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PubMed2005

Evaluation of domain prediction in CASP6.

We present an analysis of the domain boundary prediction, a new category, in the sixth community-wide experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP6). There were 1011 predictions submitted for 63 targets. Each prediction was compared to the set of domains defined manually by visual inspection of the experimental structure. The comparison was scored using a new domain prediction scoring scheme. As the definition of a domain is subjective, many targets were assigned alternate definitions. For such targets, each prediction was compared with all different definitions and the best score was chosen. The predictors found it difficult to accurately predict domain boundaries when the target protein contained many domains or domains made of multiple sequence segments. The CBRC-DR (P0536) and Sternberg (P0237) groups were the most successful among human experts, while Baker-Rossettadom (P0353) and Baker-Robetta-Ginzu (P0421) did well among servers.

Algorithms↗
PubMed2006

Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching.

The formation of disulphide bridges between cysteines plays an important role in protein folding, structure, function, and evolution. Here, we develop new methods for predicting disulphide bridges in proteins. We first build a large curated data set of proteins containing disulphide bridges to extract relevant statistics. We then use kernel methods to predict whether a given protein chain contains intrachain disulphide bridges or not, and recursive neural networks to predict the bonding probabilities of each pair of cysteines in the chain. These probabilities in turn lead to an accurate estimation of the total number of disulphide bridges and to a weighted graph matching problem that can be addressed efficiently to infer the global disulphide bridge connectivity pattern. This approach can be applied both in situations where the bonded state of each cysteine is known, or in ab initio mode where the state is unknown. Furthermore, it can easily cope with chains containing an arbitrary number of disulphide bridges, overcoming one of the major limitations of previous approaches. It can classify individual cysteine residues as bonded or nonbonded with 87% specificity and 89% sensitivity. The estimate for the total number of bridges in each chain is correct 71% of the times, and within one from the true value over 94% of the times. The prediction of the overall disulphide connectivity pattern is exact in about 51% of the chains. In addition to using profiles in the input to leverage evolutionary information, including true (but not predicted) secondary structure and solvent accessibility information yields small but noticeable improvements. Finally, once the system is trained, predictions can be computed rapidly on a proteomic or protein-engineering scale. The disulphide bridge prediction server (DIpro), software, and datasets are available through www.igb.uci.edu/servers/psss.html.

Amino Acid Sequence↗
PubMed2006

Assessing a novel approach for predicting local 3D protein structures from sequence.

We developed a novel approach for predicting local protein structure from sequence. It relies on the Hybrid Protein Model (HPM), an unsupervised clustering method we previously developed. This model learns three-dimensional protein fragments encoded into a structural alphabet of 16 protein blocks (PBs). Here, we focused on 11-residue fragments encoded as a series of seven PBs and used HPM to cluster them according to their local similarities. We thus built a library of 120 overlapping prototypes (mean fragments from each cluster), with good three-dimensional local approximation, i.e., a mean accuracy of 1.61 A Calpha root-mean-square distance. Our prediction method is intended to optimize the exploitation of the sequence-structure relations deduced from this library of long protein fragments. This was achieved by setting up a system of 120 experts, each defined by logistic regression to optimize the discrimination from sequence of a given prototype relative to the others. For a target sequence window, the experts computed probabilities of sequence-structure compatibility for the prototypes and ranked them, proposing the top scorers as structural candidates. Predictions were defined as successful when a prototype <2.5 A from the true local structure was found among those proposed. Our strategy yielded a prediction rate of 51.2% for an average of 4.2 candidates per sequence window. We also proposed a confidence index to estimate prediction quality. Our approach predicts from sequence alone and will thus provide valuable information for proteins without structural homologs. Candidates will also contribute to global structure prediction by fragment assembly.

Amino Acid Sequence↗
PubMed2006

Better prediction of the location of alpha-turns in proteins with support vector machine.

We have developed a novel method named AlphaTurn to predict alpha-turns in proteins based on the support vector machine (SVM). The prediction was done on a data set of 469 nonhomologous proteins containing 967 alpha-turns. A great improvement in prediction performance was achieved by using multiple sequence alignment generated by PSI-BLAST as input instead of the single amino acid sequence. The introduction of secondary structure information predicted by PSIPRED also improved the prediction performance. Moreover, we handled the very uneven data set by combining the cost factor j with the "state-shifting" rule. This further promoted the prediction quality of our method. The final SVM model yielded a Matthews correlation coefficient (MCC) of 0.25 by a 10-fold cross-validation. To our knowledge, this MCC value is the highest obtained so far for predicting alpha-turns. An online Web server based on this method has been developed and can be freely accessed at http://bmc.hust.edu.cn/bioinformatics/ or http://210.42.106.80/.

