open: No conditions. Results ordered Creators, Title. 2024-03-29T14:18:44ZEPrintshttp://crdd.osdd.net/images/sitelogo.gifhttp://crdd.osdd.net/open/2012-01-05T15:15:55Z2012-03-21T09:34:15Zhttp://crdd.osdd.net/open/id/eprint/279This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/2792012-01-05T15:15:55ZFunctional evolution of two subtly different (similar) folds.The function of Oligonucleotide/oligosaccharide-binding (OB) fold proteins was restricted to either DNA/RNA binding or sugar binding whereas the Src homology 3 (SH3) domain like proteins bind to a variety of ligands through loop modulations. A question was raised whether the evolution of these two folds was through DNA shuffling.Vishal AgrawalR K Kishan2011-12-08T19:35:09Z2011-12-08T19:35:09Zhttp://crdd.osdd.net/open/id/eprint/562This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5622011-12-08T19:35:09ZPrediction of guide strand of microRNAs from its sequence and secondary structure.In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study (http://crdd.osdd.net:8081/RISCbinder/).Firoz AhmedHifzur Rahman AnsariG.P.S. Raghava2011-12-08T19:33:10Z2011-12-08T19:33:10Zhttp://crdd.osdd.net/open/id/eprint/547This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5472011-12-08T19:33:10ZPrediction of polyadenylation signals in human DNA sequences using nucleotide frequencies.The polyadenylation signal plays a key role in determining the site for addition of a polyadenylated tail to nascent mRNA and its mutation(s) are reported in many diseases. Thus, identifying poly(A) sites is important for understanding the regulation and stability of mRNA. In this study, Support Vector Machine (SVM) models have been developed for predicting poly(A) signals in a DNA sequence using 100 nucleotides, each upstream and downstream of this signal. Here, we introduced a novel split nucleotide frequency technique, and the models thus developed achieved maximum Matthews correlation coefficients (MCC) of 0.58, 0.69, 0.70 and 0.69 using mononucleotide, dinucleotide, trinucleotide, and tetranucleotide frequencies, respectively. Finally, a hybrid model developed using a combination of dinucleotide, 2nd order dinucleotide and tetranucleotide frequencies, achieved a maximum MCC of 0.72. Moreover, for independent datasets this model achieved a precision ranging from 75.8-95.7% with a sensitivity of 57%, which is better than any other known methods.Firoz AhmedManish KumarG.P.S. Raghava2011-12-09T14:13:58Z2011-12-09T14:13:58Zhttp://crdd.osdd.net/open/id/eprint/452This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/4522011-12-09T14:13:58ZDesigning of highly effective complementary and mismatch siRNAs for silencing a gene.In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene.Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm (http://www.imtech.res.in/raghava/desirm/) for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein.Firoz AhmedG.P.S. Raghava2011-11-30T09:25:54Z2011-11-30T09:26:21Zhttp://crdd.osdd.net/open/id/eprint/616This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6162011-11-30T09:25:54ZRT-PCR method for selective detection of silent gene transcripts in silencing mutants in homothallic strains of Schizosaccharomyces pombe.Here we describe a method that allows selective detection of silent copy transcripts in homothallic strains of Schizosaccharomyces pombe in the presence of the active cassettes. The method involving RT-PCR (reverse transcriptase polymerase chain reaction) exploits our observation that the silent copy transcripts extend beyond the regions of homology to the flanking sequences specific for the donor cassettes, thus allowing design of oligos that are specific for the different donors. The results are validated using a known silencing mutant swi6.Shakil AhmedJagmohan Singh2011-11-30T09:59:57Z2011-11-30T11:25:15Zhttp://crdd.osdd.net/open/id/eprint/612This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6122011-11-30T09:59:57ZFolding behavior of a backbone-reversed protein: reversible polyproline type II to beta-sheet thermal transitions in retro-GroES multimers with GroES-like features.The structural consequences of the reversal of polypeptide backbone direction (retro modification) remain insufficiently explored. Here, we describe the behavior of an engineered, backbone-reversed form of the 97 residues-long GroES co-chaperonin of Escherichia coli. FTIR and far-UV CD spectroscopy suggest that retro-GroES adopts a mixed polyproline type II (PPII)-beta-strand structure with a beta(II) type CD spectrum similar to that of GroES. Gel-filtration chromatography reveals that the protein adopts trimeric and/or pentameric quaternary structures, with solubility retained