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At least 1,423 records · Page 79Linked to original sources
PubMed2004

Topological models for prediction of anti-inflammatory activity of N-arylanthranilic acids.

Relationship of anti-inflammatory activity of N-arylanthranilic acids with distance based Wiener's index, adjacency based Zagreb indices M1 and M2, and distance-cum-adjacency based eccentric connectivity index (ECI) was investigated. A dataset comprising of 112 N-arylanthranilic acids was selected. The values of all the four indices for each of the 112 compounds were calculated using an in-house computer program. The dataset was divided randomly into training and test sets. The data was analyzed and suitable models were developed after identification the active ranges in the training set. Subsequently, a biological activity was assigned to each of the compound involved in the test set using these models, which was then compared with the reported anti-inflammatory activity. High accuracy of prediction ranging from 83% to 90% was observed using models based upon topological indices.

Anti-Inflammatory Agents↗
PubMed2005

Topological models for prediction of anti-HIV activity of acylthiocarbamates.

Relationship of anti-HIV activity of acylthiocarbamates with distance based Wiener's index, adjacency based first-order molecular connectivity index and distance-cum-adjacency based augmented eccentric connectivity index was investigated. The values of all the three indices for each of the 61 compounds involved in the dataset were calculated using an in-house computer program. Resulting data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, biological activity was assigned to each of the compounds involved in the dataset using these models which was then compared with the reported anti-HIV activity. Very high accuracy of prediction ranging from 95% to 98% was observed using these topological models.

Anti-HIV Agents↗
PubMed2006

Prediction of hERG potassium channel affinity by the CODESSA approach.

The problem of predicting torsadogenic cardiotoxicity of drugs is afforded in this work. QSAR studies on a series of molecules, acting as hERG K+ channel blockers, were carried out for this purpose by using the CODESSA program. Molecules belonging to the analyzed dataset are characterized by different therapeutic targets and by high molecular diversity. The predictive power of the obtained models was estimated by means of rigorous validation criteria implying the use of highly diagnostic statistical parameters on the test set, other than the training set. Validation results obtained for a blind set, disjoined from the whole dataset initially considered, confirmed the predictive potency of the models proposed here, so suggesting that they are worth to be considered as a valuable tool for practical applications in predicting the blockade of hERG K+ channels.

Cell Line↗
PubMed2006

QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.

Back-propagation artificial neural networks (ANNs) were trained on a dataset of 42 molecules with quantitative IC50 values to model structure-activity relationships of mono- and bis-quaternary ammonium salts as antagonists at neuronal nicotinic acetylcholine receptors (nAChR) mediating nicotine-evoked dopamine release. The ANN QSAR models produced a reasonable level of correlation between experimental and calculated log(1/IC50) (r2=0.76, r(cv)2=0.64). An external test for the models was performed on a dataset of 18 molecules with IC50 values >1 microM. Fourteen of these were correctly classified. Classification ability of various models, including self-organizing maps (SOM), for all 60 molecules was also evaluated. A detailed analysis of the modeling results revealed the following relative contributions of the used descriptors to the trained ANN QSAR model: approximately 44.0% from the length of the N-alkyl chain attached to the quaternary ammonium head group, approximately 20.0% from Moriguchi octanol-water partition coefficient of the molecule, approximately 13.0% from molecular surface area, approximately 12.6% from the first component shape directional WHIM index/unweighted, approximately 7.8% from Ghose-Crippen molar refractivity, and 2.6% from the lowest unoccupied molecular orbital energy. The ANN QSAR models were also evaluated using a set of 13 newly synthesized compounds (11 biologically active antagonists and two biologically inactive compounds) whose structures had not been previously utilized in the training set. Twelve among 13 compounds were predicted to be active which further supports the robustness of the trained models. Other insights from modeling include a structural modification in the bis-quinolinium series that involved replacing the 5 and/or 8 as well as the 5' and/or 8' carbon atoms with nitrogen atoms, predicting inactive compounds. Such data can be effectively used to reduce synthetic and in vitro screening activities by eliminating compounds of predicted low activity from the pool of candidate molecules for synthesis. The application of the ANN QSAR model has led to the successful discovery of six new compounds in this study with experimental IC50 values of less than 0.1 microM at nAChR subtypes responsible for mediating nicotine-evoked dopamine release, demonstrating that the ANN QSAR model is a valuable aid to drug discovery.

Dopamine↗
PubMed2006

Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems.