Amino Acid Sequence↗
PubMed1989

Secondary structural analysis of retrovirus integrase: characterization by circular dichroism and empirical prediction methods.

The retrovirus integrase (IN) protein is essential for integration of viral DNA into host DNA. The secondary structure of the purified IN protein from avian myeloblastosis virus was investigated by both circular dichroism (CD) spectroscopy and five empirical prediction methods. The secondary structures determined from the resolving of CD spectra through a least-squares curve fitting procedure were compared with those predicted from four statistical methods, e.g., the Chou-Fasman, Garnier-Osguthorpe-Robson, Nishikawa-Ooi, and a JOINT scheme which combined all three of these methods, plus a pure a priori one, the Ptitsyn-Finkelstein method. Among all of the methods used, the Nishikawa-Ooi prediction gave the closest match in the composition of secondary structure to the CD result, although the other methods each correctly predicted one or more secondary structural group. Most of the alpha-helix and beta-sheet states predicted by the Ptitsyn-Finkelstein method were in accord with the Nishikawa-Ooi method. Secondary structural predictions by the Nishikawa-Ooi method were extended further to include IN proteins from four phylogenetic distinct retroviruses. The structural relationships between the four most conserved amino acid blocks of these IN proteins were compared using sequence homology and secondary structure predictions.

Amino Acid Sequence↗
PubMed1995

Evaluation of current techniques for ab initio protein structure prediction.

The results of a protein structure prediction contest are reviewed. Twelve different groups entered predictions on 14 proteins of known sequence whose structures had been determined but not yet disseminated to the scientific community. Thus, these represent true tests of the current state of structure prediction methodologies. From this work, it is clear that accurate tertiary structure prediction is not yet possible. However, protein fold and motif prediction are possible when the motif is recognizable similar to another known structure. Internal symmetry and the information inherent in an aligned family of homologous sequences facilitate predictive efforts. Novel folds remain a major challenge for prediction efforts.

Amino Acid Sequence↗
PubMed2004

Test of the Root Zone Water Quality Model (RZWQM) for predicting runoff of atrazine, alachlor and fenamiphos species from conventional-tillage corn mesoplots.

The Root Zone Water Quality Model (RZWQM) is a comprehensive, integrated physical, biological and chemical process model that simulates plant growth and movement of water, nutrients and pesticides in a representative area of an agricultural system. We tested the ability of RZWQM to predict surface runoff losses of atrazine, alachlor, fenamiphos and two fenamiphos oxidative degradates against results from a 2-year mesoplot rainfall simulation experiment. Model inputs included site-specific soil properties and weather, but default values were used for most other parameters, including pesticide properties. No attempts were made to calibrate the model except for soil crust/seal hydraulic conductivity and an adjustment of pesticide persistence in near-surface soil. Approximately 2.5 (+/- 0.9), 3.0 (+/- 0.8) and 0.3 (+/- 0.2)% of the applied alachlor, atrazine and fenamiphos were lost in surface water runoff, respectively. Runoff losses in the 'critical' events--those occurring 24 h after pesticide application--were respectively 91 (+/- 5), 86 (+/- 6) and 96 (+/- 3)% of total runoff losses for these pesticides. RZWQM adequately predicted runoff water volumes, giving a predicted/observed ratio of 1.2 (+/- 0.5) for all events. Predicted pesticide concentrations and loads from the 'critical' events were generally within a factor of 2, but atrazine losses from these events were underestimated, which was probably a formulation effect, and fenamiphos losses were overestimated due to rapid oxidation. The ratios of predicted to measured pesticide concentrations in all runoff events varied between 0.2 and 147, with an average of 7. Large over-predictions of pesticide runoff occurred in runoff events later in the season when both loads and concentrations were small. The normalized root mean square error for pesticide runoff concentration predictions varied between 42 and 122%, with an average of 84%. Pesticide runoff loads were predicted with a similar accuracy. These results indicate that the soil-water mixing model used in RZWQM is a robust predictor of pesticide entrainment and runoff.

Acetamides↗
PubMed2003

Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches.

