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PubMed2005

Proteomics-based consensus prediction of protein retention in a bacterial membrane.

The availability of complete bacterial genome sequences allows proteome-wide predictions of exported proteins that are potentially retained in the cytoplasmic membranes of the corresponding organisms. In practice, however, major problems are encountered with the computer-assisted distinction between (Sec-type) signal peptides that direct exported proteins into the growth medium and lipoprotein signal peptides or amino-terminal membrane anchors that cause protein retention in the membrane. In the present studies, which were aimed at improving methods to predict protein retention in the bacterial cytoplasmic membrane, we have compared sets of membrane-attached and extracellular proteins of Bacillus subtilis that were recently identified through proteomics approaches. The results showed that three classes of membrane-attached proteins can be distinguished. Two classes include 43 lipoproteins and 48 proteins with an amino-terminal transmembrane segment, respectively. Remarkably, a third class includes 31 proteins that remain membrane-retained despite the presence of typical Sec-type signal peptides with consensus signal peptidase recognition sites. This unprecedented finding indicates that unknown mechanisms are involved in membrane retention of this class of proteins. A further novelty is a consensus sequence indicative for release of certain lipoproteins from the membrane by proteolytic shaving. Finally, using non-overlapping sets of secreted and membrane-retained proteins, the accuracy of different signal peptide prediction algorithms was assessed. Accuracy for the prediction of protein retention in the membrane was increased to 82% using a majority-vote approach. Our findings provide important leads for future identification of surface proteins from pathogenic bacteria, which are attractive candidate infection markers and potential targets for drugs or vaccines.

Amino Acid Sequence↗
PubMed1994

Development of a practical screening tool to identify preterm, low-birthweight neonates in Ethiopia.

Preterm, low-birthweight (LBW) newborn infants are at high risk of neonatal mortality and morbidity and need early referral for special paediatric care. In developing countries, birthweight and gestational age often cannot be measured and a practical screening tool based on surrogate neonatal body measurements to identify high-risk infants would be very useful. We studied a consecutive series of 843 singleton infants born at a referral hospital in Addis Ababa, Ethiopia. Gestational age, birthweight, and four body measurements (chest, head, and mid-arm circumferences and length) were accurately recorded. We randomly divided the series into equal-sized training and validation groups. In the training group, we used a recursive partitioning technique to develop a simple predictive algorithm--infants were classified as high risk if head circumference was 31 cm or less or if chest circumference was 30 cm or less, and were classified as low risk otherwise. When tested in the validation group, this algorithm had sensitivity, specificity, and negative predictive value for prediction of preterm and LBW births above 90%. Thus, neonatal body measurements can be combined into a pragmatic, accurate screening tool suitable for clinical use in developing countries.

Anthropometry↗
PubMed1987

An evaluation of a computer program to predict the outcome of hyaline membrane disease.

A computerized model was developed to predict the severity of hyaline membrane disease. This study compared the program's predictive ability with that of three neonatologists. Blood gas and respiratory support data of 33 infants were entered into the computer which then predicted the severity of disease. The computer predicted 18 outcomes correctly. The neonatologists, provided with the same data, predicted 24 outcomes correctly. Both sets of predictions--computer and physician--found that patients predicted to have mild outcomes had the shortest oxygen requirements, duration of mechanical ventilation, and length of hospitalization, and infants predicted to have severe outcomes had the greatest oxygen requirements, duration of mechanical ventilation, and length of hospitalization. While actual severity was linked to birth weight, the model did not utilize birth weight in its predictive algorithm. Consequently, although the computer program predicted the outcomes with moderate success, it was less accurate than the neonatologists.

Blood Gas Analysis↗
PubMed2004

Estimating degradation model parameters using neighborhood pattern distributions: an optimization approach.

Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. There are two related but distinct estimation scenarios. The first is model calibration, where it is assumed that the input ideal bitmap and the output of the degradation process are both known. The second is the general estimation problem, where only the image from the output of the degradation process is given. While researchers have addressed the problem of calibration of models, issues with the general estimation problems have not been addressed in the literature. In this paper, we describe a parameter estimation algorithm for a morphological, binary, page-level image degradation model. The inputs to the estimation algorithm are 1) the degraded image and 2) information regarding the font type (italic, bold, serif, sans serif). We simulate degraded images using our model and search for the optimal parameter by looking for a parameter value for which the local neighborhood pattern distributions in the simulated image and the given degraded image are most similar. The parameter space is searched using a direct search optimization algorithm. We use the p-value of the Kolmogorov-Smirnov test as the measure of similarity between the two neighborhood pattern distributions. We show results of our algorithm on degraded document images.

