waveTM: wavelet-based transmembrane segment prediction

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.     

gpDB: a database of GPCRs, G-proteins, effectors and their interactions

gpDB is a publicly accessible, relational database, containing information about G-proteins, G-protein coupled receptors (GPCRs) and effectors, as well as information concerning known interactions between these molecules. The sequences are classified according to a hierarchy of different classes, families and subfamilies based on literature search. The main innovation besides the classification of G-proteins, GPCRs and effectors is the relational model of the database, describing the known coupling specificity of GPCRs to their respective alpha subunits of G-proteins, and also the specific interaction between G-proteins and their effectors, a unique feature not available in any other database. AVAILABILITY: http://bioinformatics.biol.uoa.gr/gpDB CONTACT: shamodr@biol.uoa.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm.

We present a novel method that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The PRED-TMR algorithm described, refines a standard hydrophobicity analysis with a detection of potential termini ('edges', starts and ends) of transmembrane regions. This allows one both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition. The accuracy obtained on a test set of 101 non-homologous transmembrane proteins with reliable topologies compares well with that of other popular existing methods. Only a slight decrease in prediction accuracy was observed when the algorithm was applied to all transmembrane proteins of the SwissProt database (release 35). A WWW server running the PRED-TMR algorithm is available at http://o2.db.uoa. gr/PRED-TMR/     

CoPreTHi: a Web tool which combines transmembrane protein segment prediction methods

CoPreTHi is a Java based web application, which combines the results of methods that predict the location of transmembrane segments in protein sequences into a joint prediction histogram. Clearly, the joint prediction algorithm, produces superior quality results than individual prediction schemes. The program is available at http://o2.db.uoa.gr/CoPreTHi.     

A novel tool for the prediction of transmembrane protein topology based on a statistical analysis of the SwissProt database: the OrienTM algorithm

OrienTM is a computer software that utilizes an initial definition of transmembrane segments to predict the topology of transmembrane proteins from their sequence. It uses position-specific statistical information for amino acid residues which belong to putative non-transmembrane segments derived from statistical analysis of non-transmembrane regions of membrane proteins stored in the SwissProt database. Its accuracy compares well with that of other popular existing methods. A web-based version of OrienTM is publicly available at the address http://biophysics.biol.uoa.gr/OrienTM.     

An hierarchical artificial neural network system for the classification of transmembrane proteins.

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2     

A Web-based classification system of DNA-binding protein families

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The family of DNA-binding proteins is one of the most populated and studied amongst the various genomes of bacteria, archaea and eukaryotes and the Web-based system presented here is an approach to their classification. The DnaProt resource is an annotated and searchable collection of protein sequences for the families of DNA-binding proteins. The database contains 3238 full-length sequences (retrieved from the SWISS-PROT database, release 38) that include, at least, a DNA-binding domain. Sequence entries are organized into families defined by PROSITE patterns, PRINTS motifs and de novo excised signatures. Combining global similarities and functional motifs into a single classification scheme, DNA-binding proteins are classified into 33 unique classes, which helps to reveal comprehensive family relationships. To maximize family information retrieval, DnaProt contains a collection of multiple alignments for each DNA-binding family while the recognized motifs can be used as diagnostically functional fingerprints. All available structural class representatives have been referenced. The resource was developed as a Web-based management system for online free access of customized data sets. Entries are fully hyperlinked to facilitate easy retrieval of the original records from the source databases while functional and phylogenetic annotation will be applied to newly sequenced genomes. The database is freely available for online search of a library containing specific patterns of the identified DNA-binding protein classes and retrieval of individual entries from our WWW server (http://kronos.biol.uoa.gr/~mariak/dbDNA.html).     

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 fivefold 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/.     

Prediction of the coupling specificity of GPCRs to four families of G-proteins using hidden Markov models and artificial neural networks

MOTIVATION: G-protein coupled receptors are a major class of eukaryotic cell-surface receptors. A very important aspect of their function is the specific interaction (coupling) with members of four G-protein families. A single GPCR may interact with members of more than one G-protein families (promiscuous coupling). To date all published methods that predict the coupling specificity of GPCRs are restricted to three main coupling groups G(i/o), G(q/11) and G(s), not including G(12/13)-coupled or other promiscuous receptors. RESULTS: We present a method that combines hidden Markov models and a feed-forward artificial neural network to overcome these limitations, while producing the most accurate predictions currently available. Using an up-to-date curated dataset, our method yields a 94% correct classification rate in a 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G(12/13), whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered. AVAILABILITY: A webserver for academic users is available at http://bioinformatics.biol.uoa.gr/PRED-COUPLE2     

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.     