Quantitative structure-activity relationship (QSAR) models are useful in understanding how chemical structure relates to the biological activity of natural and synthetic chemicals and for design of newer and better therapeutics. In the present study, 46 flavonoids and related polyphenols were evaluated for direct/indirect antioxidant activity in three different assay systems of increasing complexity (chemical, enzymatic, and intact phagocytes). Based on these data, two different QSAR models were developed using (i) physicochemical and structural (PC&S) descriptors to generate multiparameter partial least squares (PLS) regression equations derived from optimized molecular structures of the tested compounds and (ii) a partial 3D comparison of the 46 compounds with local fingerprints obtained from fragments of the molecules by the frontal polygon (FP) method. We obtained much higher QSAR correlation coefficients (r) for flavonoid end-point antioxidant activity in all three assay systems using the FP method (0.966, 0.948, and 0.965 for datasets evaluated in the biochemical, enzymatic, and whole cell assay systems, respectively). Furthermore, high leave-one-out cross-validation coefficients (q2) of 0.907, 0.821, and 0.897 for these datasets, respectively, indicated enhanced predictive ability and robustness of the model. Using the FP method, structural fragments (submolecules) responsible for the end-point antioxidant activity in the three assay systems were also identified. To our knowledge, this is the first QSAR model derived for description of flavonoid direct/indirect antioxidant effects in a cellular system, and this model could form the basis for further drug development of flavonoid-like antioxidant compounds with therapeutic potential.

Animals↗
PubMed2005

Topological model for the prediction of MRP1 inhibitory activity of pyrrolopyrimidines and templates derived from pyrrolopyrimidine.

Relationship between Wiener's index--a distance-based topological descriptor and multidrug-resistance-associated protein (MRP1) inhibitory activity of pyrrolopyrimidines and their derivatives has been investigated. A dataset comprising of 82 analogues of pyrrolopyrimidine was selected for the present study. The values of Wiener's index were computed for each of the 82 analogues using an in-house computer program. Resultant data were analyzed and a suitable model was developed after identification of the active range. Subsequently, a biological activity was assigned to each analogue involved in the dataset using this model, which was then compared with the reported MRP1 inhibitory activity. Accuracy of prediction was found to be 88% using model based on Wiener's index.

Molecular Structure↗
PubMed2003

Screening for asymptomatic celiac disease among patients referred for bone densitometry measurement.

Celiac disease (CD) is a relatively common gastrointestinal disorder that can be asymptomatic. An increased prevalence of subclinical CD has been reported in many populations. Even among asymptomatic patients a reduction in bone mineral density (BMD) has been observed. The aim of this study was to evaluate the prevalence of silent CD in a cohort of consecutive individuals referred for bone densitometry measurement. Serum samples were taken from 454 women attending for bone densitometry (mean age: 56 +/- 11 years). Of the individuals evaluated, 89 had normal BMD and 365 had low BMD (T score < -1.0). Subjects were screened for the presence of serum IgA anti-endomysial antibodies (EMA) and IgA tissue transglutaminase (tTG) antibodies by indirect immunofluorescence and enzyme-linked immunosorbent assay (ELISA), respectively. BMD was measured by dual X-ray absorptiometry (DEXA) at the lumbar spine and femoral neck. Eight EMA tTG-positive individuals were identified in this population (1.8% or 1:57). Serologically positive women had a lower mean Z score at both the lumbar spine and femoral neck than EMA tTG-negative women. But this did not approach significance. There was no significant difference in the incidence of CD between the normal- and low-BMD groups in this dataset (P = 0.365). In conclusion, our study indicates that the prevalence of CD in our dataset is high. However, the frequency of asymptomatic CD among low-BMD individuals is similar to that among normal-BMD individuals in our population. These observations do not support the hypothesis that serological testing for CD may be a good accompaniment to DEXA scanning.

Absorptiometry, Photon↗
PubMed2005

Effective factors in thermostability of thermophilic proteins.

Thermostability of proteins in general and especially thermophilic proteins has been subject of a wide variety of studies based on theoretical and experimental investigation. Thermostability seems to be a property obtained through many minor structural modifications rather than certain amino acids substitution. In comparison with its mesophile homologue in a thermostable protein, usually a number of amino acids are exchanged. A wide variety of theoretical studies are based on comparative investigation of thermophilic proteins characteristics with their mesophilic counterparts in order to reveal their sequences, structural differences and consequently, to relate these observed differences to the thermostability properties. In this work we have compared a dataset of thermophilic proteins with their mesophilic homologues and furthermore, a mesophilic proteins dataset was also compared with its mesophilic homologue. This strategy enabled us first, to eliminate noise or background differences from signals and moreover, the important factors which were related to the thermostability were recognized too. Our results reveal that thermophilic and mesophilic proteins have both similar polar and nonpolar contribution to the surface area and compactness. On the other hand, salt bridges and main chain hydrogen bonds show an increase in the majority of thermophilic proteins in comparison to their mesophilic homologues. In addition, in thermophilic proteins hydrophobic residues are significantly more frequent, while polar residues are less. These findings indicate that thermostable proteins through evolution adopt several different strategies to withstand high temperature environments.

