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Summary 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. Availability and implementation http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. Supplementary information Supplementary data are available at Bioinformatics online.

Voltage-gated ion channels (VGICs) are one of the largest groups of transmembrane proteins. Due to their major role in the generation and propagation of electrical signals, VGICs are considered important from a medical viewpoint, and their dysfunction is often associated with Channelopathies. We identified disease-associated mutations and polymorphisms in these proteins through mapping missense single-nucleotide polymorphisms from the UniProt and ClinVar databases on their amino acid sequence, considering their special topological and functional characteristics. Statistical analysis revealed that disease-associated SNPs are mostly found in the voltage sensor domain and the pore loop. Both of these regions are extremely important for the activation and ion conductivity of VGICs. Moreover, among the most frequently observed mutations are those of arginine to glutamine, to histidine or to cysteine, which can probably be attributed to the extremely important role of arginine residues in the regulation of membrane potential in these proteins. We suggest that topological information in combination with genetic variation data can contribute toward a better evaluation of the effect of currently unclassified mutations in VGICs. It is hoped that potential associations with certain disease phenotypes will be revealed in the future with the use of similar approaches.

Ligand-Gated Ion Channels (LGICs) are one of the largest groups of transmembrane proteins. Due to their major role in synaptic transmission, both in the nervous system and the somatic neuromuscular junction, LGICs present attractive therapeutic targets. During the last few years several computational methods for the detection of LGICs have been developed. These methods are based on machine learning approaches utilizing features extracted solely from amino acid composition. However, special topological characteristics of these proteins have not been utilized to date, which results in weaknesses regarding the correct class categorization of predicted proteins. Here we report the development of LiGIoNs, a profile Hidden Markov Model (pHMM) method for the prediction and ligand-based classification of LGICs, utilizing their special topological characteristics. The method consists of a library of 35 pHMMs, built from the alignment of transmembrane segments of representative LGIC sequences. In addition,14 Pfam pHMMs are used to further annotate and correctly classify unknown protein sequences into one of the 10 LGIC subfamilies. Evaluation of the method showed that it outperforms existent methods in the detection of LGICs. On top of that LiGIoNs is the only currently available method that classifies LGICs into subfamilies. The method is available online at http://bioinformatics.biol.uoa.gr/ligions/ .

Amyloid fibrils are formed when soluble proteins misfold into highly ordered insoluble fibrillar aggregates and affect various organs and tissues. The deposition of amyloid fibrils is the main hallmark of a group of disorders, called amyloidoses. Curiously, fibril deposition has been also recorded as a complication in a number of other pathological conditions, including well-known neurodegenerative or endocrine diseases. To date, amyloidoses are roughly classified, owing to their tremendous heterogeneity. In this work, we introduce AmyCo, a freely available collection of amyloidoses and clinical disorders related to amyloid deposition. AmyCo classifies 75 diseases associated with amyloid deposition into two distinct categories, namely 1) amyloidosis and 2) clinical conditions associated with amyloidosis. Each database entry is annotated with the major protein component (causative protein), other components of amyloid deposits and affected tissues or organs. Database entries are also supplemented with appropriate detailed annotation and are referenced to ICD-10, MeSH, OMIM, PubMed, AmyPro and UniProtKB databases. To our knowledge, AmyCo is the first attempt towards the creation of a complete and an up-to-date repository, containing information about amyloidoses and diseases related to amyloid deposition. The AmyCo web interface is available at http://bioinformatics.biol.uoa.gr/amyco .

Voltage-Gated Ion Channels (VGICs) are one of the largest groups of transmembrane proteins. Due to their major role in the generation and propagation of electrical signals, VGICs are considered important from a medical viewpoint and their dysfunction is often associated with a group of diseases known as “Channelopathies”. We identified disease associated mutations and polymorphisms in these proteins through mapping missense Single Nucleotide Polymorphisms (SNPs) from the UniProt and ClinVar databases on their amino acid sequence, taking into consideration their special topological and functional characteristics. Statistical analysis revealed that disease associated SNPs are mostly found in the Voltage Sensor Domain – and especially at its fourth transmembrane segment (S4) – and in the Pore Loop. Both these regions are extremely important for the activation and ion conductivity of VGICs. Moreover, amongst the most frequently observed mutations are those of arginine to glutamine, to histidine or to cysteine, which can probably be attributed to the extremely important role of arginine residues in the regulation of membrane potential in these proteins. We suggest that topological information in combination with genetic variation data can contribute towards a better evaluation of the effect of currently unclassified mutations in VGICs. It is hoped that potential associations with certain disease phenotypes will be revealed in the future, with the use of similar approaches.

