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PubMed2001

Database verification studies of SWISS-PROT and GenBank.

PROBLEM STATEMENT: We have studied the relationships among SWISS-PROT, TrEMBL, and GenBank with two goals. First is to determine whether users can reliably identify those proteins in SWISS-PROT whose functions were determined experimentally, as opposed to proteins whose functions were predicted computationally. If this information was present in reasonable quantities, it would allow researchers to decrease the propagation of incorrect function predictions during sequence annotation, and to assemble training sets for developing the next generation of sequence-analysis algorithms. Second is to assess the consistency between translated GenBank sequences and sequences in SWISS-PROT and TrEMBL. RESULTS: (1) Contrary to claims by the SWISS-PROT authors, we conclude that SWISS-PROT does not identify a significant number of experimentally characterized proteins. (2) SWISS-PROT is more incomplete than we expected in that version 38.0 from July 1999 lacks many proteins from the full genomes of important organisms that were sequenced years earlier. (3) Even if we combine SWISS-PROT and TrEMBL, some sequences from the full genomes are missing from the combined dataset. (4) In many cases, translated GenBank genes do not exactly match the corresponding SWISS-PROT sequences, for reasons that include missing or removed methionines, differing translation start positions, individual amino-acid differences, and inclusion of sequence data from multiple sequencing projects. For example, results show that for Escherichia coli, 80.6% of the proteins in the GenBank entry for the complete genome have identical sequence matches with SWISS-PROT/TrEMBL sequences, 13.4% have exact substring matches, and matches for 4.1% can be found using BLAST search; the remaining 2.0% of E.coli protein sequences (most of which are ORFs) have no clear matches to SWISS-PROT/TrEMBL. Although many of these differences can be explained by the complexity of the DB, and by the curation processes used to create it, the scale of the differences is notable.

Algorithms↗
PubMed2005

Longitudinal analysis of pulmonary dysfunction in the initial years of employment in the grain industry.

The negative health effects of exposure to grain dust have previously been examined, but few studies have observed the effects on newly hired employees. Young grain workers are of interest because changes in pulmonary function may occur after a short duration of employment, and because older grain workers may represent a survivor population. The New Grain Workers Study (NGWS), a longitudinal study of 299 newly hired male grain industry workers, was conducted between 1980 and 1985. The objectives were to determine the effects of employment in the grain industry on pulmonary function. Pre-employment physical examinations and pulmonary function tests were conducted on subjects at the Division of Respiratory Medicine, Department of Medicine, Royal University Hospital, University of Saskatchewan. The Grain Dust Medical Surveillance Program (GDMSP) was a Labour Canada program that began in 1978. All subjects were grain workers employed in the grain industry in Saskatchewan. All subjects completed a respiratory symptoms questionnaire and underwent pulmonary function testing. Baseline observations were recorded every three years between 1978 and 1993. Data were available on 2184 grain workers. Generalized estimating equations were used to fit marginal and transitional multivariable regression models to determine the effects of grain dust exposure on pulmonary function. Marginal and transitional models were then compared. Height, exposure weeks, and previous FVC were predictive of FVC in the NGWS, while exposure weeks and previous FEV1 were predictive of FEV1. These models, as well as a transitional regression model built using the GDMSP data, were used to compute predicted mean annual decline inpulmonary function. Non-smoking grain workers in the NGWS had the highest pulmonary function test values, but also had the greatest predicted annual decline in pulmonary function. Ever-smoking grain workers in the GDMSP had the lowest pulmonary function test values. Non-smoking grain workers in the GDMSP had the least predicted annual decline in pulmonary function.

Adult↗
PubMed2003

The future for computational modelling and prediction systems in clinical immunology.

Advances in computational science, despite their enormous potential, have been surprisingly slow to impact on clinical practice. This paper examines the potential of bioinformatics to advance clinical immunology across a number of key examples including the use of computational immunology to improve renal transplantation outcomes, identify novel genes involved in immunological disorders, decipher the relationship between antigen presentation pathways and human disease, and predict allergenicity. These examples demonstrate the enormous potential for immunoinformatics to advance clinical and experimental immunology. The acceptance of immunoinformatic techniques by clinical and research immunologists will need robust standards of data quality, system integrity and properly validated immunoinformatic systems. Such validation, at a minimum, will require appropriately designed clinical studies conducted according to Good Clinical Practice standards. This strategy will enable immunoinformatics to achieve its full potential to advance and shape clinical immunology in the future.