Prediction of the future number of cancer cases is of great interest to society. The classical approach is to use the age-period-cohort model for making cancer incidence predictions. We made an empirical comparison of different versions of this model, using data from cancer registries in the Nordic countries for the period 1958-1997. We have applied 15 different methods to 20 sites for each sex in Denmark, Finland, Norway and Sweden. Median absolute value of the relative difference between observed and predicted numbers of cases for these 160 combinations of site, sex and country was calculated. The medians varied between 10.4 per cent and 15.3 per cent in predictions 10 years ahead, and between 15.1 per cent and 32.0 per cent for 20 year predictions. We have four main conclusions: (i) projecting current trends worked better than assuming that future rates are equal to present rates; (ii) the method based on the multiplicative APC model often overestimated the number of cancer cases due to its exponential growth over time, but using a power function to level off this growth improved the predictions; (iii) projecting only half of the trend after the first 10 years also gave better long-term predictions; (iv) methods that emphasize trends in the last decade seem to perform better than those that include earlier time trends.

Cohort Studies↗
PubMed2003

Predictive accuracy and explained variation.

Measures of the predictive accuracy of regression models quantify the extent to which covariates determine an individual outcome. Explained variation measures the relative gains in predictive accuracy when prediction based on covariates replaces unconditional prediction. A unified concept of predictive accuracy and explained variation based on the absolute prediction error is presented for models with continuous, binary, polytomous and survival outcomes. The measures are given both in a model-based formulation and in a formulation directly contrasting observed and expected outcomes. Various aspects of application are demonstrated by examples from three forms of regression models. It is emphasized that the likely degree of absolute or relative predictive accuracy often is low even if there are highly significant and relatively strong covariates.

Austria↗
PubMed2001

Accuracy of point predictions in survival analysis.

Survival time prediction is important in many applications, particularly for patients diagnosed with terminal diseases. A measure of prediction error taken from the medical literature is advocated as a practicable method of quantifying reliability of point predictions. Optimum predictions are derived for familiar survival models and the accuracy of these predictions is investigated. We argue that poor predictive capability is inherent to standard survival models with realistic parameter values. A lung cancer example is used to illustrate difficulties in prediction in practice.

Carcinoma, Non-Small-Cell Lung↗
PubMed1993

Predicting rodent carcinogenicity of halogenated hydrocarbons by in vivo biochemical parameters.

Forty halogenated hydrocarbons of known rodent carcinogenicity (24 carcinogens, 16 noncarcinogens), including many promoters of carcinogenesis, nongenotoxic carcinogens, and hepatocarcinogens, were selected for study. The chemicals were administered by gavage in two dose levels to female Sprague-Dawley rats. The effects of these 40 chemicals on four biochemical assays [hepatic DNA damage by alkaline elution (DD), hepatic ornithine decarboxylase activity (ODC), serum alanine aminotransferase activity (ALT), and hepatic cytochrome P-450 content (P450)] were determined. Composite predictive parameters are defined as follows: CP = [ODC and P450], CT = [ALT and ODC], and TS = [DD or CP or CT]. The operational characteristics of TS for predicting rodent cancer were sensitivity 58%, specificity 81%, positive predictivity 82%, negative predictivity 57%, and concordance 68%. The concordance for the Ames test (45%) and structural alerts (SA; 46%) was much lower. TS also outperformed the Ames test and SA in producing fewer false positives (the specificity of TS was 81% vs. only 63% for the Ames test and 57% for SA). For predicting the carcinogenicity of the most difficult halogenated hydrocarbons (Ames and SA negative chemicals), TS was capable of successfully predicting the carcinogenicity of 8 (carbon tetrachloride, chloroform, alpha-hexachlorocyclohexane, kepone, mirex, monuron, p,p'-DDE, and 2,4,6-trichlorophenol) out of 16 of these non-DNA-reactive halogenated hydrocarbon carcinogens. All 8 of these halogenated hydrocarbons were positive in either CP or CT. This evidence shows that nongenotoxic carcinogenesis is best predicted by nongenotoxic parameters such as CP or CT (components of the predictor TS).

Animals↗
PubMed2006

Sonographic findings of placental lacunae and the prediction of adherent placenta in women with placenta previa totalis and prior Cesarean section.

