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Tertiary structure prediction using mean-force potentials and internal energy functions: successful prediction for coiled-coil geometries.

We report a preliminary study of the use of mean-force potentials (MFPs) for predicting protein tertiary structure. For three leucine zipper sequences, we have calculated ensembles of structures spanning all possible backbone conformations consistent with the canonical coiled-coil geometry. MFPs were measured with the program PROSA. The MFP alone was poor at discriminating the native structure from very divergent structures, and the global minimum of the MFP sometimes occurred far from the native structure. We found that adding an internal energy function (a subset of the CHARMM potential that describes only interactions between backbone atoms), the resultant total energy (CHARMM+PROSA) performed much better; in each case, there was a clear positive correlation between total energy and root-mean-square deviation (RMSD) from the experimental structure, and the lowest-energy structures were about 1 A RMSD from the experimental structures. Thus, we conclude that the combined potential is a powerful method for predicting leucine zippers and is very promising for general 3D structure prediction.

Leucine Zippers↗

Prediction of obliteration of arteriovenous malformations after radiosurgery: the obliteration prediction index.

OBJECTIVE: To describe the response to single dose photon stereotactic radiosurgery of arteriovenous malformations (AVMs) so that the probability of success or failure of treatment may be predicted for the individual patient. METHOD: The obliteration prediction index (OPI) was calculated for AVMs by dividing the marginal dose of radiation in Gray (Gy) by the lesion diameter in centimetres in cohorts of 42 patients treated with the modified linear accelerator at Toronto-Sunnybrook Regional Cancer Centre and 394 patients treated with the gamma unit at the Royal Hallamshire Hospital, Sheffield, United Kingdom. Patients were grouped into ranges by OPI and the proportion of success and failure was calculated for each group. An exponential function [P = 1-A.e(-B.OPI)] was fitted to the data by the least squares method. RESULTS: Despite systematic differences in radiation treatment, that is, marginal doses of 15 and 20 Gy in Toronto and most Sheffield patients with a marginal dose of 25 Gy, the resultant data points exhibited similar behaviour. CONCLUSION: The function [P = 1-A.e(-B.OPI)] partly describes the biological effect of radiation and is independent of the radiation device used. Radiosurgery centres can use this model to facilitate predictions of successful treatment for individual patients.

Follow-Up Studies↗

Exploration of the potential energy surfaces, prediction of atmospheric concentrations, and prediction of vibrational spectra for the HO2...(H2O)n (n = 1-2) hydrogen bonded complexes.

The hydroperoxy radical (HO2) plays a critical role in Earth's atmospheric chemistry as a component of many important reactions. The self-reaction of hydroperoxy radicals in the gas phase is strongly affected by the presence of water vapor. In this work, we explore the potential energy surfaces of hydroperoxy radicals hydrogen bonded to one or two water molecules, and predict atmospheric concentrations and vibrational spectra of these complexes. We predict that when the HO2 concentration is on the order of 10(8) molecules x cm(-3) at 298 K, that the number of HO2...H2O complexes is on the order of 10(7) molecules x cm(-3) and the number of HO2...(H2O)2 complexes is on the order of 10(6) molecules x cm(-3). Using the computed abundance of HO2...H2O, we predict that, at 298 K, the bimolecular rate constant for HO2...H2O + HO2 is about 10 times that for HO2 + HO2.

Atmosphere↗

Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood-brain barrier penetration.

This paper describes the derivation of a simple QSAR model for the prediction of log BB from a set of 55 diverse organic compounds. The model contains two variables: polar surface area (PSA) and calculated logP, both of which can be rapidly computed. It therefore permits the prediction of log BB for large compound sets, such as virtual combinatorial libraries. The performance of this QSAR on two test sets taken from the literature is illustrated and compared with results from other reported computational approaches to log BB prediction.

Algorithms↗

The psychological consequences of predictive testing for Huntington's disease. Canadian Collaborative Study of Predictive Testing.

