WebAllergen: a web server for predicting allergenic proteins

WebAllergen is a web server that predicts the potential allergenicity of proteins. The query protein will be compared against a set of prebuilt allergenic motifs that have been obtained from 664 known allergen proteins. The query will also be compared with known allergens that do not have detectable allergenic motifs. Moreover, users are allowed to upload their own allergens as alternative training sequences on which a new set of allergenic motifs will be built. The query sequences can also be compared with these motifs. AVAILABILITY: http://weballergen.bii.a-star.edu.sg/     

InterProSurf: a web server for predicting interacting sites on protein surfaces

A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. AVAILABILITY: The InterProSurf web server is available at http://curie.utmb.edu/     

ESBRI: A web server for evaluating salt bridges in proteins

Salt bridges can play important roles in protein structure and function and have stabilizing and destabilizing effects in protein folding. ESBRI is a software available as web tool which analyses the salt bridges in a protein structure, starting from the atomic coordinates. In the case of protein complexes, the salt bridges between protein chains can be evaluated, as well as those among specific charged amino acids and the different protein subunits, in order to obtain useful information regard the protein-protein interaction.     

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. Availability: AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.     

A web server to locate periodicities in a sequence

Summary : FT is a tool written in C++, which implements the Fourier analysis method to locate periodicities in aminoacid or DNA sequences. It is provided for free public use on a WWW server with a Java interface. Availability : The server address is http://o2.db. uoa.gr/FT Contact : shamodr@atlas.uoa.gr      

MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM

Given an uncharacterized protein sequence, how can we identify whether it is a membrane protein or not? If it is, which membrane protein type it belongs to? These questions are important because they are closely relevant to the biological function of the query protein and to its interaction process with other molecules in a biological system. Particularly, with the avalanche of protein sequences generated in the Post-Genomic Age and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to help address these questions. In this study, a 2-layer predictor, called MemType-2L, has been developed: the 1st layer prediction engine is to identify a query protein as membrane or non-membrane; if it is a membrane protein, the process will be automatically continued with the 2nd-layer prediction engine to further identify its type among the following eight categories: (1) type I, (2) type II, (3) type III, (4) type IV, (5) multipass, (6) lipid-chain-anchored, (7) GPI-anchored, and (8) peripheral. MemType-2L is featured by incorporating the evolution information through representing the protein samples with the Pse-PSSM (Pseudo Position-Specific Score Matrix) vectors, and by containing an ensemble classifier formed by fusing many powerful individual OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) classifiers. The success rates obtained by MemType-2L on a new-constructed stringent dataset by both the jackknife test and the independent dataset test are quite high, indicating that MemType-2L may become a very useful high throughput tool. As a Web server, MemType-2L is freely accessible to the public at http://chou.med.harvard.edu/bioinf/MemType.     

JUZBOX: A web server for extracting biomedical words from the protein sequence

The recognition of gene/protein names in literature is one of the pivotal steps in the processing of biological literatures for information extraction or data mining. We have compiled a lexicon of biomedical words (conserved patterns/ potential motifs) which has the combination of only 20 alphabets of amino acids. The remaining 6 letters of the English alphabets (B, J, O, U, X, Z) are treated as invalid amino acid characters (to our context), We have jumbled the 6 letters for the sake of usage and convenience and termed as ’JUZBOX‘ and these characters were filtered in the biomedical lexicon. Undoubtedly, the generation of biomedical words from protein sequence using JUZBOX have applications specific for functional annotation.

Availability

JUZBOX is available freely at http://www.spices.res.in/juzbox     

Conformations of disulfide bridges in proteins

The conformational characteristics of disulfide bridges in proteins have been analyzed using a dataset of 22 protein structures, available at a resolution of less than or equal to 2.0 A, containing a total of 72 disulfide crosslinks. The parameters used in the analysis include (phi, psi) values at Cys residues, bridge dihedral angles chi ss, chi i1, chi j1, chi i2, and chi j2, the distances C alpha i-C alpha j and C beta i-C beta j between the C alpha and C beta atoms of Cys(i) and Cys(j). Eight families of bridge conformations with three or more occurrences have been identified on the basis of these stereochemical parameters. The most populated family corresponds to the "left handed spiral" identified earlier by Richardson [1981) Adv. Protein Chem. 34, 167-330). Disulfide bridging across antiparallel extended strands is observed in alpha-lytic protease, crambin, and beta-trypsin and this structure is shown to be very similar to those obtained in small cystine peptides. Solvent accessible surface area calculations show that the overwhelming majority of disulfide bridges are inaccessible to solvent.     