OBJECTIVE: To investigate the value of transvaginal sonographic findings of intraplacental lacunae for predicting adherent placenta and clinical outcome in patients with placenta previa totalis and a history of Cesarean section. METHODS: Fifty-one patients with placenta previa totalis diagnosed by transvaginal sonography and with a history of Cesarean section who delivered at our hospital were included in the study. The sonographic findings of intraplacental lacunae were classified into one of four grades. Pathological analysis of the placenta was performed for all patients who delivered, and in cases of hysterectomy, examination of the uterus was also performed. The placental findings and obstetric complications, including massive transfusion, intensive care unit admission and Cesarean hysterectomy, were compared with the grade of lacuna. RESULTS: Lacunae were classified as Grade 1+ in 10 cases, Grade 2+ in 11 cases, Grade 3+ in five cases and as Grade 0 (i.e. lacunae were absent) in the remaining 25 cases. When lacunae of > or = Grade 1+ were considered, the sensitivity, specificity, positive predictive value and negative predictive value of diagnosing adherent placenta were 86.9%, 78.6%, 76.9% and 88.0%, respectively. When lacunae of > or = Grade 2+ were considered, the sensitivity, specificity, positive predictive value and negative predictive value of diagnosing placenta increta or percreta were 100%, 97.2%, 93.8% and 100%, respectively. Hysterectomy was performed in 18 cases, among whom two cases showed Grade 1+ lacunae, 11 cases showed Grade 2+ lacunae, and five cases showed Grade 3+ lacunae. No hysterectomy was performed in any case in which lacunae were absent. Compared to those without lacunae, the number of massive transfusions and intensive care unit admissions and cases of disseminated intravascular coagulopathy and Cesarean hysterectomy were significantly greater in those with lacunae (P < 0.0001). CONCLUSION: Transvaginal sonographic findings of intraplacental lacunae in patients with placenta previa totalis and a history of Cesarean section are useful in the prediction of adherent placenta and may have a role in the prediction of clinical outcome.

Adult↗
PubMed1993

A secondary structure prediction of the hemorrhagic metalloprotease family.

A secondary structure has been predicted for the hemorrhagic metalloproteases using a method developed in Zurich that extracts structural information from patterns of conservation and variation in homologous protein sequences. This prediction tests the limits of the method when applied to a small number of homologous sequences that have undergone only modest evolutionary divergence. Predictions were also obtained using a neural network developed by Sander and coworkers, to date the best fully automated method for predicting secondary structure, and using the classical Chou-Fasman and GOR heuristics. The predictions are different. No crystal structure is known within this protein family, but one is expected shortly. Therefore, this prediction should contribute significantly to the evaluation of the relative merits of these prediction methods.

Amino Acid Sequence↗
PubMed1994

Predictions from uncertain categorizations.

Eleven experiments investigated how categorization influences feature prediction. Subjects were provided with sets of categorized exemplars, which they used to make predictions about properties of new exemplars. Because the categories were provided for subjects, this method allowed a test of categorization and prediction processes, bypassing initial concept formation and memory. The experiments tested a Bayesian rule of prediction according to which (1) predictions of an object's features are based on information from multiple categories, and (2) features are treated as independent of one another. With one exception, the studies found evidence against both of these claims. Subjects did not generally alter their predictions as a function of information outside the most likely "target" category. In addition, feature relations had reliable effects on these predictions. We discuss the implications of these results for understanding how categories are used in drawing inferences.

Concept Formation↗
PubMed1998

Performance-guarantee gene predictions via spliced alignment.

An important and still unsolved problem in gene prediction is designing an algorithm that not only predicts genes but estimates the quality of individual predictions as well. Since experimental biologists are interested mainly in the reliability of individual predictions (rather than in the average reliability of an algorithm) we attempted to develop a gene recognition algorithm that guarantees a certain quality of predictions. We demonstrate here that the similarity level with a related protein is a reliable quality estimator for the spliced alignment approach to gene recognition. We also study the average performance of the spliced alignment algorithm for different targets on a complete set of human genomic sequences with known relatives and demonstrate that the average performance of the method remains high even for very distant targets. Using plant, fungal, and prokaryotic target proteins for recognition of human genes leads to accurate predictions with 95, 93, and 91% correlation coefficient, respectively. For target proteins with similarity score above 60%, not only the average correlation coefficient is very high (97% and up) but also the quality of individual predictions is guaranteed to be at least 82%. It indicates that for this level of similarity the worst case performance of the spliced alignment algorithm is better than the average case performance of many statistical gene recognition methods.