BACKGROUND: Advances in molecular genetics have led to the development of tests that can predict the risk of inheriting the genes for several adult-onset diseases. However, the psychological consequences of such testing are not well understood. METHODS: The 135 participants in the Canadian program of genetic testing to predict the risk of Huntington's disease were followed prospectively in three groups according to their test results: the increased-risk group (37 participants), the decreased-risk group (58 participants), and the group with no change in risk (the no-change group) (40 participants). All the participants received counseling before and after testing. Standard measures of psychological distress (the General Severity Index of the Symptom Check List 90-R), depression (the Beck Depression Inventory), and well-being (the General Well-Being Scale) were administered before genetic testing and again at intervals of 7 to 10 days, 6 months, and 12 months after the participants received their test results. RESULTS: At each follow-up assessment, the decreased-risk group had lower scores for distress than before testing (P < 0.001). The increased-risk group showed no significant change from base line on any follow-up measure, but over the year of study there were small linear declines (P < 0.023) for distress and depression. The no-change group had scores lower than at base line on the index of general well-being at each follow-up (P < or = 0.045). At the 12-month follow-up, both the increased-risk group and the decreased-risk group had lower scores for depression and higher scores for well-being than the no-change group (P < or = 0.049). CONCLUSIONS: Predictive testing for Huntington's disease has potential benefits for the psychological health of persons who receive results that indicate either an increase or a decrease in the risk of inheriting the gene for the disease.

Adult↗

The preterm prediction study: quantitative fetal fibronectin values and the prediction of spontaneous preterm birth. The National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network.

OBJECTIVE: A cervicovaginal fetal fibronectin value of >/=50 ng/mL has been used to define women at risk of having a preterm birth. We evaluated the relationship between quantitative fetal fibronectin values and spontaneous preterm birth. STUDY DESIGN: Cervical and vaginal specimens for fetal fibronectin were obtained at 24, 26, 28, and 30 weeks' gestation from 2926 women. Quantitative fetal fibronectin values were calculated by using absorbances determined by enzyme-linked immunosorbent assay. The highest fetal fibronectin value (cervical or vaginal) for each woman at each visit was evaluated in relation to spontaneous preterm birth at <35 weeks' gestation. Receiver operating characteristic curves were constructed to determine the optimal cutoff point for fetal fibronectin values to predict spontaneous preterm birth at <35 weeks' gestation and within 4 weeks of testing. RESULTS: The risk of spontaneous preterm birth increased as a function of increasing fetal fibronectin values from approximately 20 to 300 ng/mL. Fetal fibronectin values > or =300 ng/mL were not associated with a further increase in spontaneous preterm birth. Examination of the receiver operating characteristic curve indicates that the optimal cutoff point for a positive fetal fibronectin test result at 24 to 30 weeks' gestation to predict spontaneous preterm birth at <35 weeks is between 45 and 60 ng/mL. CONCLUSION: Increasing levels of cervicovaginal fetal fibronectin up to 300 ng/mL are associated with an increasing risk of spontaneous preterm birth. Nevertheless, at 24 to 30 weeks, the value currently used, 50 ng of fetal fibronectin per milliliter, appears to be a reasonable cutoff point for predicting spontaneous preterm birth at <35 weeks' gestation.

Cervix Uteri↗

The Preterm Prediction Study: the value of serum alkaline phosphatase, alpha-fetoprotein, plasma corticotropin-releasing hormone, and other serum markers for the prediction of spontaneous preterm birth.

OBJECTIVE: High levels of a number of analytes are found in maternal blood; alkaline phosphatase,alpha-fetoprotein, and corticotropin-releasing hormone have been associated with spontaneous preterm birth. We investigated the relationship between 8 potential blood markers and subsequent spontaneous preterm birth in asymptomatic pregnant women. STUDY DESIGN: We performed a nested case control study that involved 127 women who were enrolled in the preterm prediction study and who had a singleton spontaneous preterm birth at <35 weeks and 127 women who had a term birth and served as matched (age, parity, center) controls. Serum that was collected at 24 and 28 weeks was analyzed for alkaline phosphatase, alpha-fetoprotein, corticotropin-releasing hormone, and 5 other analytes. RESULTS: Alkaline phosphatase, alpha-fetoprotein, and corticotropin-releasing hormone, but not other analytes, were significantly elevated in pregnancies that ended in spontaneous preterm birth. For alkaline phosphatase at 24 weeks, the odds ratio for spontaneous preterm birth at <32 weeks was 6.8 (range, 1.4-32.8) and for spontaneous preterm birth at <35 weeks 5.1 (range, 1.7-15.6). Similar results were found at 28 weeks. For alpha-fetoprotein at 24 weeks, the odds ratio for spontaneous preterm birth at <32 weeks was 8.3 (range,2.2-30.9) and for spontaneous preterm birth at <35 weeks was 3.5 (range, 1.8-6.7). The levels at 28 weeks were still predictive but less so than at 24 weeks. Corticotropin-releasing hormone, at 28 weeks but not at 24 weeks, was predictive for spontaneous preterm birth at <35 weeks, with an odds ratio 3.4 (range, 1.0-10.9). CONCLUSION: Elevated alkaline phosphatase and alpha-fetoprotein are associated with subsequent spontaneous preterm birth in asymptomatic pregnant women at 24 and 28 weeks. Elevated corticotropin-releasing hormone levels at 28 weeks are associated with spontaneous preterm birth at <35 weeks.

