Classifying enzyme inhibitors using MS.
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Enzyme Inhibitors↗
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Mechanistic characterization of the HDV genomic ribozyme: classifying the catalytic and structural metal ion sites within a multichannel reaction mechanism.
Prior studies of the metal ion dependence of the self-cleavage reaction of the HDV genomic ribozyme led to a mechanistic framework in which the ribozyme can self-cleave by multiple Mg2+ ion-independent and -dependent channels [Nakano et al. (2001) Biochemistry 40, 12022]. In particular, channel 2 involves cleavage in the presence of a structural Mg2+ ion without participation of a catalytic divalent metal ion, while channel 3 involves both structural and catalytic Mg2+ ions. In the present study, experiments were performed to probe the nature of the various divalent ion sites and any specificity for Mg2+. A series of alkaline earth metal ions was tested for the ability to catalyze self-cleavage of the ribozyme under conditions that favor either channel 2 or channel 3. Under conditions that populate primarily channel 3, nearly identical K(d)s were obtained for Mg2+, Ca2+, Ba2+, and Sr2+, with a slight discrimination against Ca2+. In contrast, under conditions that populate primarily channel 2, tighter binding was observed as ion size decreases. Moreover, [Co(NH3)6]3+ was found to be a strong competitive inhibitor of Mg2+ for channel 3 but not for channel 2. The thermal unfolding of the cleaved ribozyme was also examined, and two transitions were found. Urea-dependent studies gave m-values that allowed the lower temperature transition to be assigned to tertiary structure unfolding. The effects of high concentrations of Na+ on the melting temperature for RNA unfolding and the reaction rate revealed ion binding to the folded RNA, with significant competition of Na+ (Hill coefficient of 1.5-1.7) for a structural Mg2+ ion and an unusually high intrinsic affinity of the structural ion for the RNA. Taken together, these data support the existence of two different classes of metal ion sites on the ribozyme: a structural site that is inner sphere with a major electrostatic component and a preference for Mg2+, and a weak catalytic site that is outer sphere with little preference for a particular divalent ion.
Barium↗
The EH1 domain of Eps15 is structurally classified as a member of the S100 subclass of EF-hand-containing proteins.
The Eps15 homology (EH) domain is a protein-protein interaction module that binds to proteins containing the asparagine-proline-phenylalanine (NPF) or tryptophan/phenylalanine-tryptophan (W/FW) motif. EH domain-containing proteins serve important roles in signaling and processes connected to transport, protein sorting, and organization of subcellular structure. Here, we report the solution structure of the apo form of the EH1 domain of mouse Eps15, as determined by high-resolution multidimensional heteronuclear NMR spectroscopy. The polypeptide folds into six alpha-helices and a short antiparallel beta-sheet. Additionally, it contains a long, structured, topologically unique C-terminal loop. Helices 2-5 form two EF-hand motifs. Structural similarity and Ca(2+) binding properties lead to classification of the EH1 domain as a member of the S100 subclass of EF-hand-containing proteins, albeit with a unique set of interhelical angles. Binding studies using an eight-residue NPF-containing peptide derived from RAB, the cellular cofactor of the HIV Rev protein, show a hydrophobic peptide-binding pocket formed by conserved tryptophan and leucine residues.
Adaptor Proteins, Signal Transducing↗
Classifying 'drug-likeness' with kernel-based learning methods.
In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process.
Artificial Intelligence↗
Substructure-based support vector machine classifiers for prediction of adverse effects in diverse classes of drugs.