Allergens↗
PubMed1999

An approximation of loop free energy values of RNA H-pseudoknots.

A set of free energy values is suggested for RNA H-pseudoknot loops. The parameters are adjusted to be consistent with the theory of polymer thermodynamics and known data on pseudoknots. The values can be used for estimates of pseudoknot stabilities and computer predictions of RNA structures.

Computer Simulation↗
PubMed2003

Sigma70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals.

We present here a computational analysis showing that sigma70 house-keeping promoters are located within zones with high densities of promoter-like signals in Escherichia coli, and we introduce strategies that allow for the correct computer prediction of sigma70 promoters. Based on 599 experimentally verified promoters of E.coli K-12, we generated and evaluated more than 200 weight matrices optimizing different criteria to obtain the best recognition matrices. The alignments generating the best statistical models did not fully correspond with the canonical sigma70 model. However, matrices that correspond to such a canonical model performed better as tools for prediction. We tested the predictive capacity of these matrices on 250 bp long regions upstream of gene starts, where 90% of the known promoters occur. The computational matrix models generated an average of 38 promoter-like signals within each 250 bp region. In more than 50% of the cases, the true promoter does not have the best score within the region. We observed, in fact, that real promoters occur mostly within regions with high densities of overlapping putative promoters. We evaluated several strategies to identify promoters. The best one uses an intrinsic score of the -10 and -35 hexamers that form the promoter as well as an extrinsic score that uses the distribution of promoters from the start of the gene. We were able to identify 86% true promoters correctly, generating an average of 4.7 putative promoters per region as output, of which 3.7, on average, exist in clusters, as a series of overlapping potentially competing RNA polymerase-binding sites. As far as we know, this is the highest predictive capability reported so far. This high signal density is found mainly within regions upstream of genes, contrasting with coding regions and regions located between convergently transcribed genes. These results are consistent with experimental evidence that show the existence of multiple overlapping promoter sites that become functional under particular conditions. This density is probably the consequence of a rich number of vestiges of promoters in evolution. We suggest that transcriptional regulators as well as other functional promoters play an important role in keeping these latent signals suppressed.

Bacterial Proteins↗
PubMed2004

Predicting subcellular localization of proteins using machine-learned classifiers.

MOTIVATION: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. RESULTS: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.

Algorithms↗
PubMed2005

Structure of Ca2+ release channel at 14 A resolution.

The 14 A resolution structure of the 2.3 MDa Ca2+ release channel (also known as RyR1) was determined by electron cryomicroscopy and single particle reconstruction. This structure was produced using collected data used for our previous published structures at 22-30 A resolution, but now taking advantage of recent algorithmic improvements in the EMAN software suite. This improved map clearly exhibits more structural detail and allows better defined docking of computationally predicted structural domain folds. Using sequence-based fold recognition, the N-terminal region of RyR1, residues 216-572, was predicted to have significant structural similarity with the IP3-binding core region of the type 1 IP3R. This putative structure was computationally localized to the clamp-shaped region of RyR1, which has been implicated to have a regulatory role in the channel activity.

Algorithms↗
PubMed1997

Formation of circular satellite tobacco ringspot virus RNA in protoplasts transiently expressing the linear RNA.

The most abundant form of the satellite RNA of tobacco ringspot virus (sTRSV RNA) is a linear, unit length molecule of 359 nucleotide residues, designated L-(+)M. A postulated replication scheme for the satellite RNA has as its first, and apparently virus-independent, step the ligation of L-(+)M into the corresponding circular form C-(+)M. We transiently expressed L-(+)M wild type and L-(+)M mutants in tobacco protoplasts using an African cassava mosaic geminivirus vector. Measured extents of C-(+)M accumulation were correlated with computer-predicted folding to suggest wild-type secondary structure elements that might be deleted without reducing ligation. A 127-nucleotide residue mutant L-(+)M was created by replacing, with 7 and 3 residues, respectively, nucleotide residues 53-211 and 268-350, each of which was predicted to form a set of three adjacent imperfect stem-loops in wild-type L-(+)M. The mutant L-(+)M was found to be extensively ligated to C-(+)M in protoplasts and to retain a calculated helix of the wild-type molecule that incorporates the 3' terminal sequence. A trinucleotide in the 3' region was mutated so as to disrupt and restore, respectively, the calculated helix, reducing and restoring, respectively, C-(+)M formation. These results suggest that the 3' stem contributes to the suitability of the small L-(+)M molecules as a substrate for a protoplast RNA ligase and that computed folding of sTRSV RNA may be predictive of sTRSV RNA structure in vivo.