Alkaline Phosphatase↗

Gene expression profiles in hepatitis C virus (HCV) and HIV coinfection: class prediction analyses before treatment predict the outcome of anti-HCV therapy among HIV-coinfected persons.

Therapy for hepatitis C virus (HCV) infection in human immunodeficiency virus (HIV)-infected patients results in modest cure rates. Gene expression patterns in peripheral blood mononuclear cells from 29 patients coinfected with HIV and HCV were used to predict virological response to therapy for HCV infection. Prediction analysis using pretherapy samples identified 79 genes that correctly classified all 10 patients who did not respond to therapy, 8 of 10 patients with a response at the end of treatment, and 7 of 9 patients with sustained virological response (86% overall). Analysis of 17 posttreatment samples identified 105 genes that correctly classified all 9 patients with response at the end of treatment and 7 of 8 patients with sustained virological response (94% overall). Failure of anti-HCV therapy was associated with elevated expression of interferon-stimulated genes. Gene expression patterns may provide a tool to predict anti-HCV therapeutic response.

Adult↗

How homologs can help to predict protein folds even though they cannot be predicted for individual sequences.

At present, one cannot predict the 3D structure of a protein directly from its sequence alone mainly because of errors in the energy estimates. However, a recently developed simple analytical theory (Finkelstein, 1998) shows that using a set of homologs (i.e., chains with numerous amino acid mutations but with equal 3D folds) one can average the interaction energies over the homologs and predict their common 3D fold even when predictions for individual sequences are wrong because the energy parameters are known only approximately. In this work we verify this theoretical conclusion by computer simulations performed with simplified models of protein chains.

Computer Simulation↗

Predicting protein structure classes from function predictions.

MOTIVATION: We introduce a new approach to using the information contained in sequence-to-function prediction data in order to recognize protein template classes, a critical step in predicting protein structure. The data on which our method is based comprise probabilities of functional categories; for given query sequences these probabilities are obtained by a neural net that has previously been trained on a variety of functionally important features. On a training set of sequences we assess the relevance of individual functional categories for identifying a given structural family. Using a combination of the most relevant categories, the likelihood of a query sequence to belong to a specific family can be estimated. RESULTS: The performance of the method is evaluated using cross-validation. For a fixed structural family and for every sequence, a score is calculated that measures the evidence for family membership. Even for structural families of small size, family members receive significantly higher scores. For some examples, we show that the relevant functional features identified by this method are biologically meaningful. The proposed approach can be used to improve existing sequence-to-structure prediction methods. AVAILABILITY: Matlab code is available on request from the authors. The data are available at http://www.mpisb.mpg.de/~sommer/Fun2Struc/

Algorithms↗

Repeated enflurane anaesthetics and model predictions: a study of the variability in the predictive performance measures.

We quantified the total variability (reproducibility) and the within-patient but between repeat anaesthetics variability (repeatability) in measures which are used to judge the predictive performance of our physiological model. We studied 14 patients who received enflurane closed-circuit anaesthesia on two occasions. The end-tidal concentrations measured and those predicted served to calculate the predictive performance measures of the model: root mean squared error (rmse = total error), bias (systematic error) and scatter (error around the bias). The overall results were: rmse 15 (7)%, bias 0 (14)% and scatter 9 (3)% (grand mean (total SD)). The within-patient SD values were smaller for the rmse (4%) and bias (10%), but not for scatter (3%). The repeat rmse values and biases were linked to the first results. This implies that these performance measures depended partly on the patient. As there was no association between the personal performance measures and age, sex, body weight, body surface area or body mass index, these characteristics cannot be used to further tune the model.

Adolescent↗

Prediction of coronary events in a low incidence population. Assessing accuracy of the CUORE Cohort Study prediction equation.

