MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification

We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13-14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.     

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.     

Topochemical models for the prediction of voltage-gated sodium channel binding activity of hydantoins and related non-hydantoins

The relationship of Wiener’s topochemical index—a distance based topochemical index, molecular connectivity topochemical index—an adjacency based topochemical index and eccentric connectivity topochemical index—an adjacency-cum-distance based topochemical index with sodium channel binding activity has been investigated. A dataset comprising 50 hydantoins and related non-hydantoins was selected. The dataset was divided equally into training and test sets. The values of the three topochemical indices for all the compounds present in both the training and test sets were computed using an in-house computer program. The resulting data was analyzed and suitable models were developed after identification of the active ranges in the training set. Subsequently, a biological activity was assigned to each compound involved in the training set using these models, which was then compared with the reported sodium channel binding activity. An accuracy of prediction of the order of >99% was observed using the proposed models. Cross-validation of these models using the test set revealed an exceptionally high accuracy of ∼95%. 3,5-disubstituted-5-phenylhydantoin     

A Developmental Toxicology Assay Platform for Screening Teratogenic Liability of Pharmaceutical Compounds

Increasing need for proactive safety optimization of pharmaceutical compounds has led to generation and/or refinement of in vitro developmental toxicology assays. Our laboratory has developed three in vitro developmental toxicology assays to assess teratogenic liability of pharmaceutical compounds. These assays included a mouse molecular embryonic stem cell assay (MESCA), a dechorionated zebrafish embryo culture (ZEC) assay, and a streamlined rat whole embryo culture (rWEC) assay. Individually, the assays presented good (73–82%) predictivity. However, it remains to be determined whether combining or tiering the assays could enhance performance. Seventy‐three compounds representing a broad spectrum of pharmaceutical targets and chemistry were evaluated across the assays to generate testing strategies that optimized performance. The MESCA and ZEC assays were found to have two limitations: compound solubility and frequent misclassification of compounds with H1 receptor or GABAnergic activity. The streamlined rWEC assay was found to be a cost‐effective stand‐alone assay for supporting poorly soluble compounds and/or ones with H1 or GABAnergic activity. For all other compounds, a tiering strategy using the MESCA and ZEC assays additionally optimized throughput, cost, and minimized animal use. The tiered strategy resulted in improved performance achieving 88% overall predictivity and was comparable with 89% overall predictivity achieved with frequency analysis (final teratogenic classification made from most frequent teratogenic classification from each individual assay). Furthermore there were 21 compounds in the test set characterized as definitive or suspect human teratogens and the multiassay approach achieved 95 and 91% correct classification using the tiered or frequency screening approach, respectively     

The pharmacophore hypotheses of I(Kr) potassium channel blockers: novel class III antiarrhythmic agents

Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening     

Serine-arginine (SR) protein-like factors that antagonize authentic SR proteins and regulate alternative splicing.

We have characterized two RNA-binding proteins, of apparent molecular masses of approximately 40 and 35 kDa, which possess a single N-terminal RNA-recognition motif (RRM) followed by a C-terminal domain rich in serine-arginine dipeptides. Their primary structures resemble the single-RRM serine-arginine (SR) protein, SC35; however their functional effects are quite distinctive. The 40-kDa protein cannot complement SR protein-deficient HeLa cell S100 extract and showed a dominant negative effect in vitro against the authentic SR proteins, SF2/ASF and SC35. Interestingly, the 40- and 35-kDa proteins antagonize SR proteins and activate the most distal alternative 5' splice site of adenovirus E1A pre-mRNA in vivo, an activity that is similar to that characterized previously for the heterogeneous nuclear ribonucleoprotein particles A/B group of proteins. A series of recombinant chimeric proteins consisting of domains from these proteins and SC35 in various combinations showed that the RRM, but not the C-terminal domain rich in serine-arginine dipeptides, has a dominant role in this activity. Because of the similarity to SR proteins we have named these proteins SRrp40 and SRrp35, respectively, for SR-repressor proteins of approximately 40 and approximately 35 kDa. Both factors show tissue- and cell type-specific patterns of expression. We propose that these two proteins are SR protein-like alternative splicing regulators that antagonize authentic SR proteins in the modulation of alternative 5' splice site choice.     