Base Sequence↗
PubMed1990

Experimental and theoretical description of the isotachophoretic behavior of serum albumin.

The isotachophoretic behavior of serum albumin is examined for three anionic and one cationic electrolyte systems by (i) computer simulation, (ii) capillary isotachophoresis (ITP) and (iii) continuous flow ITP. The theoretical relationship between pH of the leading electrolyte and the steady state protein plateau concentration is presented for one of the anionic systems. With leading ion concentrations of the order of 10 mM, experimental protein plateau concentrations of 1.3-2.3% w/v are obtained. The computer predictions are approximately half these values.

Aminocaproic Acid↗
PubMed2005

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding.

Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

ATP-Binding Cassette Transporters↗
PubMed2005

SPdb--a signal peptide database.

BACKGROUND: The signal peptide plays an important role in protein targeting and protein translocation in both prokaryotic and eukaryotic cells. This transient, short peptide sequence functions like a postal address on an envelope by targeting proteins for secretion or for transfer to specific organelles for further processing. Understanding how signal peptides function is crucial in predicting where proteins are translocated. To support this understanding, we present SPdb signal peptide database http://proline.bic.nus.edu.sg/spdb, a repository of experimentally determined and computationally predicted signal peptides. RESULTS: SPdb integrates information from two sources (a) Swiss-Prot protein sequence database which is now part of UniProt and (b) EMBL nucleotide sequence database. The database update is semi-automated with human checking and verification of the data to ensure the correctness of the data stored. The latest release SPdb release 3.2 contains 18,146 entries of which 2,584 entries are experimentally verified signal sequences; the remaining 15,562 entries are either signal sequences that fail to meet our filtering criteria or entries that contain unverified signal sequences. CONCLUSION: SPdb is a manually curated database constructed to support the understanding and analysis of signal peptides. SPdb tracks the major updates of the two underlying primary databases thereby ensuring that its information remains up-to-date.

Databases, Nucleic Acid↗
PubMed1989

Use of artificial intelligence for the preoperative diagnosis of pulmonary lesions.

The relatively new field of artificial intelligence has spawned a variety of techniques associated with computer-assisted diagnosis. These techniques have been applied to the diagnosis of pulmonary lesions, but previous reports have focused on medical rather than surgical populations and the results have been evaluated using only retrospective patient surveys. We used a Bayesian algorithm to develop a diagnostic computer model for prospectively evaluating patients undergoing thoracotomy for suspected pulmonary malignancy. Patients who had a preoperative diagnosis were not included. Preoperative clinical and radiographic parameters for 100 consecutive patients were prospectively entered into the diagnostic model, which then categorized the lesion as benign or malignant. The computer predictions agreed with the final histological diagnosis in 95 of the 100 patients. The sensitivity was 96% and the specificity was 89% for this prospective series. These results indicate that the computer-assisted diagnosis of pulmonary lesions may have a role in this clinical setting.

Adult↗
PubMed2006

miRGen: a database for the study of animal microRNA genomic organization and function.

miRGen is an integrated database of (i) positional relationships between animal miRNAs and genomic annotation sets and (ii) animal miRNA targets according to combinations of widely used target prediction programs. A major goal of the database is the study of the relationship between miRNA genomic organization and miRNA function. This is made possible by three integrated and user friendly interfaces. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands and pseudogenes. These miRNAs are connected through the Targets interface to their experimentally supported target genes from TarBase, as well as computationally predicted target genes from optimized intersections and unions of several widely used mammalian target prediction programs. Finally, the Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance and provides specific functional information on the targets of miRNAs within each cluster. All of these unique features of miRGen are designed to facilitate investigations into miRNA genomic organization, co-transcription and targeting. miRGen can be freely accessed at http://www.diana.pcbi.upenn.edu/miRGen.