up to concentrations of 5.0-5.5 mg/ml in aqueous solutions. Mutations inserting a single tryptophan residue as a spectroscopic probe at three different sites cause no perturbation in the protein's CD spectral characteristics, or in its quaternary structural status. The protein is cooperatively dissociated, and non-cooperatively unfolded, by both guanidine hydrochloride and urea. Intriguingly, unlike with GroES, retro-GroES is not unfolded by heat. Instead, there is a reversible structural transition involving conversion of PPII structure to beta sheet structure, upon heating, with no attendant aggregation even at 90 degrees C. Retro-GroES does not bind GroEL. In summary, some structure-forming characteristics of GroES appear to be conserved through the backbone reversal process, although the differential conformational behavior upon heating also indicates differences.Shubbir AhmedAnshuman ShuklaPurnananda Guptasarma2011-12-08T19:32:41Z2011-12-08T19:32:41Zhttp://crdd.osdd.net/open/id/eprint/543This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5432011-12-08T19:32:41ZAntigenDB: an immunoinformatics database of pathogen antigens.The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.Hifzur Rahman AnsariDarren R FlowerG.P.S. Raghava2011-12-08T19:30:25Z2012-03-22T09:45:11Zhttp://crdd.osdd.net/open/id/eprint/525This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5252011-12-08T19:30:25ZIdentification of NAD interacting residues in proteins.For the first time a sequence-based method has been developed for the prediction of NAD binding proteins and their interacting residues, in the absence of any prior structural information. The present model will aid in the understanding of NAD+ dependent mechanisms of action in the cell. To provide service to the scientific community, we have developed a user-friendly web server, which is available from URL http://www.imtech.res.in/raghava/nadbinder/.Hifzur Rahman AnsariG.P.S. Raghava2011-12-08T19:39:51Z2011-12-08T19:39:51Zhttp://crdd.osdd.net/open/id/eprint/596This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5962011-12-08T19:39:51ZTAPPred prediction of TAP-binding peptides in antigens.The transporter associated with antigen processing (TAP) plays a crucial role in the transport of the peptide fragments of the proteolysed antigenic or self-altered proteins to the endoplasmic reticulum where the association between these peptides and the major histocompatibility complex (MHC) class I molecules takes place. Therefore, prediction of TAP-binding peptides is highly helpful in identifying the MHC class I-restricted T-cell epitopes and hence in the subunit vaccine designing. In this chapter, we describe a support vector machine (SVM)-based method TAPPred that allows users to predict TAP-binding affinity of peptides over web. The server allows user to predict TAP binders using a simple SVM model or cascade SVM model. The server also allows user to customize the display/output. It is freely available for academicians and noncommercial organization at the address http://www.imtech.res.in/raghava/tappred.Manoj BhasinSneh LataG.P.S. Raghava2012-01-10T08:05:56Z2012-01-10T08:05:56Zhttp://crdd.osdd.net/open/id/eprint/54This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/542012-01-10T08:05:56ZA hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes.In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was developed for 47 MHC class I alleles having at least 15 binders. A secondary artificial neural network (ANN)-based method was developed for 30 out of 47 MHC alleles having a minimum of 40 binders. Combination of these ANN-and QM-based prediction methods for 30 alleles improved the accuracy of prediction by 6% compared to each individual method. Average accuracy of hybrid method for 30 MHC alleles is 92.8%. This method also allows prediction of binders for 20 additional alleles using QM that has been reported in the literature, thus allowing prediction for 67 MHC class I alleles. The performance of the method was evaluated using jack-knife validation test. The performance of the methods was also evaluated on blind or independent data. Comparison of our method with existing MHC binder prediction methods for alleles studied by both methods shows that our method is superior to other existing methods. This method also identifies proteasomal cleavage sites in antigen sequences by implementing the matrices described earlier. Thus, the method that we discover allows the identification of MHC class I binders (peptides binding with many MHC alleles) having proteasomal cleavage site at C-terminus. The user-friendly result display format (HTML-II) can assist in locating the promiscuous MHC binding regions from