Analysis of donor splice sites in different eukaryotic organisms

We present here a new algorithm for functional site analysis. It is based on four main assumptions: each variation of nucleotide composition makes a different contribution to the overall binding free energy of interaction between a functional site and another molecule; nonfunctioning site-like regions (pseudosites) are absent or rare in genomes; there may be errors in the sample of sites; and nucleotides of different site positions are considered to be mutually dependent. In this algorithm, the site set is divided into subsets, each described by a certain consensus. Donor splice sites of the human protein-coding genes were analyzed. Comparing the results with other methods of donor splice site prediction has demonstrated a more accurate prediction of consensus sequences AG/GU(A,G), G/GUnAG, /GU(A,G)AG, /GU(A,G)nGU, and G/GUA than is achieved by weight matrix and consensus (A,C)AG/GU(A,G)AGU with mismatches. The probability of the first type error, E1, for the obtained consensus set was about 0.05, and the probability of the second type error, E2, was 0.15. The analysis demonstrated that accuracy of the functional site prediction could be improved if one takes into account correlations between the site positions. The accuracy of prediction by using human consensus sequences was tested on sequences from different organisms. Some differences in consensus sequences for the plant Arabidopsis sp., the invertebrate Caenorhabditis sp., and the fungus Aspergillus sp. were revealed. For the yeast Saccharomyces sp. only one conservative consensus, /GUA(U,A,C)G(U,A,C), was revealed (E1 = 0.03, E2 = 0.03). Yeast is a very interesting model to use for analysis of molecular mechanisms of splicing. Received: 14 October 1996 / Accepted: 30 January 1997     

FrameD: A flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences

We describe FrameD, a program that predicts coding regions in prokaryotic and matured eukaryotic sequences. Initially targeted at gene prediction in bacterial GC rich genomes, the gene model used in FrameD also allows to predict genes in the presence of frameshifts and partially undetermined sequences which makes it also very suitable for gene prediction and frameshift correction in unfinished sequences such as EST and EST cluster sequences. Like recent eukaryotic gene prediction programs, FrameD also includes the ability to take into account protein similarity information both in its prediction and its graphical output. Its performances are evaluated on different bacterial genomes. The web site (http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD) allows direct prediction, sequence correction and translation and the ability to learn new models for new organisms.     

Support vector machine approach for protein subcellular localization prediction

MOTIVATION: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. AVAILABILITY: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.     

Analysis of homeodomain specificities allows the family-wide prediction of preferred recognition sites

We describe the comprehensive characterization of homeodomain DNA-binding specificities from a metazoan genome. The analysis of all 84 independent homeodomains from D. melanogaster reveals the breadth of DNA sequences that can be specified by this recognition motif. The majority of these factors can be organized into 11 different specificity groups, where the preferred recognition sequence between these groups can differ at up to four of the six core recognition positions. Analysis of the recognition motifs within these groups led to a catalog of common specificity determinants that may cooperate or compete to define the binding site preference. With these recognition principles, a homeodomain can be reengineered to create factors where its specificity is altered at the majority of recognition positions. This resource also allows prediction of homeodomain specificities from other organisms, which is demonstrated by the prediction and analysis of human homeodomain specificities.     

Enzymatic characterization of Catalase from Bacillus anthracis and prediction of critical residues using information theoretic measure of Relative Entropy

In order to cope up with the reactive oxygen species (ROS) generated by host innate immune response, most of the intracellular organisms express Catalase for the enzymatic destruction/detoxification of hydrogen peroxide, to combat its deleterious effects. Catalase thus, scavenges ROS thereby playing a pivotal role in facilitating the survival of the pathogen within the host, and thus contributes to its pathogenesis. Bacillus anthracis harbors five isoforms of Catalase, but none of them has been studied so far. Thus, this study is the first attempt to delineate the biochemical and functional characteristics of one of the isoforms of Catalase (Cat1.4) of B. anthracis, followed by identification of residues critical for catalysis. The general strategy used, so far for mutational analysis in Catalases is structure based, i.e. the residues in the vicinity of heme were mutated to decipher the enzymatic mechanism. However, in the present study, protein sequence analysis was used for the prediction of catalytically important residues of Catalase. Essential measures were adopted to ensure the accuracy of predictions like after retrieval of well-annotated sequences from the database with EC 1.11.1.6, preprocessing was done to remove irrelevant sequences. The method used for multiple alignment of sequences, was guided by structural alignment and thereafter, an information theoretic measure, Relative Entropy was used for the critical residue prediction. By exploiting this strategy, we identified two previously known essential residues, H55 and Y338 in the active site which were demonstrated to be crucial for the activity. We also identified six novel crucial residues (Q332, Y117, H215, W257, N376 and H146) located distantly from the active site. Thus, the present study highlights the significance of this methodology to identify not only those crucial residues which lie in the active site of Catalase, but also the residues located distantly.     

Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides

We have developed an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences. It is a 2-layer predictor: the 1st-layer prediction engine is to identify a query protein as secretory or non-secretory; if it is secretory, the process will be automatically continued with the 2nd-layer prediction engine to further identify the cleavage site of its signal peptide. The new predictor is called Signal-CF, where C stands for "coupling" and F for "fusion", meaning that Signal-CF is formed by incorporating the subsite coupling effects along a protein sequence and by fusing the results derived from many width-different scaled windows through a voting system. Signal-CF is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-CF is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-CF/ or http://202.120.37.186/bioinf/Signal-CF/.     

A set of nearest neighbor parameters for predicting the enthalpy change of RNA secondary structure formation

A complete set of nearest neighbor parameters to predict the enthalpy change of RNA secondary structure formation was derived. These parameters can be used with available free energy nearest neighbor parameters to extend the secondary structure prediction of RNA sequences to temperatures other than 37°C. The parameters were tested by predicting the secondary structures of sequences with known secondary structure that are from organisms with known optimal growth temperatures. Compared with the previous set of enthalpy nearest neighbor parameters, the sensitivity of base pair prediction improved from 65.2 to 68.9% at optimal growth temperatures ranging from 10 to 60°C. Base pair probabilities were predicted with a partition function and the positive predictive value of structure prediction is 90.4% when considering the base pairs in the lowest free energy structure with pairing probability of 0.99 or above. Moreover, a strong correlation is found between the predicted melting temperatures of RNA sequences and the optimal growth temperatures of the host organism. This indicates that organisms that live at higher temperatures have evolved RNA sequences with higher melting temperatures.     

Genomic BLAST: custom-defined virtual databases for complete and unfinished genomes

BLAST (Basic Local Alignment Search Tool) searches against DNA and protein sequence databases have become an indispensable tool for biomedical research. The proliferation of the genome sequencing projects is steadily increasing the fraction of genome-derived sequences in the public databases and their importance as a public resource. We report here the availability of Genomic BLAST, a novel graphical tool for simplifying BLAST searches against complete and unfinished genome sequences. This tool allows the user to compare the query sequence against a virtual database of DNA and/or protein sequences from a selected group of organisms with finished or unfinished genomes. The organisms for such a database can be selected using either a graphic taxonomy-based tree or an alphabetical list of organism-specific sequences. The first option is designed to help explore the evolutionary relationships among organisms within a certain taxonomy group when performing BLAST searches. The use of an alphabetical list allows the user to perform a more elaborate set of selections, assembling any given number of organism-specific databases from unfinished or complete genomes. This tool, available at the NCBI web site http://www.ncbi.nlm.nih.gov/cgi-bin/Entrez/genom_table_cgi, currently provides access to over 170 bacterial and archaeal genomes and over 40 eukaryotic genomes.     

Evolution of alpha-amylases: architectural features and key residues in the stabilization of the (beta/alpha)(8) scaffold

We provide a comprehensive analysis of the current enzymes with alpha-amylase activity (AAMYs) that belong to family 13 glycoside hydrolase (GH-13; 144 Archaea, Bacteria, and Eukaryota sequences from 87 different species). This study aims to further knowledge of the evolutionary molecular relationships among the sequences of their A and B domains with special emphasis on the correlation between what is observed in the structures and protein evolution. Multialignments for the A domain distinguish two clusters for sequences from Archaea organisms, eight for sequences from Bacteria organisms, and three for sequences from Eukaryota organisms. The clusters for Bacteria do not follow any strict taxonomic pathway; in fact, they are rather scattered. When we compared the A domains of sequences belonging to different kingdoms, we found that various pairs of clusters were significantly similar. Using either sequence similarity with crystallized structures or secondary-structure prediction methods, we identified in all AAMYs the eight putative beta-strands that constitute the beta-sheet in the TIM barrel of the A domain and studied the packing in its interior. We also discovered a "hidden homology" in the TIM barrel, an invariant Gly located upstream in the sequence before the conserved Asp in beta-strand 3. This Gly precedes an alpha-helix and is actively involved in capping its N-terminal end with a capping box. In all cases, a Schellman motif caps the C-terminal end of this helix.     

Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.SVMSignal is publicly available at http://bio-cluster.iis.sinica.edu.tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.     

Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence

With the exponential growth of genomic sequences, there is an increasing demand to accurately identify protein coding regions (exons) from genomic sequences. Despite many progresses being made in the identification of protein coding regions by computational methods during the last two decades, the performances and efficiencies of the prediction methods still need to be improved. In addition, it is indispensable to develop different prediction methods since combining different methods may greatly improve the prediction accuracy. A new method to predict protein coding regions is developed in this paper based on the fact that most of exon sequences have a 3-base periodicity, while intron sequences do not have this unique feature. The method computes the 3-base periodicity and the background noise of the stepwise DNA segments of the target DNA sequences using nucleotide distributions in the three codon positions of the DNA sequences. Exon and intron sequences can be identified from trends of the ratio of the 3-base periodicity to the background noise in the DNA sequences. Case studies on genes from different organisms show that this method is an effective approach for exon prediction.     

Predicting DNA-binding sites of proteins based on sequential and 3D structural information

Protein–DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein–DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein–DNA-binding sites based solely on protein sequences. In this study, we developed a novel predictor based on support vector machine algorithm coupled with the maximum relevance minimum redundancy method followed by incremental feature selection. We incorporated not only features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, solvent accessibility, but also five three-dimensional (3D) structural features calculated from PDB data to predict the protein–DNA interaction sites. Feature analysis showed that 3D structural features indeed contributed to the prediction of DNA-binding site and it was demonstrated that the prediction performance was better with 3D structural features than without them. It was also shown via analysis of features from each site that the features of DNA-binding site itself contribute the most to the prediction. Our prediction method may become a useful tool for identifying the DNA-binding sites and the feature analysis described in this paper may provide useful insights for in-depth investigations into the mechanisms of protein–DNA interaction.     

Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information

Sequence-based approach for motif prediction is of great interest and remains a challenge. In this work, we develop a local combinational variable approach for sequence-based helix-turn-helix (HTH) motif prediction. First we choose a sequence data set for 88 proteins of 22 amino acids in length to launch an optimized traversal for extracting local combinational segments (LCS) from the data set. Then after LCS refinement, local combinational variables (LCV) are generated to construct prediction models for HTH motifs. Prediction ability of LCV sets at different thresholds is calculated to settle a moderate threshold. The large data set we used comprises 13 HTH families, with 17 455 sequences in total. Our approach predicts HTH motifs more precisely using only primary protein sequence information, with 93.29% accuracy, 93.93% sensitivity and 92.66% specificity. Prediction results of newly reported HTH-containing proteins compared with other prediction web service presents a good prediction model derived from the LCV approach. Comparisons with profile-HMM models from the Pfam protein families database show that the LCV approach maintains a good balance while dealing with HTH-containing proteins and non-HTH proteins at the same time. The LCV approach is to some extent a complementary to the profile-HMM models for its better identification of false-positive data. Furthermore, genome-wide predictions detect new HTH proteins in both Homo sapiens and Escherichia coli organisms, which enlarge applications of the LCV approach. Software for mining LCVs from sequence data set can be obtained from anonymous ftp site ftp://cheminfo.tongji.edu.cn/LCV/freely.     

Structural predictions on the 33 kDa extrinsic protein associated to the oxygen evolving complex of photosynthetic organisms

Modern computational methods for protein structure prediction have been used to study the structure of the 33 kDa extrinsic membrane protein, associated to the oxygen evolving complex of photosynthetic organisms. A multiple alignment of 14 sequences of this protein from cyanobacteria, algae and plants is presented. The alignment allows the identification of fully conserved residues and the recognition of one deletion and one insertion present in the plant sequences but not in cyanobacteria. A tree of similarity, deduced from pair-wise comparison and cluster analysis of the sequences, is also presented. The alignment and the consensus sequence derived are used for prediction the secondary structure of the protein. This prediction indicates that it is a mainly-beta protein (25–38% of -strands) with no more than 4% of -helix. Fold recognition by threading is applied to obtain a topological 2D model of the protein. In this model the secondary structure elements are located, including several highly conserved loops. Some of these conserved loops are suggested to be important for the binding of the 33 kDa protein to Photosystem II and for the stability of the manganese cluster. These structural predictions are in good agreement with experimental data reported by several authors.     

Quantification and diversity of the archaeal community in a landfill site

At a sea-based, solid waste disposal site, methanogenic organisms were quantified by molecular approaches. The samples collected for analysis were from anaerobic leachate of the landfill site. When the DNA extracted from the leachate was examined by a quantitative PCR method using domain-specific 16S rDNA primers, archaeal DNA represented 2-3% of the total extracted DNA. On the basis of cloning and sequence comparison of the archaeal PCR products, more than half of the sequences belonged to Euryarchaeota, particularly relatives of the genus Methanosaeta. The cloning analysis suggested that the majority of methane emitted from the landfill site originated from the acetate-utilizing Methanosaeta.     

LIPPRED: A web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Na?ve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.