PerMemDB is currently the most complete and comprehensive repository of data for peripheral membrane proteins from all reference eukaryotic proteomes deposited in UniProt or predicted with the use of MBPpred, an algorithm developed in our lab that specializes in the detection of membrane binding proteins. The database contains 231770 peripheral membrane proteins from 1009 organisms. All entries have cross-references to other databases, literature references and annotation regarding their interactions with other proteins. Moreover, entries collected with the use of MBPpred have additional information, regarding their characteristic domains that allow them to interact with membranes. [Read More]

The database is freely available at http://bioinformatics.biol.uoa.gr/db=permemdb

JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the software on various biological problems. Importantly, the JUCHMME toolkit includes several additional features that allow for easy building and evaluation of custom HMMs, which could be a useful resource for the research community. [Read More]

The method is freely available at http://www.compgen.org/tools/juchmme

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. [Read More]

The method is freely available at http://bioinformatics.biol.uoa.gr/MBPpred

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. [Read More]

The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM

G-protein coupled receptors form the largest and most diverse superfamily of transmembrane receptors in eykaryotic cells. All known GPCRs share common topology, which consists of 7 transmembrane α-helices and extracellular N-terminals. Even though GPCRs share common architecture and function, they show important diversity at sequence level, and, therefore are divided into families. This lack of sequence similarity makes difficult the detection of GPCRs in proteomes, especially, the finding of novel members of the GPCR superfamily. In this work, we developed a pipeline for the accurate detection of GPCRs in proteomes called GPCRpipe. [Read More]

The method is freely available at http://bioinformatics.biol.uoa.gr/

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 protein's 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. [Read More]

The database is freely available at http://bioinformatics.biol.uoa.gr/mpMoRFsDB

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 alpha-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 alpha-helical transmembrane proteins. Topological information is combined with transmembrane topology prediction resulting in more reliable topological models. [Read More]

The database is freely available at http://bioinformatics.biol.uoa.gr/ExTopoDB