Modeling K(m) values using electrotopological state: substrates for cytochrome P450 3A4-mediated metabolism

In order to determine K(m) values of substrates for CYP3A4-mediated metabolism, an in silico model has been developed in the present work. Using electrotopological state (E-state) indices, together with Bayesian-regularized neural network (BRNN), we have described an in silico method to model log(1/K(m)) values of various substrates. The relative importance of the E-state indices is analyzed by principal component analysis. By using an additional external test set, which is independent of the training set, the robustness and predictivity of the model are also validated.     

3D-QSAR CoMFA/CoMSIA studies on Urokinase plasminogen activator (uPA) inhibitors: a strategic design in novel anticancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of indole/benzoimidazole-5-carboxamidines as urokinase plasminogen activator (uPA) inhibitors. The ligand molecular superimposition on template structure was performed by atom/shape-based RMS fit, multifit, and RMSD fit methods. The removal of two outliers from the initial training set of 30 molecules improved the predictivity of the models. The statistically significant model was established from 28 molecules, which were validated by evaluation of test set of nine compounds. The atom-based RMS alignment yielded best predictive CoMFA model (r2(cv) = 0.611, r2(cnv) = 0.778, F value = 43.825, r2(bs) = 0.842, r2(pred) = 0.616 with two components) while the CoMSIA model yielded (r2(cv) = 0.499, r2(cnv) = 0.976, F value=96.36, r2(bs) = 0.993, r2(pred) = 0.694 with eight components). The contour maps obtained from 3D-QSAR studies were appraised for the activity trends of the molecules analyzed. The results indicate that the steric, electrostatic, and hydrogen bond donor/acceptor substituents play significant role in uPA activity and selectivity of these compounds. The data generated from the present study can be used as putative pharmacophore in the design of novel, potent, and selective urokinase plasminogen activator inhibitors as cancer therapeutics.     

Effects of sphingomyelin/ceramide ratio on the permeability and microstructure of model stratum corneum lipid membranes

The conversion of sphingomyelin (SM) to a ceramide (Cer) by acid sphingomyelinase (aSMase) is an important event in skin barrier development. A deficiency in aSMase in diseases such as Niemann–Pick disease and atopic dermatitis coincides with impaired skin barrier recovery after disruption. We studied how an increased SM/Cer ratio influences the barrier function and microstructure of model stratum corneum (SC) lipid membranes. In the membranes composed of isolated human SC Cer (hCer)/cholesterol/free fatty acids/cholesteryl sulfate, partial or full replacement of hCer by SM increased water loss. Partial replacement of 25% and 50% of hCer by SM also increased the membrane permeability to theophylline and alternating electric current, while a higher SM content either did not alter or even decreased the membrane permeability. In contrast, in a simple membrane model with only one type of Cer (nonhydroxyacyl sphingosine, CerNS), an increased SM/Cer ratio provided a similar or better barrier against the permeation of various markers. X-ray powder diffraction revealed that the replacement of hCer by SM interferes with the formation of the long periodicity lamellar phase with a repeat distance of d = 12.7 nm. Our results suggest that SM-to-Cer processing in the human epidermis is essential for preventing excessive water loss, while the permeability barrier to exogenous compounds is less sensitive to the presence of sphingomyelin.     

Characterization and cloning of the human splicing factor 9G8: a novel 35 kDa factor of the serine/arginine protein family.

By adopting a monoclonal antibody approach, we have identified a novel splicing factor of 35 kDa which we have termed 9G8. The isolation and characterization of cDNA clones indicate that 9G8 is a novel member of the serine/arginine (SR) splicing factor family because it includes an N-terminal RNA binding domain (RBD) and a C-terminal SR domain. The RNA binding domain of 9G8 is highly homologous to those of the SRp20 and RBP1 factors (79-71% identity), but the homology is less pronounced in the cases of SF2/ASF and SC35/PR264 (45-37% identity). Compared with the other SR splicing factors, 9G8 presents some specific sequence features because it contains an RRSRSXSX consensus sequence repeated six times in the SR domain, and a CCHC motif in its median region, similar to the zinc knuckle found in the SLU7 splicing factor in yeast. Complete immunodepletion of 9G8 from a nuclear extract, which is accompanied by a substantial depletion of other SR factors, results in a loss of splicing activity. We show that a recombinant 9G8 protein, expressed using a baculovirus vector and excluding other SR factors, rescues the splicing activity of a 9G8-depleted nuclear extract and an S100 cytoplasmic fraction. This indicates that 9G8 plays a crucial role in splicing, similar to that of the other SR splicing factors. This similarity was confirmed by the fact that purified human SC35 also rescues the 9G8-depleted extract. The identification of the 9G8 factor enlarges the essential family of SR splicing factors, whose members have also been proposed to play key roles in alternative splicing.     