LiGIoNs: A computational method for the detection and classification of ligand-gated ion channels

Ligand-Gated Ion Channels (LGICs) is 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 the amino acid composition. Here we report the development of LiGIoNs, a profile Hidden Markov Model (pHMM) method for the prediction and ligand-based classification of LGICs. The method consists of a library of 10 pHMMs, one per LGIC subfamily, built from the alignment of representative LGIC sequences. In addition, 14 Pfam pHMMs are used to further annotate and classify unknown protein sequences into one of the 10 LGIC subfamilies. Evaluation of the method showed that it outperforms existing 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/.     

LepChorionDB,a database of Lepidopteran chorion proteins and a set of tools useful for the identification of chorion proteins in Lepidopteran proteomes

Chorion proteins of Lepidoptera have a tripartite structure, which consists of a central domain and two, more variable, flanking arms. The central domain is highly conserved and it is used for the classification of chorion proteins into two major classes, A and B. Annotated and unreviewed Lepidopteran chorion protein sequences are available in various databases. A database, named LepChorionDB, was constructed by searching 5 different protein databases using class A and B central domain-specific profile Hidden Markov Models (pHMMs), developed in this work. A total of 413 Lepidopteran chorion proteins from 9 moths and 1 butterfly species were retrieved. These data were enriched and organised in order to populate LepChorionDB, the first relational database, available on the web, containing Lepidopteran chorion proteins grouped in A and B classes. LepChorionDB may provide insights in future functional and evolutionary studies of Lepidopteran chorion proteins and thus, it will be a useful tool for the Lepidopteran scientific community and Lepidopteran genome annotators, since it also provides access to the two pHMMs developed in this work, which may be used to discriminate A and B class chorion proteins. LepChorionDB is freely available at http://bioinformatics.biol.uoa.gr/LepChorionDB.     

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.     

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/.     

Actomyosin is involved in the plasmolytic cycle: gliding movement of the deplasmolyzing protoplast

Summary.  The leaf cells of Chlorophytum comosum seem to have the ability to regulate their protoplast volume and shape during the plasmolytic cycle. This phenomenon was morphologically expressed by the stabilization of the plasmolyzed protoplast volume and shape within 1–5 min after the immersion of the leaf segments in the plasmolytic fluid and temporarily at the onset of deplasmolysis. During the latter stage the plasmolyzed protoplast rounded up and assumed a perfectly convex shape and glided into the cell lumen along the cell axis. This gliding movement was active, nonsaltatory, and conducted with a constant velocity and lasted for a short time. During this movement the protoplast volume did not change appreciably. As far as we know, this movement has not been described so far. Deplasmolysis proceeded and was rapidly completed when the protoplast stopped moving. Leaf cells which have been affected by an antiactin filament drug or myosin inhibitors lost their ability to regulate the volume and shape of the plasmolyzing protoplast. In addition, the gliding protoplast movement was also inhibited in the treated cells. These data show for the first time that the actomyosin system is involved in the mechanism of volume regulation during the plasmolytic cycle and that it underlies the gliding movement of the deplasmolyzing protoplast. Received June 3, 2002; accepted September 26, 2002; published online April 2, 2003 RID="*" ID="*" Correspondence and reprints: Department of Botany, Faculty of Biology, University of Athens, Athens 15784, Greece. E-mail: bgalatis@biol.uoa.gr     

Neuro-endocrine correlations of hypothalamic-pituitary-thyroid axis in healthy humans

Neuro-endocrine hormone secretion is characterized by circadian rhythmicity. Melatonin, GRH and GH are secreted during the night, CRH and ACTH secretion peak in the morning, determining the circadian rhythm of cortisol secretion, TRH and TSH show circadian variations with higher levels at night. Thyroxine levels do not change with clear circadian rhythmicity. In this paper we have considered a possible influence of cortisol and melatonin on hypothalamic-pituitary-thyroid axis function in humans. Melatonin, cortisol, TRH, TSH and FT4 serum levels were determined in blood samples obtained every four hours for 24 hours from ten healthy males, aged 36-51 years. We correlated hormone serum levels at each sampling time and evaluated the presence of circadian rhythmicity of hormone secretion. In the activity phase (06:00 h-10:00 h-14:00 h) cortisol correlated negatively with FT4, TSH correlated positively with TRH, TRH correlated positively with FT4 and melatonin correlated positively with TSH. In the resting phase (18:00 h-22:00 h-02:00 h) TRH correlated positively with FT4, melatonin correlated negatively with FT4, TSH correlated negatively with FT4, cortisol correlated positively with FT4 and TSH correlated positively with TRH. A clear circadian rhythm was validated for the time-qualified changes of melatonin and TSH secretion (with acrophase during the night), for cortisol serum levels (with acrophase in the morning), but not for TRH and FT4 serum level changes. In conclusion, the hypothalamic-pituitary-thyroid axis function may be modulated by cortisol and melatonin serum levels and by their circadian rhythmicity of variation.     