antigen sequence. The method is available on the web at www.imtech.res.in/raghava/nhlapred and its mirror site is available at http://bioinformatics.uams.edu/mirror/nhlapred/.Manoj BhasinG.P.S. Raghava2011-12-08T19:32:26Z2011-12-08T19:32:26Zhttp://crdd.osdd.net/open/id/eprint/540This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5402011-12-08T19:32:26ZIdentification of ATP binding residues of a protein from its primary sequence.This study demonstrates that it is possible to predict 'ATP interacting residues' in a protein with moderate accuracy using its sequence. The evolutionary information is important for the identification of 'ATP interacting residues', as it provides more information compared to the primary sequence. This method will be useful for researchers studying ATP-binding proteins. Based on this study, a web server has been developed for predicting 'ATP interacting residues' in a protein http://www.imtech.res.in/raghava/atpint/.Jagat S ChauhanNitish K MishraG.P.S. Raghava2011-12-08T19:17:51Z2011-12-08T19:17:51Zhttp://crdd.osdd.net/open/id/eprint/514This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5142011-12-08T19:17:51ZPrediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information.These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://www.imtech.res.in/raghava/gtpbinder/ based on above models has been developed for predicting GTP interacting residues in a protein.Jagat S ChauhanNitish K MishraG.P.S. Raghava2011-12-08T19:37:09Z2011-12-08T19:37:09Zhttp://crdd.osdd.net/open/id/eprint/578This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5782011-12-08T19:37:09ZA machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search.Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the preprotein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.Aarti GargG.P.S. Raghava2011-12-08T19:30:46Z2011-12-08T19:30:46Zhttp://crdd.osdd.net/open/id/eprint/528This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5282011-12-08T19:30:46ZKiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials.Our results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "KiDoQ" has been developed http://crdd.osdd.net/raghava/kidoq, which allows the prediction of Ki value of a new ligand molecule against DHDPS.Aarti GargRupinder TewariG.P.S. Raghava2011-12-08T19:31:07Z2011-12-08T19:31:07Zhttp://crdd.osdd.net/open/id/eprint/530This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5302011-12-08T19:31:07ZVirtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis.The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time.Aarti GargRupinder TewariG.P.S. Raghava2012-01-05T15:16:49Z2015-01-07T05:42:37Zhttp://crdd.osdd.net/open/id/eprint/285This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/2852012-01-05T15:16:49ZMucins in protozoan parasites.Studies on host-pathogen interactions have led to the discovery of various cell surface associated and secretory molecules. Mucins and mucin-like molecules have recently been described in several protozoan parasites, at different stages of the life cycle. These share many structural and compositional features with mammalian mucins, but vary in several other aspects. It is now becoming evident that mucins in parasite are involved in cell-cell interaction and cell surface protection, thus helping the parasite to establish infection. A large number of mucin like genes from the parasite genome have been reported, and their expression differ during the developmental stages of the parasite. In this review, we describe the structure and functions of mucin and mucin-like molecules in parasitic protozoa.M JainD KaranS K BatraGrish C Varshney2012-09-11T06:22:55Z2012-09-11T06:22:55Zhttp://crdd.osdd.net/open/id/eprint/1188This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/11882012-09-11T06:22:55ZElucidation of N-terminal methionine excision pathway in mycobacteria.Pavitra Kanudia2011-12-08T19:36:10Z2011-12-08T19:36:10Zhttp://crdd.osdd.net/open/id/eprint/570This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5702011-12-08T19:36:10ZPrediction of nuclear proteins using SVM and HMM models.This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.Manish KumarG.P.S. Raghava2012-07-05T09:21:10Z2012-07-05T09:21:10Zhttp://crdd.osdd.net/open/id/eprint/1152This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/11522012-07-05T09:21:10ZBioinformatics approach for searching subunit vaccine candidates and adjuvants."SUMMARY OF THE THISIS IS ATTACHED"Sneh Lata2011-12-08T19:39:59Z2011-12-08T19:39:59Zhttp://crdd.osdd.net/open/id/eprint/597This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5972011-12-08T19:39:59ZApplication of machine learning techniques in predicting MHC binders.The machine