Unforeseen adverse effects exhibited by drugs contribute heavily to late-phase failure and even withdrawal of marketed drugs. Torsade de pointes (TdP) is one such important adverse effect, which causes cardiac arrhythmia and, in some cases, sudden death, making it crucial for potential drugs to be screened for torsadogenicity. The need to tap the power of computational approaches for the prediction of adverse effects such as TdP is increasingly becoming evident. The availability of screening data including those in organized databases greatly facilitates exploration of newer computational approaches. In this paper, we report the development of a prediction method based on a support machine vector algorithm. The method uses a combination of descriptors, encoding both the type of toxicophore as well as the position of the toxicophore in the drug molecule, thus considering both the pharmacophore and the three-dimensional shape information of the molecule. For delineating toxicophores, a novel pattern-recognition method that utilizes substructures within a molecule has been developed. The results obtained using the hybrid approach have been compared with those available in the literature for the same data set. An improvement in prediction accuracy is clearly seen, with the accuracy reaching up to 97% in predicting compounds that can cause TdP and 90% for predicting compounds that do not cause TdP. The generic nature of the method has been demonstrated with four data sets available for carcinogenicity, where prediction accuracies were significantly higher, with a best receiver operating characteristics (ROC) value of 0.81 as against a best ROC value of 0.7 reported in the literature for the same data set. Thus, the method holds promise for wide applicability in toxicity prediction.
Algorithms↗
Determining the geographic origin of potatoes with trace metal analysis using statistical and neural network classifiers.
The objective of this research was to develop a method to confirm the geographical authenticity of Idaho-labeled potatoes as Idaho-grown potatoes. Elemental analysis (K, Mg, Ca, Sr, Ba, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, S, Cd, Pb, and P) of potato samples was performed using ICPAES. Six hundred eight potato samples were collected from known geographic growing sites in the U.S. and Canada. An exhaustive computational evaluation of the 608 x 18 data sets was carried out using statistical (PCA, CDA, discriminant function analysis, and k-nearest neighbors) and neural network techniques. The neural network classification of the samples into two geographic regions (defined as Idaho and non-Idaho) using a bagging technique had the highest percentage of correct classifications, with a nearly 100% degree of accuracy. We report the development of a method combining elemental analysis and neural network classification that may be widely applied to the determination of the geographical origin of unprocessed, fresh commodities.
Analysis of Variance↗
Classifying diabetes according to the new WHO clinical stages.
AIMS/HYPOTHESIS: To test the usefulness of the new WHO criteria for clinical staging of diabetes in the characterization of 1977 diabetic patients. METHODS: The following clinical stages were used: patients on diet and/or oral antidiabetic agents 2 years after diagnosis were considered as non-insulin requiring (NIR; n = 711) and patients who required insulin therapy after 1 year as insulin requiring for control (IRC; n = 543). Patients who because of deteriorating hyperglycemia within 1 year required insulin therapy were considered as insulin requiring for survival (IRS; n = 743). RESULTS: The NIR patients had the highest age at onset (52 +/- 12 years; mean +/- SD), BMI (29.3 +/- 5.2 kg/m2) and C-peptide concentrations (median 0.98 nmol/l; interquartile range 0.72-1.31 nmol/l) but the lowest frequency of GAD antibodies (5.5%) compared to the IRC and IRS groups. The IRC group had a high age at onset (49 +/- 13 years), BMI (28.0 +/- 4.8 kg/m2), frequency of GAD antibodies (16.8%), intermediate C-peptide concentrations (0.56 nmol/l, interquartile range 0.28 +/- 0.94), and the highest prevalence of nephropathy (31.5%) and neuropathy (68.1%). The IRS group had the lowest age at onset (23 +/- 15 years), BMI (24.2 +/- 3.4 kg/m2), C-peptide concentrations (0.05 nmol/l, interquartile range below detection limit 0.01) and highest frequency of GAD antibodies (44.5%). Retinopathy was more common in IRS than in IRC patients (62.1 vs. 43.9%;p < 0.001). CONCLUSIONS: The new WHO criteria seem to discriminate three distinct subgroups and thus provide a useful tool for clinical staging. The IRC patients seem to have a more severe disease than the IRS patients, which has not been clearly acknowledged in the etiological classification. However, because of the cross-sectional nature of these data, they need to be confirmed in a prospective study with defined cut-off limits for when insulin should be initiated.