BACKGROUND: The aims of this paper are to derive a 10-year coronary risk predictive equation for adult Italian men, and to assess its accuracy in comparison with the Framingham Heart Study (FHS) and PROCAM study equations. METHODS: The CUORE study is a prospective fixed-cohort study. Eleven cohorts, from the north and the centre-south of Italy, had been investigated at baseline between 1982 and 1996, adopting MONICA methods to measure risk factors. Among this sample of 6865 men, aged 35-69 years and free of coronary heart disease (CHD) at baseline, 312 first fatal and non-fatal major coronary events occurred in 9.1 years median follow-up. Calibration, as the difference between 10-year predicted and actual risk, and discrimination, as the ability of the risk functions to separate high-risk from low-risk subjects, have been assessed to compare accuracy of the FHS, the PROCAM, and the CUORE study equations. RESULTS: The best CUORE equation includes age, total cholesterol, systolic blood pressure, cigarette smoking, HDL-cholesterol, diabetes mellitus, hypertension drug treatment, and family history of CHD (area under the ROC curve = 0.75). The uncalibrated estimates of the 10-year risk in this CUORE follow-up data were 0.093 and 0.109 higher (P < 0.05) from the Framingham and PROCAM risk scores, respectively, than the Kaplan-Meier estimate for CUORE, indicating risk overestimates for both equations. Standard recalibration techniques improved accuracy of the FHS equation only. PROCAM overestimates were prominent in the higher risk deciles. With an alternative method for recalibration better risk estimates were obtained, but a cohort study was needed to obtain a properly calibrated risk equation. CONCLUSIONS: The CUORE Project predictive equation showed better accuracy of the FHS and PROCAM equations, overcoming frequently reported risk overestimates. The CUORE equation may be adopted to identify men with high coronary risk in Italy.

Adult↗

Predictive and potentially predictive factors in early arthritis: a multidisciplinary approach.

OBJECTIVES: Rheumatoid arthritis (RA) is characterized by variable degrees of joint inflammation, joint destruction, progressive disability and premature death. Destruction of joint cartilage and bone may occur early during disease, as was shown in longitudinal studies of RA, and there is increasing consent among rheumatologists that early diagnosis and early initiation of therapy with disease-modifying anti-rheumatic drugs (DMARDs) can limit the severity of RA. Unfortunately, the currently used diagnostic and predictive indicators (clinical, laboratory and radiological) are of limited value for making an early diagnosis and prognosis of the disease course at the individual level, thus reducing optimal benefit from present and emerging therapies. Therefore, this review focuses on the multidisciplinary aspects of neuroendocrine-immune changes in RA. METHODS: A Medline search was performed using the search terms 'androgens', 'estrogens', 'sympathetic nervous system', 'sensory nervous system', 'prognosis', 'early rheumatoid arthritis', 'arthritis' and 'studies' in various combinations. For the tabular overview, we only listed clinical studies focusing on endocrine and neuronal aspects. RESULTS: In addition to the currently used predictive indicators, there is an abundant body of literature describing changes of the neuronal, endocrine and immune parameters during inflammatory diseases. Unfortunately, no longitudinal studies concerning neuroendocrine aspects have been done up to now. CONCLUSION: Parameters of the neuroendocrine system should be included in anticipated longitudinal clinical studies to find their true predictive value in early RA.

Arthritis, Rheumatoid↗

Prediction of the protein requirements of farm ruminants and implications of these predictions for diet formulation.

Methods of determining protein requirements are reviewed and recent proposals of the Agricultural Research Council working party on nutrient requirements of ruminants outlined. Needs of the rumen microorganisms for degradable nitrogen to achieve optimum rumen digestion of feed are predicted. The extent to which milk production and live-weight gain can be sustained by microbial protein alone is estimated. Higher milk yields and rates of growth require dietary protein that escapes degradation in the rumen but is digested in the small intestine. Small changes in degradability of dietary protein are predicted to have a large effect on the dietary crude protein requirement. Although there is still inadequate data for precise prediction, the concepts of the metabolic approach have been valuable in understanding those physiological situations where protein is most likely to be limiting, where use of protected proteins and urea might be most appropriate, in the planning of critical experiments and in the design of new methods of feeding or management of ruminants.

Animal Feed↗

Predicting the distribution of synaptic strengths and cell firing correlations in a self-organizing, sequence prediction model.