learning techniques are playing a vital role in the field of immunoinformatics. In the past, a number of methods have been developed for predicting major histocompatibility complex (MHC)-binding peptides using machine learning techniques. These methods allow predicting MHC-binding peptides with high accuracy. In this chapter, we describe two machine learning technique-based methods, nHLAPred and MHC2Pred, developed for predicting MHC binders for class I and class II alleles, respectively. nHLAPred is a web server developed for predicting binders for 67 MHC class I alleles. This sever has two methods: ANNPred and ComPred. ComPred allows predicting binders for 67 MHC class I alleles, using the combined method [artificial neural network (ANN) and quantitative matrix] for 30 alleles and quantitative matrix-based method for 37 alleles. ANNPred allows prediction of binders for only 30 alleles purely based on the ANN. MHC2Pred is a support vector machine (SVM)-based method for prediction of promiscuous binders for 42 MHC class II alleles.Sneh LataManoj BhasinG.P.S. Raghava2011-12-08T19:35:03Z2011-12-08T19:35:03Zhttp://crdd.osdd.net/open/id/eprint/561This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5612011-12-08T19:35:03ZMHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes.MHCBN database updating is meant to facilitate immunologist in understanding the immune system and provide them the latest information. We feel that our database will complement the existing databases in serving scientific community.Sneh LataManoj BhasinG.P.S. Raghava2011-12-08T19:31:21Z2011-12-08T19:31:21Zhttp://crdd.osdd.net/open/id/eprint/532This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5322011-12-08T19:31:21ZAntiBP2: improved version of antibacterial peptide prediction.Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial peptides. Amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. Antibp2 will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. We also developed user friendly web server for the biological community.Sneh LataNitish K MishraG.P.S. Raghava2011-11-30T09:49:17Z2011-12-13T17:16:24Zhttp://crdd.osdd.net/open/id/eprint/613This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6132011-11-30T09:49:17ZCytoPred: a server for prediction and classification of cytokines.Cytokines are messengers of immune system. They are small secreted proteins that mediate and regulate the immune system, inflammation and hematopoiesis. Recent studies have revealed important roles played by the cytokines in adjuvants as therapeutic targets and in cancer therapy. In this paper, an attempt has been made to predict this important class of proteins and classify further them into families and subfamilies. A PSI-BLAST+Support Vector Machine-based hybrid approach is adopted to develop the prediction methods. CytoPred is capable of predicting cytokines with an accuracy of 98.29%. The overall accuracy of classification of cytokines into four families and further classification into seven subfamilies is 99.77 and 97.24%, respectively. It has been shown by comparison that CytoPred performs better than the already existing CTKPred. A user-friendly server CytoPred has been developed and available at http://www.imtech.res.in/raghava/cytopred.Sneh LataG.P.S. Raghava2011-12-08T19:34:36Z2011-12-09T09:42:21Zhttp://crdd.osdd.net/open/id/eprint/557This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5572011-12-08T19:34:36ZPrediction and classification of chemokines and their receptors.Chemokines are low molecular mass cytokine-like proteins that orchestrate myriads of immune functions like leukocyte trafficking, T cell differentiation, angiogenesis, hematopeosis and mast cell degranulation. Chemokines also play a role as HIV-1 inhibitor and act as potent natural adjuvant in antitumor immunotherapy. Receptors for these molecules are all seven-pass transmembrane G-protein-coupled receptors that are intimately involved with chemokines in a wide array of physiological and pathological conditions. These receptors also have a major role as co-receptors for HIV-1 entry into target cells. Therefore, chemokine receptors have proven to be excellent targets for small molecule in pharmaceutical industry. The immense importance of chemokines and their receptors motivated us to develop a support vector machine-based method ChemoPred to predict this important class of proteins and further classify them into subfamilies. ChemoPred is capable of predicting chemokines and chemokine receptors with an accuracy of 95.08% and 92.19%, respectively. The overall accuracy of classification of chemokines into three subfamilies was 96.00% and that of chemokine receptors into three families was 