Aged↗
Degradation of volatile hydrocarbons from steam-classified solid waste by a mixture of aromatic hydrocarbon-degrading bacteria.
Steam classification is a process for treatment of solid waste that allows recovery of volatile organic compounds from the waste via steam condensate and off-gases. A mixed culture of aromatic hydrocarbon-degrading bacteria was used to degrade the contaminants in the condensate, which contained approx. 60 hydrocarbons, of which 38 were degraded within 4 d. Many of the hydrocarbons, including styrene, 1,2,4-trimethylbenzene, naphthalene, ethylbenzene, m-/p-xylene, chloroform, 1,3-dichloropropene, were completely or nearly completely degraded within one day, while trichloroethylene and 1,2,3-trichloropropane were degraded more slowly.
Biodegradation, Environmental↗
Classifying life-style types of phytoseiid mites: diagnostic traits.
Several traits are useful for identifying life-style types of predaceous phytoseiid mites when either 2 (diet generalist-specialist) or 4 (specialist I and II-generalist III and IV) type models [McMurtry J.A. and Croft B.A. 1997. Annu. Rev. Entomol. 42: 291-321] are considered. Traits useful for both models are developmental time and oviposition rates when feeding on several food types. Discriminating for the 2-types model are dorsal shield setae lengths, and intra- and inter-specific predation. Another trait useful for both models is feeding preferences of adult female phytoseiids on eggs versus larvae of Tetranychus urticae Koch. In this paper, we review established and other traits that need more study such as mouthpart types, other morphological features, spider mite webbing effects, distributions relative to prey-foods, plant-host relationships including domatia and sap feeding, density-dependent responses to prey and predator-prey ratios required for biological control. Uses of life-style data in biological control decision-making are discussed.
Animals↗
Using an Hebbian learning rule for multi-class SVM classifiers.
Regarding biological visual classification, recent series of experiments have enlighten the fact that data classification can be realized in the human visual cortex with latencies of about 100-150 ms, which, considering the visual pathways latencies, is only compatible with a very specific processing architecture, described by models from Thorpe et al. Surprisingly enough, this experimental evidence is in coherence with algorithms derived from the statistical learning theory. More precisely, there is a double link: on one hand, the so-called Vapnik theory offers tools to evaluate and analyze the biological model performances and on the other hand, this model is an interesting front-end for algorithms derived from the Vapnik theory. The present contribution develops this idea, introducing a model derived from the statistical learning theory and using the biological model of Thorpe et al. We experiment its performances using a restrained sign language recognition experiment. This paper intends to be read by biologist as well as statistician, as a consequence basic material in both fields have been reviewed.
Algorithms↗
A new method for classifying patterns of prenatal care utilization using cluster analysis.
OBJECTIVES: The objectives of this study were: to 1) define patterns of prenatal care utilization using cluster analysis, 2) describe two alternative cluster solutions and compare these groupings to the Adequacy of Prenatal Care Utilization Index (APNCU), 3) compare the cluster solutions and the APNCU with respect to maternal age and prematurity, and 4) discuss advantages and disadvantages of using cluster analysis to study prenatal care. METHODS: The study sample included 3544 women in the 1988 National Maternal and Infant Health Survey for whom complete prenatal care visit data were available. Clustering was carried out in two stages, first employing nearest centroid sorting (the k means method), a nonhierarchical approach, and then using Ward's Minimum Variance Method, a hierarchical clustering technique. RESULTS: Patterns of prenatal care defined by cluster analysis varied by timing of the first visit, total number of visits, and the rate of accumulation of visits, but this variation was different compared to that seen for the APNCU. While the cluster solutions and the APNCU identified a similar normative pattern of care, other patterns identified were quite different. In particular, the six-cluster solution differentiated among women who entered care at similar times, but accumulated visits at differing rates and experienced differing rates of preterm delivery. CONCLUSION: Cluster analysis is a new tool for studying prenatal care. Further studies are needed to refine the method and test whether the alternative perspective it provides will lead to new findings concerning the relationship of prenatal care and birth outcomes.