Algorithms↗
PubMed1975

Assessment of some problems associated with prediction of the three-dimensional structure of a protein from its amino-acid sequence.

It is shown that most present empirical prediction algorithms provide information about the conformational states of individual residues, but give little information about the three-dimensional structure of a protein. It is necessary to predict the conformational state of every residue before the resulting structure can serve as a starting conformation to compute the native structure. It is also shown that even a perfect five-state algorithm (which does not include long-range interactions from disulifide loop closing or solvation) will not lead to a globular structure resembling the native one. However, starting from the results of a perfect prediction algorithm, it appears that conformational energy minimization (with long-range interactions included) can lead to a structure having the general features of the native protein.

Amino Acid Sequence↗
PubMed2005

A direct comparison of drug susceptibility to HIV type 1 from antiretroviral experienced subjects as assessed by the antivirogram and PhenoSense assays and by seven resistance algorithms.

HIV-1 drug resistance methodologies are being increasingly utilized to guide treatment decisions; however, information comparing the various assays is limited. Duplicate plasma samples from 70 ART-experienced subjects were analyzed by both the Antivirogram and PhenoSense phenotypic assays and the results compared. HIV genotypes were also obtained and analyzed using seven different resistance algorithms. These results were also compared with the phenotypic assay results. Concordances between the phenotypic tests and between each algorithm, and between the two phenotypic assays were calculated and kappa coefficients (KC) determined. Overall agreement between the two phenotypic assays was good (86.9% concordance; KC 0.621). The highest concordance by drug class was seen for protease inhibitors (93.4%; KC 0.679) and the lowest (79.8%; KC 0.549) for nucleoside reverse transcriptase inhibitors. Concordance between the two phenotypic assays, when evaluating individual drugs, was good for all drugs tested except for abacavir, zalcitabine, and indinavir. Agreement between the seven algorithms and each phenotypic assay was variable, though most had good or excellent agreement. The highest overall level of agreement for an individual drug was observed when comparing lamivudine susceptibility to either assay. Concordance for abacavir, didanosine, zalcitabine, and saquinavir was generally problematic when comparing one or more phenotypic assays to the drug resistance predictive algorithms. In conclusion, results comparing these two phenotypic tests were mostly similar, but comparisons of the predictive resistance algorithms for specific drugs, as well as to specific phenotypic assays, were more inconsistent.

Adult↗
PubMed1998

Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations.

By incorporating predicted secondary and tertiary restraints derived from multiple sequence alignments into ab initio folding simulations, it has been possible to assemble native-like tertiary structures for a test set of 19 nonhomologous proteins ranging from 29 to 100 residues in length and representing all secondary structural classes. Secondary structural restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Multiple sequence alignments also provide predicted tertiary restraints via a two-step process: First, seed side chain contacts are selected from a correlated mutation analysis, and then an inverse folding algorithm expands these seed contacts. The predicted secondary and tertiary restraints are incorporated into a lattice-based, reduced protein model for structure assembly and refinement. The resulting native-like topologies exhibit a coordinate root-mean-square deviation from native for the whole chain between 3.1 and 6.7 A, with values ranging from 2.6 to 4.1 A over approximately 80% of the structure. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm is a promising approach to the prediction of the global topology of small proteins.

Algorithms↗
PubMed2004

Random screening of proteins for HLA-A*0201-binding nine-amino acid peptides is not sufficient for identifying CD8 T cell epitopes recognized in the context of HLA-A*0201.

HLA-A2 is the most frequent HLA molecule in Caucasians with HLA-A*0201 representing the most frequent allele; it was also the first human HLA allele for which peptide binding prediction was developed. The Bioinformatics and Molecular Analysis Section of the National Institutes of Health (BIMAS) and the University of Tübingen (Syfpeithi) provide the most popular prediction algorithms of peptide/MHC interaction on the World Wide Web. To test these predictions, HLA-A*0201-binding nine-amino acid peptides were searched by both algorithms in 19 structural CMV proteins. According to Syfpeithi, the top 2% of predicted peptides should contain the naturally presented epitopes in 80% of predictions (www.syfpeithi.de). Because of the high number of predicted peptides, the analysis was limited to 10 randomly chosen proteins. The top 2% of peptides predicted by both algorithms were synthesized corresponding to 261 peptides in total. PBMC from 10 HLA-A*0201-positive and CMV-seropositive healthy blood donors were tested by ex vivo stimulation with all 261 peptides using crossover peptide pools. IFN-gamma production in T cells measured by CFC was used as readout. However, only one peptide was found to be stimulating in one single donor. As a result of this work, we report a potential new T cell target protein, one previously unknown CD8-T cell-stimulating peptide, and an extensive list of CMV-derived potentially strong HLA-A*0201-binding peptides that are not recognized by T cells of HLA-A*0201-positive CMV-seropositive donors. We conclude that MHC/peptide binding predictions are helpful for locating epitopes in known target proteins but not necessarily for screening epitopes in proteins not known to be T cell targets.