92.87%. The server ChemoPred is freely available at www.imtech.res.in/raghava/chemopred.Sneh LataG.P.S. Raghava2011-12-08T19:31:14Z2011-12-08T19:31:14Zhttp://crdd.osdd.net/open/id/eprint/531This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5312011-12-08T19:31:14ZPrediction of FAD interacting residues in a protein from its primary sequence using evolutionary information.This study suggests that evolutionary information of 17 amino acid patterns perform best for FAD interacting residues prediction. We also developed a web server which predicts FAD interacting residues in a protein which is freely available for academics.Nitish K MishraG.P.S. Raghava2011-11-30T09:45:02Z2011-12-13T17:18:41Zhttp://crdd.osdd.net/open/id/eprint/614This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6142011-11-30T09:45:02ZOxypred: prediction and classification of oxygen-binding proteins.This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins (available from http://www.imtech.res.in/raghava/oxypred/).S MuthukrishnanAarti GargG.P.S. Raghava2011-12-08T19:09:33Z2011-12-08T19:09:33Zhttp://crdd.osdd.net/open/id/eprint/494This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/4942011-12-08T19:09:33ZPrediction and classification of aminoacyl tRNA synthetases using PROSITE domains.We have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models using selected dipeptide composition demonstrates that certain types of dipeptide are preferred in aaRSs. We were able to identify PROSITE domains that are preferred in aaRSs and their classes, providing interesting insights into tRNA synthetases. The method developed in this study will be useful for researchers studying aaRS enzymes and tRNA biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/icaars/.Bharat PanwarG.P.S. Raghava2011-12-13T16:48:36Z2014-03-31T05:31:51Zhttp://crdd.osdd.net/open/id/eprint/389This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/3892011-12-13T16:48:36ZUniqueness and multiplicity of steady states in monocyclic enzyme cascades: a graph-theoretic analysis.Monocyclic enzyme cascades are important regulators of biochemical reactions in living cells. The reaction network may have either one steady state or many, depending on its structure. The occurrence of multiple steady states has important biological implications. A simple graph-theoretic method has been applied to five reaction mechanisms--which together cover many common monocyclic cascades--to determine which mechanisms generate just one steady state and which ones allow more than one state. It is shown that an unstable steady state in a multiplicity region can be usefully exploited and that in some cases transitions may occur between uniqueness and multiplicity regions. The possible effects of such transitions are discussed.P R Patnaik2011-12-08T19:36:49Z2011-12-08T19:36:49Zhttp://crdd.osdd.net/open/id/eprint/575This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5752011-12-08T19:36:49ZCognitive optimization of microbial PHB production in an optimally dispersed bioreactor by single and mixed cultures.Cognitive (or intelligent) models are often superior to mechanistic models for nonideal bioreactors. Two kinds of cognitive models--cybernetic and neural--were applied recently to fed-batch fermentation by Ralstonia eutropha in a bioreactor with optimum finite dispersion. In the present work, these models have been applied in simulation studies of co-cultures of R. eutropha and Lactobacillus delbrueckii. The results for both cognitive and mechanistic models have been compared with single cultures. Neural models were the most effective for both types of cultures and mechanistic models the least effective. Simulations with co-culture fermentations predicted more PHB than single cultures with all three types of models. Significantly, the predicted enhancements in PHB concentration by cognitive methods for mixed cultures were four to five times larger than the corresponding increases in biomass concentration. Further improvements are possible through a hybrid combination of all three types of models.Pratap R Patnaik2011-12-08T18:57:26Z2011-12-08T18:57:26Zhttp://crdd.osdd.net/open/id/eprint/492This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/4922011-12-08T18:57:26ZA simple approach for predicting protein-protein interactions.The availability of an increased number of fully sequenced genomes demands functional interpretation of the genomic information. Despite high throughput experimental techniques and in silico methods of predicting protein-protein interaction (PPI); the interactome of most organisms is far from completion. Thus, predicting the interactome of an organism is one of the major challenges in the post-genomic