Algorithms↗
PubMed2002

The relationship between expression of c-ras, c-erbB-2, nm23, and p53 gene products and development of trophoblastic tumor and their predictive significance for the malignant transformation of complete hydatidiform mole.

OBJECTIVE: The aims of this retrospective study by means of immunohistochemical staining were (1) to study the expression of c-ras, c-erbB-2, p53, and nm23 gene products in complete hydatidiform moles that progress to gestational trophoblastic tumor and in those that remit spontaneously after evacuation, and (2) to estimate the predictive value of the expression of these four gene products in malignant transformation of complete hydatidiform mole. METHODS: Clinical data of patients with complete hydatidiform mole were obtained by retrospective chart review. Formalin-fixed paraffin sections of 50 cases of complete mole that progressed to gestational tumor and 32 cases of complete mole that remitted spontaneously were studied immunohistochemically for c-ras, c-erbB-2, p53, and nm23 proteins. The prognostic value of the proteins for the malignant transformation of complete mole was analyzed by multiple logistic regression and stepwise logistic estimation. Sections of 30 cases of invasive mole and 19 cases of choriocarcinoma were also immunohistologically studied for expression of the proteins. RESULTS: Expression of c-erbB-2 and p53 gene products was significantly increased and expression of nm23 and c-ras products was remarkably decreased in complete hydatidiform moles that progressed into postmolar tumor compared with those that remitted spontaneously after evacuation. There was no significant difference in the expression of the four genes in invasive mole and in choriocarcinoma. A logistic estimation model for predicting malignant transformation of complete mole was established based on the expression of gene products. When the expression of four gene products was used, the predictive sensitivity of the regression model was 86.0%, and the specificity was 75.0%. The positive predictive value was 84.3%, the negative predictive value was 77.4%. Logistic stepwise regression analysis showed that the altered expression of c-erbB-2 and nm23 products had strong predictive value, while the expression of c-ras and p53 products had no significant predictive value for the malignant transformation of complete mole. CONCLUSION: The altered expression of c-ras, c-erbB-2, nm23, and p53 gene products may be important in the pathogenesis of gestational trophoblastic tumor. The decreased expression of nm23 protein and increased expression of c-erbB-2 protein are strong predictors for the malignant transformation of complete mole.

Cell Transformation, Neoplastic↗
PubMed1994

Predicting protein mutant energetics by self-consistent ensemble optimization.

In this paper we present a self-consistent ensemble optimization (SCEO) theory for efficient conformational search, which we have applied to predicting the effects of mutations on protein thermostability. This approach takes advantage of a statistical mechanical self-consistency condition to home in iteratively on the global minimum structure. We employ a fast potential of mean-force approximation to cut computation time to a few minutes for a typical protein mutation, with only linear time-dependence on the size of the prediction problem. Rather than seeking a single, static structure of minimum energy, the new method optimizes an ensemble of many conformations, seeking to predict the most likely ensemble for the native state at a desired temperature. Testing this approach with a simple physical model focusing entirely on steric interactions and side-chain rearrangement, we obtain robustly convergent prediction of core side-chain conformation, and of hydrophobic core mutations' effects on protein stability. Self-consistent ensemble optimization is superior to simulated annealing in its speed and convergence to the global minimum, and insensitive to starting conformation. In calculations on lambda repressor protein, structural predictions for an eight-residue molten-zone had side-chain r.m.s. error of 0.49 A for the wild-type protein. Evaluation of the method's mutant structure predictions should become possible, as structures of these mutant repressors are solved. Predicted energies for a series of nine hydrophobic core mutants correlated with measured free energies of unfolding with a coefficient of 0.82.

Amino Acid Sequence↗
PubMed1994

Use of amino acid environment-dependent substitution tables and conformational propensities in structure prediction from aligned sequences of homologous proteins. I. Solvent accessibility classes.

Buried and exposed residues are predicted by composing amino acid substitution patterns and mean propensities for the two solvent accessibility classes with the amino acid residues at equivalent sites in aligned sequences of homologous proteins. In a study of 13 protein families, the accuracy of the prediction is around 77% (the correlation coefficient between the predicted and observed accessibility classes is 0.52). The environment dependent amino acid substitution tables are especially important in prediction of buried hydrophilic and exposed hydrophobic residues, which are not well predicted with propensities alone. Since the prediction is site-specific in the sense that any averaged properties over neighbouring residues are not required, the results can be used for the prediction of secondary structures by detecting periodicity in the sequence of buried and exposed classes.

Amino Acid Sequence↗