Algorithms↗
PubMed1999

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.

Algorithms↗
PubMed2000

Ab initio gene finding in Drosophila genomic DNA.

Ab initio gene identification in the genomic sequence of Drosophila melanogaster was obtained using (human gene predictor) and Fgenesh programs that have organism-specific parameters for human, Drosophila, plants, yeast, and nematode. We did not use information about cDNA/EST in most predictions to model a real situation for finding new genes because information about complete cDNA is often absent or based on very small partial fragments. We investigated the accuracy of gene prediction on different levels and designed several schemes to predict an unambiguous set of genes (annotation CGG1), a set of reliable exons (annotation CGG2), and the most complete set of exons (annotation CGG3). For 49 genes, protein products of which have clear homologs in protein databases, predictions were recomputed by Fgenesh+ program. The first annotation serves as the optimal computational description of new sequence to be presented in a database. Reliable exons from the second annotation serve as good candidates for selecting the PCR primers for experimental work for gene structure verification. Our results shows that we can identify approximately 90% of coding nucleotides with 20% false positives. At the exon level we accurately predicted 65% of exons and 89% including overlapping exons with 49% false positives. Optimizing accuracy of prediction, we designed a gene identification scheme using Fgenesh, which provided sensitivity (Sn) = 98% and specificity (Sp) = 86% at the base level, Sn = 81% (97% including overlapping exons) and Sp = 58% at the exon level and Sn = 72% and Sp = 39% at the gene level (estimating sensitivity on std1 set and specificity on std3 set). In general, these results showed that computational gene prediction can be a reliable tool for annotating new genomic sequences, giving accurate information on 90% of coding sequences with 14% false positives. However, exact gene prediction (especially at the gene level) needs additional improvement using gene prediction algorithms. The program was also tested for predicting genes of human Chromosome 22 (the last variant of Fgenesh can analyze the whole chromosome sequence). This analysis has demonstrated that the 88% of manually annotated exons in Chromosome 22 were among the ab initio predicted exons. The suite of gene identification programs is available through the WWW server of Computational Genomics Group at http://genomic.sanger.ac.uk/gf. html.

Algorithms↗
PubMed1989

QRS feature extraction using linear prediction.

This communication proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. This communication also indicates that the prediction order need not be more than two for fast arrhythmia detection. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex, the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to a set of three states pulse-code train relative to the original ECG signal. The pulse-code train has the advantage of easy implementation in digital hardware circuits to achieve automated ECG diagnosis. The algorithm performs very well in feature extraction in arrhythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contraction) detection has at least a 92 percent sensitivity for MIT/BIH arrhythmia database.

Algorithms↗
PubMed2005

Pocketome via comprehensive identification and classification of ligand binding envelopes.

We developed a new computational algorithm for the accurate identification of ligand binding envelopes rather than surface binding sites. We performed a large scale classification of the identified envelopes according to their shape and physicochemical properties. The predicting algorithm, called PocketFinder, uses a transformation of the Lennard-Jones potential calculated from a three-dimensional protein structure and does not require any knowledge about a potential ligand molecule. We validated this algorithm using two systematically collected data sets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank, 5616 and 11,510, respectively. As many as 96.8% of experimental binding sites were predicted at better than 50% overlap level. Furthermore 95.0% of the asserted sites from the apo receptors were predicted at the same level. We demonstrate that conformational differences between the apo and bound pockets do not dramatically affect the prediction results. The algorithm can be used to predict ligand binding pockets of uncharacterized protein structures, suggest new allosteric pockets, evaluate feasibility of protein-protein interaction inhibition, and prioritize molecular targets. Finally the data base of the known and predicted binding pockets for the human proteome structures, the human pocketome, was collected and classified. The pocketome can be used for rapid evaluation of possible binding partners of a given chemical compound.

Algorithms↗
PubMed1999

Prediction of antisense oligonucleotide binding affinity to a structured RNA target.

Antisense oligonucleotides, which act through the pairing of complementary bases to an RNA target sequence, are showing great promise in research and clinical applications. However, the selection of effective antisense oligonucleotides has proven more difficult than initially presumed. We developed a prediction algorithm to identify those sequences with the highest predicted binding affinity for their target mRNA based on a thermodynamic cycle that accounts for the energetics of structural alterations in both the target mRNA and the oligonucleotide. The model was used to predict the binding affinity of antisense oligonucleotides complementary to the rabbit beta-globin (RBG) and mouse tumor necrosis factor-alpha (TNFalpha) mRNAs, for which large experimental datasets were available. Of the top ten candidates identified by the algorithm for the RBG mRNA, six were the most strongly binding sequences determined from an experimental assay. The prediction for the TNFalpha mRNA also identified high affinity sequences with approximately 60% accuracy. Computational prediction of antisense efficacy is more cost-efficient and faster than in vitro or in vivo selection and can potentially speed the development of sequences for both research and clinical applications.