IWS: integrated web server for protein sequence and structure analysis

Rapid increase in protein sequence information from genome sequencing projects demand the intervention of bioinformatics tools to recognize interesting gene-products and associated function. Often, multiple algorithms need to be employed to improve accuracy in predictions and several structure prediction algorithms are on the public domain. Here, we report the availability of an Integrated Web-server as a bioinformatics online package dedicated for in-silico analysis of protein sequence and structure data (IWS). IWS provides web interface to both in-house and widely accepted programs from major bioinformatics groups, organized as 10 different modules. IWS also provides interactive images for Analysis Work Flow, which will provide transparency to the user to carry out analysis by moving across modules seamlessly and to perform their predictions in a rapid manner. AVAILABILITY: IWS IS AVAILABLE FROM THE URL: http://caps.ncbs.res.in/iws.     

BIPAD: A web server for modeling bipartite sequence elements

Background  

Many dimeric protein complexes bind cooperatively to families of bipartite nucleic acid sequence elements, which consist of pairs of conserved half-site sequences separated by intervening distances that vary among individual sites.     

Evaluating the stability of disulfide bridges in proteins: a torsional potential energy surface for diethyl disulfide

Disulfide bonds formed by the oxidation of cysteine residues in proteins are the major form of intra- and inter-molecular covalent linkages in the polypeptide chain. To better understand the conformational energetics of this linkage, we have used the MP2(full)/6-31G(d) method to generate a full potential energy surface (PES) for the torsion of the model compound diethyl disulfide (DEDS) around its three critical dihedral angles (χ₂, χ₃, χ₂′). The use of ten degree increments for each of the parameters resulted in a continuous, fine-grained surface. This allowed us to accurately predict the relative stabilities of disulfide bonds in high resolution structures from the Protein Data Bank. The MP2(full) surface showed significant qualitative differences from the PES calculated using the Amber force field. In particular, a different ordering was seen for the relative energies of the local minima. Thus, Amber energies are not reliable for comparison of the relative stabilities of disulfide bonds. Surprisingly, the surface did not show a minimum associated with χ₂～ ? 60°, χ₃～90, χ₂′～ ? 60°. This is due to steric interference between Hα atoms. Despite this, significant populations of disulfides were found to adopt this conformation. In most cases this conformation is associated with an unusual secondary structure motif, the cross-strand disulfide. The relative instability of cross-strand disulfides is of great interest, as they have the potential to act as functional switches in redox processes.     

ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes

ABSTRACT: BACKGROUND: Understanding protein subcellular localization is a necessary component toward understanding the overall function of a protein. Numerous computational methods have been published over the past decade, with varying degrees of success. Despite the large number of published methods in this area, only a small fraction of them are available for researchers to use in their own studies. Of those that are available, many are limited by predicting only a small number of major organelles in the cell. Additionally, the majority of methods predict only a single location, even though it is known that a large fraction of the proteins in eukaryotic species shuttle between locations to carry out their function. FINDINGS: We present a software package and a web server for predicting subcellular localization of protein sequences based on the ngLOC method. ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 89.8% to 91.4% across species. This program can predict 11 distinct locations each in plant and animal species. ngLOC also predicts 4 and 5 distinct locations on gram-positive and gram-negative bacterial datasets, respectively. CONCLUSIONS: ngLOC is a generic method that can be trained by data from a variety of species or classes for predicting protein subcellular localization. The standalone software is freely available for academic use under GNU GPL, and the ngLOC web server is also accessible at http://ngloc.unmc.edu.     

DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins

This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. AVAILABILITY: Freely available at http://lcg.rit.albany.edu/dp-bind. SUPPLEMENTARY INFORMATION: http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html.     

NdPASA: a pairwise sequence alignment server for distantly related proteins

SUMMARY: NdPASA is a web server specifically designed to optimize sequence alignment between distantly related proteins. The program integrates structure information of the template sequence into a global alignment algorithm by employing neighbor-dependent propensities of amino acids as a unique parameter for alignment. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. NdPASA is most effective in aligning homologous proteins sharing low percentage of sequence identity. The server is designed to aid homologous protein structure modeling. A PSI-BLAST search engine was implemented to help users identify template candidates that are most appropriate for modeling the query sequences.     