Animals↗
PubMed2007

Predicting single nucleotide polymorphisms (SNP) from DNA sequence by support vector machine.

Recently, SNP has gained substantial attention as genetic markers and is recognized as a key element in the development of personalized medicine. Computational prediction of SNP can be used as a guide for SNP discovery to reduce the cost and time needed for the development of personalized medicine. We have developed a method for SNP prediction based on support vector machines (SVMs) using different features extracted from the SNP data. Prediction rates of 60.9% was achieved by sequence feature, 59.1% by free-energy feature, 58.1% by GC content feature, 58.0% by melting temperature feature, 56.2% by enthalpy feature, 55.1% by entropy feature and 54.3% by the gene, exon and intron feature. We introduced a new feature, the SNP distribution score that achieved a prediction rate of 77.3%. Thus, the proposed SNP prediction algorithm can be used to in SNP discovery.

Algorithms↗
PubMed2003

Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence.

Classification of gene function remains one of the most important and demanding tasks in the post-genome era. Most of the current predictive computer methods rely on comparing features that are essentially linear to the protein sequence. However, features of a protein nonlinear to the sequence may also be predictive to its function. Machine learning methods, for instance the Support Vector Machines (SVMs), are particularly suitable for exploiting such features. In this work we introduce SVM and the pseudo-amino acid composition, a collection of nonlinear features extractable from protein sequence, to the field of protein function prediction. We have developed prototype SVMs for binary classification of rRNA-, RNA-, and DNA-binding proteins. Using a protein's amino acid composition and limited range correlation of hydrophobicity and solvent accessible surface area as input, each of the SVMs predicts whether the protein belongs to one of the three classes. In self-consistency and cross-validation tests, which measures the success of learning and prediction, respectively, the rRNA-binding SVM has consistently achieved >95% accuracy. The RNA- and DNA-binding SVMs demonstrate more diverse accuracy, ranging from approximately 76% to approximately 97%. Analysis of the test results suggests the directions of improving the SVMs.

Computational Biology↗
PubMed2005

Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.

Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity.

Computational Biology↗
PubMed2003

A bioinformatics-based approach for the prediction and identification of novel proteins potentially involved in phosphorylation signalling pathways.

Together with the explosion in the availability of genome data of a number of organisms including human and mouse, various methods and programs for computational prediction of protein-coding genes and annotation of functional proteins have dramatically increased. For the last decade there has been intense interest in the role of protein phosphorylation which is involved in post-translation modification mechanisms critically regulating inter/intracellular communication, patho/physiological responses and homeostasis during many biological processes. In the present study a total of 202 functionally uncharacterized human full-coding cDNA sequences were investigated using a bioinformatics-based approach. Ten novel potential substrates for protein kinases have been identified which may play multiple roles in regulating intracellular phosphorylation signalling pathways. In addition, 5 of those may be involved in the human-only post-translation mechanism regulated by specific protein kinases. The data presented here therefore would greatly contribute toward the understanding of human molecular basis and cellular signalling networks.

Amino Acid Motifs↗
PubMed2000

Construction and analysis of three-dimensional graphic model of single-chain Fv derived from an anti-human placental acidic isoferritin monoclonal antibody by computer.

A three-dimensional (3D) graphic model of a single-chain Fv (scFv) which was derived from an anti-human placental acidic isoferritin (PAF) monoclonal antibody (MAb) was constructed by a homologous protein-predicting computer algorithm on Silicon graphic computer station. The structure, surface static electricity and hydrophobicity of scFv were investigated. Computer graphic modelling indicated that all regions of scFv including the inker, variable regions of the heavy (VH) and light (VL) chains were suitable. The VH region and the VL region were involved in composing the "hydrophobic pocket". The linker was drifted away VH and VL regions. The complementarity determining regions (CDRs) of VH and VL regions surrounded the "hydrophobic pocket". This study provides a theory basis for improving antibody affinity, investigating antibody structure and analyzing the functions of VH and VL regions in antibody activity.

Amino Acid Sequence↗