Birth Certificates↗
Classifying periodontitis among adolescents: implications for epidemiological research.
OBJECTIVES: To evaluate the performance of four clinical classification systems proposed for periodontitis in young subjects when applied to epidemiological data on clinical attachment loss. We assess the extent to which the use of different case definition systems may influence the outcome of descriptive and analytical epidemiological studies. METHODS: The data originate in a screening examination for periodontitis carried out among 9162 high school students. Each of four previously published classification systems was applied to the data. The prevalence of cases according to each system was estimated and the association between case status, as defined by each system, and a set of candidate determinant variables was assessed using multivariable logistic regression analyses. RESULTS: The four classification systems yielded rather different prevalence estimates. For localized periodontitis the estimates varied by a factor of 10, and for generalized periodontitis, these varied by a factor of 30. The results of the logistic regression analyses using the different case-definitions essentially confirmed the results of a population-based analysis. However, the precision of the estimates decreased with decreasing numbers of cases identified by the classification systems. CONCLUSIONS: From an epidemiological point of view there is little justification for the use of the complicated classification systems. An approach based on the simple definition of a case as a person with clinical attachment loss, e.g. >/=3 mm, is preferable.
Adolescent↗
Defining and classifying periodontitis: need for a paradigm shift?
The past two decades have witnessed a large number of proposals for the classification of periodontitis. These proposals are all founded in an essentialistic disease concept, according to which periodontitis is a link between the causes and the signs and symptoms of periodontitis. Essentialistic definitions are necessarily rather imprecise and thereby subject to multiple interpretations. Consequently, it remains unknown to what extent current knowledge regarding 'different' forms of periodontitis is based on the 'same' type of patients. However, periodontitis is a syndrome, the clinical manifestations of which may come in all sizes. Thereby, periodontitis has no diagnostic truth, just as there is no natural basis for a sharp distinction between health and disease or between 'different' forms of periodontitis. Recognition of these facts and adoption of a nominalistic approach to the definition of periodontitis is needed to provide a rational framework for the development of a classification system that meets the needs of both clinicians and scientists.
Acute Disease↗
Risk factors in periodontitis and classifying the disease.
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Decision Making↗
Classifying periodontal diseases--a long-standing dilemma.
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Adolescent↗
Classifying college students as depressed or nondepressed using the Beck Depression Inventory: an empirical analysis.
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Adult↗
Classifying intergral stimuli.
Two reported experiments support holistic, as opposed to analytic, processing models for integral stimuli. Speeded classification data from different information-processing tasks (univariate and correlated) were predicted by distance between stimuli in similarity space but not by redundancy. The results of the filtering and condensation tasks and the notion of configural stimuli are also explicable in these terms. It is shown that some operational definitions commonly used to define integral stimuli are usually confounded with stimulus similarity. The assumption of independence between the attributes that combine to form multidimensional stimuli is not always met and is always an empirical question. When these attributes are not independent, physical and psychological spaces are not necessarily the same. Similarity structure is a crucial concern if inferences of cognitive processing are to be based on information-processing task results.
Association↗
Can questionnaire reports correctly classify relationship distress and partner physical abuse?
Relationship adjustment (e.g., Dyadic Adjustment Scale; DAS) and physical aggression (e.g., Conflict Tactics Scale) measures are used both as screening tools and as the sole criterion for classification. This study created face valid diagnostic interviews for relationship distress and physical abuse, through which one could compare preliminarily the classification properties of questionnaire reports. The DAS (and a global measure of relationship satisfaction) had modest agreement with a structured diagnostic interview; both questionnaires tended to overdiagnose distress compared with the interview. Results for partner abuse reiterated the need to go beyond occurrence of aggression as the sole diagnostic criterion, because men's aggression was more likely than women's to rise to the level of "abuse" when diagnostic criteria (injury or substantial fear) were applied.
Adult↗