Cutaneous melanoma is a malignant tumor of skin melanocytes that are pigment-producing cells located in the basal layer (stratum basale) of epidermis. Accumulation of genetic mutations within their oncogenes or tumor-suppressor genes compels melanocytes to aberrant proliferation and spread to distant organs of the body, thereby resulting in severe and/or lethal malignancy. Metastatic melanoma's heavy mutational load, molecular heterogeneity and resistance to therapy necessitate the development of novel biomarkers and drug-based protocols that target key proteins involved in perpetuation of the disease. To this direction, we have herein employed a nano liquid chromatography-tandem mass spectrometry (nLC-MS/MS) proteomics technology to profile the deep-proteome landscape of WM-266-4 human metastatic melanoma cells. Our advanced melanoma-specific catalogue proved to contain 6,681 unique proteins, which likely constitute the hitherto largest single cell-line-derived proteomic collection of the disease. Through engagement of UNIPROT, DAVID, KEGG, PANTHER, INTACT, CYTOSCAPE, dbEMT and GAD bioinformatics resources, WM-266-4 melanoma proteins were categorized according to their sub-cellular compartmentalization, function and tumorigenicity, and successfully reassembled in molecular networks and interactomes. The obtained data dictate the presence of plastically inter-converted sub-populations of non-cancer and cancer stem cells, and also indicate the oncoproteomic resemblance of melanoma to glioma and lung cancer. Intriguingly, WM-266-4 cells seem to be subjected to both epithelial-to-mesenchymal (EMT) and mesenchymal-to-epithelial (MET) programs, with 1433G and ADT3 proteins being identified in the EMT/MET molecular interface. Oncogenic addiction of WM-266-4 cells to autocrine/paracrine signaling of IL17-, DLL3-, FGF(2/13)- and OSTP-dependent sub-routines suggests their critical contribution to the metastatic melanoma chemotherapeutic refractoriness. Interestingly, the 1433G family member that is shared between the BRAF- and EMT/MET-specific interactomes likely emerges as a novel and promising druggable target for the malignancy. Derailed proliferation and metastatic capacity of WM-266-4 cells could also derive from their metabolic addiction to pathways associated with glutamate/ammonia, propanoate and sulfur homeostasis, whose successful targeting may prove beneficial for advanced melanoma-affected patients.

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/.

G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors, including several functionally distinct families, that play a key role in cellular signalling and regulation of basic physiological processes. GPCRs are the focus of a significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs, whereas they still comprise the best potential targets for drug design. Taking into account the excess of data derived by genome sequencing projects, the use of computational tools for automated characterization of novel GPCRs is imperative. Typical computational strategies for identifying and classifying GPCRs involve sequence similarity searches (e.g. BLAST) coupled with pattern database analysis (e.g. PROSITE, BLOCKS). The diagnostic method presented here is based on a probabilistic approach that exploits highly discriminative profile Hidden Markov Models, excised from low entropy regions of multiple sequence alignments, to derive potent family signatures. For a given query, a P-value is obtained, combining individual hits derived from the same family. Hence a best-guess family membership is depicted, allowing GPCRs' classification at a family level, solely using primary structure information. A web-based version of the application is freely available at URL: http:/bioinformatics.biol.uoa.gr/PRED-GPCR.

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 non-covalent 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., Comment: 39 pages, 5 figures, 3 supplement figures, under review in BMRI

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.

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/.

Genomics and proteomics have added valuable information to our knowledgebase of the human biological system including the discovery of therapeutic targets and disease biomarkers. However, molecular profiling studies commonly result in the identification of novel proteins of unknown localization. A class of proteins of special interest is membrane proteins, in particular plasma membrane proteins. Despite their biological and medical significance, the 3-dimensional structures of less than 1% of plasma membrane proteins have been determined. In order to aid in identification of membrane proteins, a number of computational methods have been developed. These tools operate by predicting the presence of transmembrane segments. Here, we utilized five topology prediction methods (TMHMM, SOSUI, waveTM, HMMTOP, and TopPred II) in order to estimate the ratio of integral membrane proteins in the human proteome. These methods employ different algorithms and include a newly-developed method (waveTM) that has yet to be tested on a large proteome database. Since these tools are prone for error mainly as a result of falsely predicting signal peptides as transmembrane segments, we have utilized an additional method, SignalP. Based on our analyses, the ratio of human proteins with transmembrane segments is estimated to fall between 15% and 39% with a consensus of 13%. Agreement among the programs is reduced further when both a positive identification of a membrane protein and the number of transmembrane segments per protein are considered. Such a broad range of prediction depends on the selectivity of the individual method in predicting integral membrane proteins. These methods can play a critical role in determining protein structure and, hence, identifying suitable drug targets in humans.

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, PredSL's 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

waveTM is a web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. Prediction is performed by a dynamic programming algorithm on wavelet-denoised 'hydropathy' signals. Users submit a protein sequence and receive interactively the results. Topology prediction can also be obtained in conjunction with the algorithm OrienTM. A web server that implements the waveTM algorithm is freely available at http://bioinformatics.biol.uoa.gr/waveTM.

Membrane proteins represent ca. 20–30% of both eukaryotic and prokaryotic proteomes. They play crucial roles in cell survival and cell communication, as they function as transporters, receptors, anchors and enzymes. More than 30% of all prescribed drugs are targeting membrane proteins. Transmembrane proteins are either single-spanning membrane proteins or multi-spanning proteins. Single-spanning proteins can be classified into four types I, II, III and IV, depending on their topology and membrane targeting. They are very important functionally, involved in the presentation of antigens to the immune system, they are calcium-dependent cell adhesion proteins, they play a role in septum formation and they have many more specific, crucial roles. The purpose of this work was the construction of a database containing all single-spanning membrane proteins and their functional classification. This database is available at http://aias.biol.uoa.gr/sstmpdb

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/.