3%. At least one DNA mutation was detected in 374 patients (74.5%) and an RNA fusion was identified in 16 patients, (3.2%). In total, alterations in a cancer-driver gene were identified (including point mutations, gene rearrangements and MET amplifications) in 77.6% of the tumors tested. Among the NSCLC patients, 23% presented a mutation in a gene associated with approved or emerging targeted therapy. More specifically, 13.5% (68/502) presented a mutation in a gene with approved targeted therapy (EGFR, ALK, ROS1) and 9.4% (47/502) had an alteration in a gene related to emerging targeted therapies (ERBB2, BRAF, MET and RET). Furthermore, 51.6% of the patients had a mutation in a gene that could be related to an off label therapy or indicative for access to a clinical trial. Thus, the targeted NGS panel used in this study is a reliable approach for tumor molecular profiling and can be applied in personalized treatment decision making for NSCLC patients.