Adaptation, Physiological↗
PubMed2006

Prediction of protein mutant stability using classification and regression tool.

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and the solving of which would help for designing stable mutants. In this work, we have analyzed the stability of protein mutants using two different datasets of 1396 and 2204 mutants obtained from ProTherm database, respectively for free energy change due to thermal (DeltaDeltaG) and denaturant denaturations (DeltaDeltaG(H(2)O)). We have used a set of 48 physical, chemical energetic and conformational properties of amino acid residues and computed the difference of amino acid properties for each mutant in both sets of data. These differences in amino acid properties have been related to protein stability (DeltaDeltaG and DeltaDeltaG(H(2)O)) and are used to train with classification and regression tool for predicting the stability of protein mutants. Further, we have tested the method with 4 fold, 5 fold and 10 fold cross validation procedures. We found that the physical properties, shape and flexibility are important determinants of protein stability. The classification of mutants based on secondary structure (helix, strand, turn and coil) and solvent accessibility (buried, partially buried, partially exposed and exposed) distinguished the stabilizing/destabilizing mutants at an average accuracy of 81% and 80%, respectively for DeltaDeltaG and DeltaDeltaG(H(2)O). The correlation between the experimental and predicted stability change is 0.61 for DeltaDeltaG and 0.44 for DeltaDeltaG(H(2)O). Further, the free energy change due to the replacement of amino acid residue has been predicted within an average error of 1.08 kcal/mol and 1.37 kcal/mol for thermal and chemical denaturation, respectively. The relative importance of secondary structure and solvent accessibility, and the influence of the dataset on prediction of protein mutant stability have been discussed.

Amino Acid Substitution↗
PubMed2025

From sporulation to village differentiation: The shaping of the social microbiome over rural-to-urban lifestyle transition in Indonesia.

Despite established roles in human health and profound global diversity, microbiome datasets remain biased toward Western urban cohorts, with especial under-representation of Southeast Asia. Here, we present a gut microbiome dataset from 116 Indonesians spanning transitional hunter-gatherer, rural agricultural, and urban lifestyles. We identify 1,304 species and 3,258 subspecies by assembling 11,070 metagenome-assembled genomes, revealing substantial species- (15%) and subspecies- (50%) level novelty. Novel taxa are rare, often village specific, and depleted for sporulation genes, revealing a link between bacterial physiology, transmission, prevalence, and discovery. We identify rural-to-urban clines across multiple levels of biological organization, from species abundance to microbiome composition and diversity. Furthermore, between-community, but not within-community, diet variation is strongly predictive of microbiome composition, suggesting that microbiome divergence is driven by community-level differences. Our work highlights the interplay of host lifestyle, population structure, and bacterial physiology in shaping microbiome diversity and biogeography, at the key scale of human communities.

Humans↗
PubMed2004

Mu rhythm-based cursor control: an offline analysis.

OBJECTIVE: To classify the EEG data recorded in mu rhythm-based cursor control experiments with 4 possible choices. METHODS: The algorithm included preprocessing, feature extraction, and classification. Two spatial filters, common average reference and common spatial subspace decomposition, were used in preprocessing to improve the signal-to-noise ratio, and then two features were extracted based on the power spectrum and the time course of the mu rhythm respectively. A Fisher ratio was defined to select channels in feature extraction. A 2-dimensional linear classifier was trained for final classification. RESULTS: Two types of classifiers were trained for the training dataset. The uniform classifier gave a classification accuracy of 76.4%, and the classifier trained by leave-one-out method gave a classification accuracy of 74.4%, both higher than the online accuracy 69.5%. The uniform classifier was applied to the test dataset and the classification accuracy was 65.9%, lower than the online accuracy 73.2%. CONCLUSIONS: Spatial filtering can give a notable improvement in classification accuracy. The time course of the mu rhythm, as well as the power of the mu rhythm, shows difference between the 4 targets, and can contribute to the classification. SIGNIFICANCE: The spatial filtering, feature extraction and channel selection methods in the algorithm will provide some practical suggestions for further study on the mu rhythm-based brain-computer interface.

Algorithms↗
PubMed2004

Analysis of gene expression data using functional principal components.

The large amount of data involved in DNA microarrays implies the development of efficient computer algorithms to analyze the gene expressions, and thus to study the transcriptome. Numerous techniques already exist and we propose a new method based on the key idea that gene profiles may be considered as continuous curves. The analysis of the set of curves stemming from the DNA microarray may be then performed using a functional analysis which can exhibit the main modes of variations in this set, gather genes with similar variations and extract characteristic parameters of gene profiles. We aim here at introducing this method, called the Functional Principal Component Analysis. A prospective study has been performed on two available datasets, concerning on the one hand the sporulation data of the Saccharomyces cerevisiae, and on the other hand data of tumor cell lines. Results are very promising: the method is able to extract characteristic parameters from the datasets, to extract significant modes of variations in the set of gene profiles, and to link these variations to biological processes already studied in literature.