STAR: predicting recombination sites from amino acid sequence

Background  

Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probability that hybrid sequences have the desired properties is increased dramatically. The prohibitive requirements for applying current tools led us to investigate machine learning to assist in finding useful recombination sites from amino acid sequence alone.     

CASVM: web server for SVM-based prediction of caspase substrates cleavage sites

Caspases belong to a unique class of cysteine proteases which function as critical effectors of apoptosis, inflammation and other important cellular processes. Caspases cleave substrates at specific tetrapeptide sites after a highly conserved aspartic acid residue. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. We have recently developed a support vector machines (SVM) method to address this issue. Our algorithm achieved an accuracy ranging from 81.25 to 97.92%, making it one of the best methods currently available. CASVM is the web server implementation of our SVM algorithms, written in Perl and hosted on a Linux platform. The server can be used for predicting non-canonical caspase substrate cleavage sites. We have also included a relational database containing experimentally verified caspase substrates retrievable using accession IDs, keywords or sequence similarity. AVAILABILITY: http://www.casbase.org/casvm/index.html     

BAGET: a web server for the effortless retrieval of prokaryotic gene context and sequence

BAGET (Bacterial and Archaeal Gene Exploration Tool) is a web service designed to facilitate extraction, by molecular geneticists and phylogeneticists, of specific gene and protein sequences from completely determined prokaryotic genomes. Upon selection of a particular prokaryotic organism and gene, two levels of visual gene context information are provided on a single dynamic page: (i) a graphical representation of a user defined portion of the chromosome centered on the gene of interest and (ii) the DNA sequence of the query gene, of the immediate neighboring genes and the intergenic regions each identified by a consistent color code. The aminoacid sequence is provided for protein-coding query genes. Query results can be exported as a rich text format (RTF) word processor file for printing, archival or further analysis. AVAILABILITY: http://archaea.u-psud.fr/bin/baget.dll.     

ScanMoment: a web server for combinatorial analysis of basic residues in nucleic acid binding sites

ScanMoment is a webserver designed to identify the presence of the basic faced α‐helix (BFAH) motif in the nucleic acid binding sites of proteins. The program calculates the ’Basic Moment‘, a parameter that quantitizes the distribution of basic residues on the surface of an α‐helix. A sliding window is used to generate a plot displaying regions of the protein sequence that possesses a high Basic Moment and hus likely to possess a BFAH motif. The user may vary the periodicity from that of an alpha‐helix (100°), to those of other secondary structures such as beta sheets and 3₁₀ helices. The program can also plot the periodicity of basic residues in a protein sequence using a Fourier transformation. The procedure has been used to characterize the presence of BFAHs in the N‐terminal extensions of the eukaryotic aminoacyl‐tRNA synthetases and to indicate the presence of a BFAH in the tRNA binding site of alanyl‐tRNA synthetase.     

PluriPred: A Web server for predicting proteins involved in pluripotent network

Pluripotency is a unique property of stem cells that allows them to differentiate into all types of adult cells or maintain the self-renewal property. PluriPred predicts whether a protein is involved in pluripotency from primary protein sequence using manually curated pluripotent proteins as training datasets. Machine learning techniques (MLTs) such as Support Vector Machine (SVM), Naïve Base (NB), Random Forest (RF), and sequence alignment technique BLAST were used in our study. The combination of SVM and PSI-BLAST was our proposed best model, which obtained a sensitivity of 77.40%, specificity of 79.72%, accuracy of 79.2%, and area under the ROC curve was 0.82 using 5-fold cross-validation. Furthermore, PluriPred gives the confidence of the prediction from training dataset’s SVM score distribution and p-value from BLAST. We validated our proposed model with the other existing high-throughput studies using blind/independent datasets. Using PluriPred, 233 novel core and 323 novel extended core pluripotent proteins from mouse proteome, and 167 novel core and 385 extended core pluripotent proteins from human proteome, were predicted with high confidence. The Web application of PluriPred is available from bicresources.jcbose.ac.in/ssaha4/pluripred/. Many pluripotent genes/proteins take part in protein-protein networks associated with stem cell, cancer, and developmental biology, and we believe that PluriPred will help in these research.