Regulation of the alternative splicing of tau exon 10 by SC35 and Dyrk1A

Abnormal alternative splicing of tau exon 10 results in imbalance of 3R-tau and 4R-tau expression, which is sufficient to cause neurofibrillary degeneration. Splicing factor SC35, a member of the superfamily of the serine/arginine-rich (SR) proteins, promotes tau exon 10 inclusion. The molecular mechanism by which SC35 participates in tau exon 10 splicing remains elusive. In the present study, we found that tau pre-mRNA was coprecipitated by SC35 tagged with HA. Mutation of the SC35-like exonic splicing enhancer located at exon 10 of tau affected both the binding of SC35 to tau pre-mRNA and promotion of tau exon 10 inclusion, suggesting that SC35 acts on the SC35-like exonic splicing enhancer to promote tau exon 10 inclusion. Dyrk1A (dual-specificity tyrosine-phosphorylated and regulated kinase 1A) phosphorylated SC35 in vitro and interacted with it in cultured cells. Overexpression of Dyrk1A suppressed SC35's ability to promote tau exon 10 inclusion. Downregulation of Dyrk1A promoted 4R-tau expression. Therefore, upregulation of Dyrk1A in Down syndrome brain or Alzheimer's brain may cause dysregulation of tau exon 10 splicing through SC35, and probably together with other splicing factors, leading to the imbalance in 3R-tau and 4R-tau expression, which may initiate or accelerate tau pathology and cause neurofibrillary degeneration in the diseases.     

Prediction of the aqueous solvation free energy of organic compounds by using autocorrelation of molecular electrostatic potential surface properties combined with response surface analysis

Several quantitative structure-property relationship (QSPR) approaches have been explored for the prediction of aqueous solubility or aqueous solvation free energies, DeltaG(sol), as crucial parameter affecting the pharmacokinetic profile and toxicity of chemical compounds. It is mostly accepted that aqueous solvation free energies can be expressed quantitatively in terms of properties of the molecular surface electrostatic potentials of the solutes. In the present study we have introduced autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with nonlinear response surface analysis (RSA) as alternative 3D-QSPR strategy to evaluate the aqueous solvation free energy of organic compounds. A robust QSPR model (r(cv)=0.93) has been obtained by using a collection of 248 organic chemicals. An external test set based on 23 molecules confirmed the good predictivity of the autoMEP/RSA model suggesting its further applicability in the in silico prediction of water solubility of large organic compound libraries.     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.     

155 3D QSAR and protein–protein interaction studies on neuraminidase against Clostridium perfringens: An approach toward target identification using structure-based drug designing

The rapid onset of resistance to new drugs and emergence of antibiotic resistant bacteria has led to resurgence in life-threatening bacterial infections. These problems have revitalized interest in antibiotics and lead to new research. To gain further insight between structural and biological activity of Clostridium perfringens, a gram-positive anaerobe responsible for food poisoning, mynecrosis in wound infections, and enterotoxemia in humans. We have considered various in silico approaches for developing new drug leads, based on small ligand structure. The importance of neuraminidases in the virulence of C. perfringens makes it a potent target for the studies of drug designing against this microbe. Natural products or their direct derivatives play crucial roles in many diseases. In the present study, 3D QSAR analysis using kNN-MFA method was performed on a series of pterocarpan derivatives as Clostridial neuraminidase inhibitors. Twenty-five compounds using random selection and sphere exclusion method for the division of dataset into training and test set were chosen. kNN-MFA methodology with stepwise, simulated annealing and genetic algorithm was used for model building and four predictive models have been generated. The most significant model has a high internal predictivity of 64.80% (q ²?=?0.6480) and an external predictivity of 95.46% (r ²?=?0.9546). Model showed that electrostatic and steric interactions play important role in determining neuraminidase inhibitory activity. The kNN-MFA plots provide further understanding of the relationship between structural features of substituted pterocarpan derivatives and their activities which were applied for designing new compounds as inhibitors. The drug likeliness of these compounds was checked through ADME property prediction and their interaction with the neuraminidase was checked by molecular docking studies. Moreover, analysis of protein–protein interaction network of NanI in C. perfringens was done.