Algorithms↗
PubMed2005

Mapping the dimensionality, density and topology of data: the growing adaptive neural gas.

Self-organized maps are commonly applied for tasks of cluster analysis, vector quantization or interpolation. The artificial neural network model introduced in this paper is a hybrid model of the growing neural gas model introduced by Fritzke (Fritzke, in Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995) and the adaptive resolution clustering modification for self-organized maps proposed by Firenze (Firenze et al., in International Conference on Artificial Neural Networks, Springer-Verlag, London, 1994). The hybrid model is capable of mapping the distribution, dimensionality and topology of the input data. It has a local performance measure that enables the network to terminate growing in areas of the input space that is mapped by units reaching a performance goal. Therefore the network can accurately map clusters of data appearing on different scales of density. The capabilities of the algorithm are tested using simulated datasets with similar spatial spread but different local density distributions, and a simulated multivariate MR dataset of an anatomical human brain phantom with mild multiple sclerosis lesions. These tests demonstrate the advantages of the model compared to the growing neural gas algorithm when adaptive mapping of areas with low sample density is desirable.

Algorithms↗
PubMed2006

Robust regression for high throughput drug screening.

Effective analysis of high throughput screening (HTS) data requires automation of dose-response curve fitting for large numbers of datasets. Datasets with outliers are not handled well by standard non-linear least squares methods, and manual outlier removal after visual inspection is tedious and potentially biased. We propose robust non-linear regression via M-estimation as a statistical technique for automated implementation. The approach of finding M-estimates by Iteratively Reweighted Least Squares (IRLS) and the resulting optimization problem are described. Initial parameter estimates for iterative methods are important, so self-starting methods for our model are presented. We outline the software implementation, done in Matlab and deployed as an Excel application via the Matlab Excel Builder Toolkit. Results of M-estimation are compared with least squares estimates before and after manual editing.

Algorithms↗
PubMed2006

The multimod application framework: a rapid application development tool for computer aided medicine.

This paper describes a new application framework (OpenMAF) for rapid development of multimodal applications in computer-aided medicine. MAF applications are multimodal in data, in representation, and in interaction. The framework supports almost any type of biomedical data, including DICOM datasets, motion-capture recordings, or data from computer simulations (e.g. finite element modeling). The interactive visualization approach (multimodal display) helps the user interpret complex datasets, providing multiple representations of the same data. In addition, the framework allows multimodal interaction by supporting the simultaneous use of different input-output devices like 3D trackers, stereoscopic displays, haptics hardware and speech recognition/synthesis systems. The Framework has been designed to run smoothly even on limited power computers, but it can take advantage of all hardware capabilities. The Framework is based on a collection of portable libraries and it can be compiled on any platform that supports OpenGL, including Windows, MacOS X and any flavor of Unix/linux.

Humans↗
PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗
PubMed2004

ConPred_elite: a highly reliable approach to transmembrane topology predication.

The function of transmembrane (TM) proteins is closely correlated to their TM topology; large quantities of highly reliable TM topology data are becoming increasingly required. We present a new consensus approach for TM topology prediction (ConPred_elite) that can predict the whole topology with accuracies of 0.98 for prokaryotic and 0.95 for eukaryotic proteins on a dataset of experimentally-characterized TM topologies. The predicted yield on the dataset is 30.4% for prokaryotic and 21.5% for eukaryotic proteins. Applying ConPred_elite to predicted TM proteins extracted from 29 prokaryotic and 10 eukaryotic proteomes, we obtained 3871 and 7271 highly reliable TM topologies (yields, 19.8 and 13.3%), respectively. The predicted TM topology data may contribute to further research into a comprehensive functional classification and identification of TM proteins based on information of the topology.

Archaeal Proteins↗
PubMed2005

Prediction by support vector machines and analysis by Z-score of poly-L-proline type II conformation based on local sequence.

In recent years, the poly-L-proline type II (PPII) conformation has gained more and more importance. This structure plays vital roles in many biological processes. But few studies have been made to predict PPII secondary structures computationally. The support vector machine (SVM) represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In this paper, we present a SVM prediction method of PPII conformation based on local sequence. The overall accuracy for both the independent testing set and estimate of jackknife testing reached approximately 70%. Matthew's correlation coefficient (MCC) could reach 0.4. By comparing the results of training and testing datasets with different sequence identities, we suggest that the performance of this method correlates with the sequence identity of dataset. The parameter of SVM kernel function was an important factor to the performance of this method. The propensities of residues located at different positions were also analyzed. By computing Z-scores, we found that P and G were the two most important residues to PPII structure conformation.

Computational Biology↗