Animals↗
PubMed2006

Influence of methods used in body composition analysis on the prediction of resting energy expenditure.

OBJECTIVE: There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. DESIGN: In a cross-sectional design measurements of REE and body composition were performed. SUBJECTS: The study population consisted of 50 men (age 37.1+/-15.1 years, body mass index (BMI) 25.9+/-4.1 kg/m2) and 54 women (age 35.3+/-15.4 years, BMI 25.5+/-4.4 kg/m2). INTERVENTIONS: REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D2O). A 4-compartment (4C)-model was used as a reference. RESULTS: When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFM(ADP)) to 1645 kJ/24 h (FFM(SF)) and the slopes ranged between 100.3 kJ (FFM(SF)) and 108.1 kJ/FFM (kg) (FFM(ADP)). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFM(BIA)) to 75% (FFM(DXA)) and was only 46% for body weight. CONCLUSION: Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population- and/or investigator specificity of algorithms for REE prediction.

Absorptiometry, Photon↗
PubMed2000

Largest lyapunov-exponent estimation and selective prediction by means of simplex forecast algorithms

Limited predictability is one of the remarkable features of deterministic chaos and this feature may be quantized in terms of Lyapunov exponents. Accordingly, Lyapunov-exponent estimates may be expected to follow in a natural way from forecast algorithms. Exploring this idea, we propose a method estimating the largest Lyapunov exponent from a time series which uses the behavior of so-called simplex forecasts. The method considers the estimation of properties of the distribution of local simplex expansion coefficients. These are also used for the definition of error bars for the Lyapunov-exponent estimates and allows for selective forecasts with improved prediction accuracy. We demonstrate these concepts on standard test examples and three realistic applications to time series concerning largest Lyapunov-exponent estimation of an experimentally obtained hyperchaotic NMR signal, brain state differentiation, and stock-market prediction.

Journal Article↗
PubMed1997

Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm.

Water-mediated ligand interactions are essential to biological processes, from product displacement in thymidylate synthase to DNA recognition by Trp repressor, yet the structural chemistry influencing whether bound water is displaced or participates in ligand binding is not well characterized. Consolv, employing a hybrid k-nearest-neighbors classifier/genetic algorithm, predicts bound water molecules conserved between free and ligand-bound protein structures by examining the environment of each water molecule in the free structure. Four environmental features are used: the water molecule's crystallographic temperature factor, the number of hydrogen bonds between the water molecule and protein, and the density and hydrophilicity of neighboring protein atoms. After training on 13 non-homologous proteins, Consolv predicted the conservation of active-site water molecules upon ligand binding with 75% accuracy (Matthews coefficient Cm = 0.41) for seven new proteins. Mispredictions typically involved water molecules predicted to be conserved that were displaced by a polar ligand atom, indicating that Consolv correctly assesses polar binding sites; 90% accuracy (Cm = 0.78) was achieved for predicting conserved active-site water or polar ligand atom binding. Consolv thus provides an accurate means for optimizing ligand design by identifying sites favored to be occupied by either a mediating water molecule or a polar ligand atom, as well as water molecules likely to be displaced by the ligand. Accuracy for predicting first-shell water conservation between independently determined structures was 61% (Cm=0.23). The ability to predict water-mediated and polar interactions from the free protein structure indicates the surprising extent to which the conservation or displacement of active-site bound water is independent of the ligand, and shows that the protein micro-environment of each water molecule is the dominant influence.

Algorithms↗
PubMed2006

Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies.

PURPOSE: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle. METHODS: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics. RESULTS: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns. CONCLUSIONS: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.

Adolescent↗
PubMed2006

Predicting free recalls.

This article reports some calculations on free-recall data from B. Murdock and J. Metcalfe (1978), with vocal rehearsal during the presentation of a list. Given the sequence of vocalizations, with the stimuli inserted in their proper places, it is possible to predict the subsequent sequence of recalls--the predictions taking the form of a probability distribution over all possible such sequences. The predictions are parameter free. This article looks at (a) the accuracy of those predictions and how that accuracy can be validated, (b) the principles on which the predictive algorithm is based, and (c) the limits to predictability. Some implications for the understanding of free recall are spelled out.

Algorithms↗