This article investigates the synaptic weight distribution of a self-supervised, sparse, and randomly connected recurrent network inspired by hippocampal region CA3. This network solves nontrivial sequence prediction problems by creating, on a neuron-by-neuron basis, special patterns of cell firing called local context units. These specialized patterns of cell firing--possibly an analog of hippocampal place cells--allow accurate prediction of the statistical distribution of synaptic weights, and this distribution is not at all gaussian. Aside from the majority of synapses that are, at least functionally, lost due to synaptic depression, the distribution is approximately uniform. Unexpectedly, this result is relatively independent of the input environment, and the uniform distribution of synaptic weights can be approximately parameterized based solely on the average activity level. Next, the results are generalized to other cell firing types (frequency codes and stochastic firing) and place cell-like firing distributions. Finally, we note that our predictions concerning the synaptic strength distribution can be extended to the distribution of correlated cell firings. Recent published neurophysiological results are consistent with this extension.

Electrophysiology↗

REGANOR: a gene prediction server for prokaryotic genomes and a database of high quality gene predictions for prokaryotes.

UNLABELLED: With >1,000 prokaryotic genome sequencing projects ongoing or already finished, comprehensive comparative analysis of the gene content of these genomes has become viable. To allow for a meaningful comparative analysis, gene prediction of the various genomes should be as accurate as possible. It is clear that improving the state of genome annotation requires automated gene identification methods to cope with the influence of artifacts, such as genomic GC content. There is currently still room for improvement in the state of annotations. We present a web server and a database of high-quality gene predictions. The web server is a resource for gene identification in prokaryote genome sequences. It implements our previously described, accurate gene finding method REGANOR. We also provide novel gene predictions for 241 complete, or almost complete, prokaryotic genomes. We demonstrate how this resource can easily be utilised to identify promising candidates for currently missing genes from genome annotations with several examples. All data sets are available online. AVAILABILITY: The gene finding server is accessible via https://www.cebitec.uni-bielefeld.de/groups/brf/software/reganor/cgi-bin/reganor_upload.cgi. The server software is available with the GenDB genome annotation system (version 2.2.1 onwards) under the GNU general public license. The software can be downloaded from https://sourceforge.net/projects/gendb/. More information on installing GenDB and REGANOR and the system requirements can be found on the GenDB project page http://www.cebitec.uni-bielefeld.de/groups/brf/software/wiki/GenDBWiki/AdministratorDocumentation/GenDBInstallation

Chromosome Mapping↗

Discriminant analysis for predicting dystocia in beef cattle. II. Derivation and validation of a prebreeding prediction model.

Discriminant analysis was utilized to derive and validate a model for predicting dystocia using only data available at the beginning of the breeding season. Data were collected from 211 Chianina crossbred cows (2 to 6 yr old) bred to Chianina bulls. A proportionally stratified sampling procedure divided females into an analysis sample (n = 134) on which the model was derived and a hold-out sample (n = 77) on which the prediction model was validated (tested). Variables available during the derivation stage were cow age, cow weight, pelvic height, pelvic width, pelvic area and calf sire. Dystocia was categorized as either unassisted or assisted. Occurrence of dystocia was 17.2 and 18.2% in the analysis and hold-out samples, respectively. All data were standardized to a mean of zero and a variance of one before statistical analysis. The centroid of cows experiencing dystocia differed (P less than .01) from that of cows calving unassisted in the analysis sample. Significant variables were pelvic area and cow age (standardized coefficients = .56 and .51, respectively). This model correctly classified 85.1% of the cows in the analysis sample. This was 13.5% greater than the proportional chance criterion. For model validation, prediction accuracy was 84.4% in the hold-out group, which was 14.2% greater than the proportional chance criterion. However, only 57.1% of the cows that experienced dystocia were correctly classified. Examination of the data revealed that those cows misclassified were 3 yr of age or older.(ABSTRACT TRUNCATED AT 250 WORDS)

Age Factors↗

Value seeking and prediction-decision inconsistency: why don't people take what they predict they'll like the most?

In this research, it is proposed that, when making a choice between consumption goods, people do not just think about which option will deliver the highest consumption utility but also think about which choice is most consistent with rationales--beliefs about how they should make decisions. The present article examines a specific rationale, value seeking. The value-seeking rationale refers to the belief that one should choose the option in a choice set that has the highest monetary value. Studies 1 and 2 show that value seeking could lead to a prediction-decision inconsistency, predicting a high consumption utility from one option but choosing another option. Study 3 shows that the prediction-decision inconsistency could be created even by "illusory" (as opposed to truly monetary) values and that the inconsistency could be turned on or off through empirical manipulation.

Adult↗