era. This manuscript describes Support Vector Machine (SVM) based models that have been developed for discriminating interacting and non-interacting pairs of proteins from their amino acid sequence. We have developed SVM models using various types of sequence compositions e.g. amino acid, dipeptide, biochemical property, split amino acid and pseudo amino acid composition. We also developed SVM models using evolutionary information in the form of Position Specific Scoring Matrix (PSSM) composition. We achieved maximum Matthews's correlation coefficient (MCC) of 1.00, 0.52 and 0.74 for Escherichia coli, Saccharomyces cerevisiae, and Helicobacter pylori, using dipeptide based SVM model at default threshold. It was observed that the performance of a prediction model depends on the dataset used for training and testing. In case of E. coli MCC decreased from 1.0 to 0.67 when evaluated on a new dataset. In order to understand PPI in different cellular environment, we developed species-specific and general models. It was observed that species-specific models are more accurate than general models. We conclude that the primary amino acid sequence based descriptors could be used to differentiate interacting from non-interacting protein pairs. Some amino acids tend to be favored in interacting pairs than non-interacting ones. Finally, a web server has been developed for predicting protein-protein interactions.Mamoon RashidSumathy RamasamyG.P.S. Raghava2011-12-12T15:39:58Z2014-03-28T11:18:34Zhttp://crdd.osdd.net/open/id/eprint/434This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/4342011-12-12T15:39:58ZA database management system for recombinant DNA clones and hosts for use in IBM personal computers.A RoyT K Roy2011-12-08T19:41:02Z2011-12-08T19:41:02Zhttp://crdd.osdd.net/open/id/eprint/606This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6062011-12-08T19:41:02ZBTXpred: prediction of bacterial toxins.This paper describes a method developed for predicting bacterial toxins from their amino acid sequences. All the modules, developed in this study, were trained and tested on a non-redundant dataset of 150 bacterial toxins that included 77 exotoxins and 73 endotoxins. Firstly, support vector machines (SVM) based modules were developed for predicting the bacterial toxins using amino acids and dipeptides composition and achieved an accuracy of 96.07% and 92.50%, respectively. Secondly, SVM based modules were developed for discriminating entotoxins and exotoxins, using amino acids and dipeptides composition and achieved an accuracy of 95.71% and 92.86%, respectively. In addition, modules have been developed for classifying the exotoxins (e.g. activate adenylate cyclase, activate guanylate cyclase, neurotoxins) using hidden Markov models (HMM), PSI-BLAST and a combination of the two and achieved overall accuracy of 95.75%, 97.87% and 100%, respectively. Based on the above study, a web server called 'BTXpred' has been developed, which is available at http://www.imtech.res.in/raghava/btxpred/. Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.Sudipto SahaG.P.S. Raghava2011-12-08T19:41:10Z2011-12-08T19:41:10Zhttp://crdd.osdd.net/open/id/eprint/607This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6072011-12-08T19:41:10ZPrediction of neurotoxins based on their function and source.We have developed a method NTXpred for predicting neurotoxins and classifying them based on their function and origin. The dataset used in this study consists of 582 non-redundant, experimentally annotated neurotoxins obtained from Swiss-Prot. A number of modules have been developed for predicting neurotoxins using residue composition based on feed-forwarded neural network (FNN), recurrent neural network (RNN), support vector machine (SVM) and achieved maximum accuracy of 84.19%, 92.75%, 97.72% respectively. In addition, SVM modules have been developed for classifying neurotoxins based on their source (e.g., eubacteria, cnidarians, molluscs, arthropods have been and chordate) using amino acid composition and dipeptide composition and achieved maximum overall accuracy of 78.94% and 88.07% respectively. The overall accuracy increased to 92.10%, when the evolutionary information obtained from PSI-BLAST was combined with SVM module of source classification. We have also developed SVM modules for classifying neurotoxins based on functions using amino acid, dipeptide composition and achieved overall accuracy of 83.11%, 91.10% respectively. The overall accuracy of function classification improved to 95.11%, when PSI-BLAST output was combined with SVM module. All the modules developed in this study were evaluated using five-fold cross-validation technique. The NTXpred is