Biomedical subjects

Aarti Garg

Publications and source records attributed to Aarti Garg.

4 recordsLinked to original sources

PubMed2006

Evidence for centrophenoxine as a protective drug in aluminium induced behavioral and biochemical alteration in rat brain.

Potential use of various nootropic drugs have been a burning area of research on account of various physical and chemical insult in brain under different toxicological conditions. One of the nootropic drug centrophenoxine, also known as an anti-aging drug has been exploited in the present experiment under aluminium toxic conditions. Aluminium was administered by oral gavage at a dose level of 100 mg/Kg x b x wt/day for a period of six weeks. To elucidate the region specific response, study was carried out in two different regions of brain namely cerebrum and cerebellum. Following aluminium exposure, a significant decrease in the activities of enzymes namely Hexokinase, Lactate dehydrogenase, Succinate dehydrogenase, Mg(2+) dependent ATPase was observed in both the regions. Moreover, the activity of acetylcholinesterase was also reported to be significantly decreased. Post-treatment with centrophenoxine was able to restore the altered enzyme activities and the effect was observed in both the regions of brain although the activity of lactate dehydrogenase and acetylcholinesterase did not register significant increase in the cerebellum region. Further, centrophenoxine was able to improve the altered short-term memory and cognitive performance resulted from aluminium exposure. From the present study, it can be concluded that centrophenoxine has a potential and can be exploited in other toxicological conditions also.

Acetylcholinesterase↗

PubMed2005

Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.

The present study is an attempt to develop a neural network-based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed-forward networks with a single hidden layer have been trained with standard back-propagation as a learning algorithm. The Pearson's correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple-sequence alignment obtained from PSI-BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence-to-structure network is trained with the multiple alignment profiles in the form of PSI-BLAST-generated position-specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED-predicted secondary structure information is used as an input to the second structure-to-structure network. Based on the present study, a server SARpred (http://www.imtech.res.in/raghava/sarpred/) has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values.

Amino Acids↗

PubMed2005

PSLpred: prediction of subcellular localization of bacterial proteins.

SUMMARY: We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict approximately 74% of sequences with an average prediction accuracy of 98% at RI = 5. AVAILABILITY: PSLpred is available at http://www.imtech.res.in/raghava/pslpred/

Algorithms↗

PubMed2005

Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search.

Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.

Algorithms↗