Behavior of free testosterone in patients with prostate cancer on androgen deprivation therapy

OBJECTIVES: Determination of free testosterone (FT) serum level is an efficient method to evaluate bioavailable testosterone. We analyzed the behavior of serum FT in patients with prostate cancer receiving androgen deprivation therapy (ADT) and correlated FT with total testosterone (TT). We also analyzed the efficiency of both isoforms in the evaluation of the ADT. METHODS: Serum levels of TT and FT were determined in 191 patients with prostate cancer in a cross-sectional study. A subset of 56 patients submitted only to radical prostatectomy served as control group. The remaining 135 patients with advanced prostate cancer on three-month LHRH agonist treatment comprised the study group. The median age of the population was 73 years (range, 53-86 years) and the median time on ADT was 42 months (6-198). RESULTS: A significant correlation and linear regression between TT and FT was observed (r2 0.948). The efficiency of TT and FT to discriminate patients with and without ADT was similar (AUC: 0.993 and 0.995, respectively, p > 0.05). A castration level of serum FT established at 1.7 pg/mL had a sensitivity of 85.9% and a specificity of 100%, which are similar to the sensitivity and specificity of 50 ng/dL of TT. All patients without ADT had levels of serum TT and FT above the castration level. In 19 of the 135 (14.1%) patients on ADT serum TT was above 50 ng/dL. In 12 of these 19 patients (63.2%) serum FT was below 1.7 pg/mL while in seven patients (5.2%) FT was also above the castration level. CONCLUSIONS: The castration level of FT was established at 1.7 pg/mL. Serum TT and TF correlated very well; however, they seemed to provide complementary information in the evaluation of ADT efficiency. 14.1% of the patients on ADT failed to reach the castration level of serum TT; determination of serum FT in these patients would reduce this rate to 5.2%.     

甲状腺功能减退患者血清甲状腺激素、同型半胱氨酸及血脂水平测定的临床意义

目的:探讨甲状腺功能减退患者血清同型半胱氨酸(Hcy)、甲状腺激素(TH)及血脂水平测定的临床意义。方法:选取2016年2月-2017年2月期间我院收治的甲状腺功能减退患者101例为观察组,选取同期于我院体检的健康志愿者80例为对照组。检测所有研究对象甲状腺素(FT4)和三碘甲状腺原氨酸(FT3)、Hcy及血脂[甘油三酯(TG)、总胆固醇(TC)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)]变化水平。比较观察组治疗前后、对照组与观察组治疗前FT3、FT4、Hcy及血脂水平,采用Pearson相关性分析血清Hcy与FT3、FT4、血脂水平的相关性。结果:观察组患者治疗后FT3、FT4均较治疗前升高,Hcy、TC、LDL-C均较治疗前降低(P0.05),而观察组患者治疗前后TG、HDL-C比较差异无统计学意义(P0.05)。观察组治疗前FT3、FT4、HDL-C水平明显低于对照组,而Hcy、TG、TC、LDL-C则明显高于对照组(P0.05)。经Pearson相关性分析显示,甲状腺功能减退患者Hcy与FT3、FT4呈负相关(P0.05),与TC、TG呈正相关(P0.05),而与LDL-C、HDL-C无相关性(P0.05)。结论:甲状腺功能减退患者的FT3、FT4、Hcy及血脂水平表达异常,且Hcy与FT3、FT4、血脂水平密切相关,临床上可通过监测甲状腺功能减退患者的上述指标,有助于评估患者的病情程度。     

Plasmapheresis rapidly eliminates thyroid hormones from the circulation, but does not affect the speed of TSH recovery following prolonged suppression

OBJECTIVE: To report an attempt to shorten the preparation interval before radioactive iodine administration using plasmapheresis in a 77-year-old woman with a history of papillary thyroid carcinoma with local recurrence and lung metastases, in whom the administration of a high dose of radioactive iodine was intended as a desperate rescue procedure. METHODS: The patient was initially started on cholestyramine. Two days later, plasmapheresis was performed. RESULTS: Plasmapheresis rapidly decreased free tri-iodothyronine (FT(3)) and free thyroxine (FT(4)). Serum FT(4) subsequently remained low, while FT(3) recovered the next day. Thyroid-stimulating hormone (TSH) reached 25 mIU/l in 14 days, which is within the time frame required to reach the target TSH level by withdrawing levothyroxine alone. CONCLUSION: Plasmapheresis is very effective in eliminating thyroid hormones from the circulation. However, it does not seem to accelerate thyrotroph recovery to a considerable extent after prolonged suppression.