available at www.imtech.res.in/raghava/ntxpred/ and mirror site at http://bioinformatics.uams.edu/mirror/ntxpred.Sudipto SahaG.P.S. Raghava2011-12-08T19:40:06Z2011-12-08T19:40:06Zhttp://crdd.osdd.net/open/id/eprint/599This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5992011-12-08T19:40:06ZSearching and mapping of B-cell epitopes in Bcipep database.One of the major challenges in the field of subunit vaccine design is to identify the antigenic regions in an antigen, which can activate B cell. These antigenic regions are called B-cell epitopes. In this chapter, we describe how to use Bcipep, which is a database of experimentally determined linear B-cell epitopes of varying immunogenicity collected from literature and other publicly available databases. The current version of Bcipep database contains 3,031 entries that include 763 immunodominant, 1,797 immunogenic, and 471 null-immunogenic epitopes. The database provides a set of tools for analysis and extraction of data that includes keyword search, peptide mapping, and BLAST search. The database is available at http://www.imtech.res.in/raghava/bcipep/.Sudipto SahaG.P.S. Raghava2013-01-23T09:19:21Z2015-01-09T11:24:41Zhttp://crdd.osdd.net/open/id/eprint/1284This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/12842013-01-23T09:19:21ZA catalase-peroxidase for oxidation of β-lactams to their (R)-sulfoxides.In this communication we report for the first time a biocatalytic method for stereoselective oxidation of β-lactams, represented by penicillin-G, penicillin-V and cephalosporin-G to their (R)-sulfoxides. The method involves use of a bacterium, identified as Bacillus pumilis as biocatalyst. The enzyme responsible for oxidase activity has been purified and characterized as catalase-peroxidase (KatG). KatG of B. pumilis is a heme containing protein showing characteristic heme spectra with soret peak at 406 nm and visible peaks at 503 and 635 nm. The major properties that distinguish B. pumilis KatG from other bacterial KatGs are (i) it is a monomer and contains one heme per monomer, whereas KatGs of other bacteria are dimers or tetramers and have low heme content of about one per dimer or two per tetramer and (ii) its 12-residue, N-terminal sequence obtained by Edman degradation did not show significant similarity with any of known KatGs.Shefali SangerMohan PalLomary S MoonR S Jolly2011-12-08T19:40:20Z2011-12-13T17:10:37Zhttp://crdd.osdd.net/open/id/eprint/601This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6012011-12-08T19:40:20ZDPROT: prediction of disordered proteins using evolutionary information.The association of structurally disordered proteins with a number of diseases has engendered enormous interest and therefore demands a prediction method that would facilitate their expeditious study at molecular level. The present study describes the development of a computational method for predicting disordered proteins using sequence and profile compositions as input features for the training of SVM models. First, we developed the amino acid and dipeptide compositions based SVM modules which yielded sensitivities of 75.6 and 73.2% along with Matthew's Correlation Coefficient (MCC) values of 0.75 and 0.60, respectively. In addition, the use of predicted secondary structure content (coil, sheet and helices) in the form of composition values attained a sensitivity of 76.8% and MCC value of 0.77. Finally, the training of SVM models using evolutionary information hidden in the multiple sequence alignment profile improved the prediction performance by achieving a sensitivity value of 78% and MCC of 0.78. Furthermore, when evaluated on an independent dataset of partially disordered proteins, the same SVM module provided a correct prediction rate of 86.6%. Based on the above study, a web server ("DPROT") was developed for the prediction of disordered proteins, which is available at http://www.imtech.res.in/raghava/dprot/.Deepti SethiAarti GargG.P.S. Raghava2011-12-08T19:30:59Z2011-12-08T19:30:59Zhttp://crdd.osdd.net/open/id/eprint/529This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5292011-12-08T19:30:59ZBIAdb: a curated database of benzylisoquinoline alkaloids.A database of benzylisoquinoline compounds has been created, which provides comprehensive information about benzylisoquinoline alkaloids. This database will be very useful for those who are working in the field of drug discovery based on natural products. This database will also serve researchers working in the field of synthetic biology, as developing medicinally important alkaloids using synthetic process are one of important challenges. This database is available from http://crdd.osdd.net/raghava/biadb/.Deepak SinglaArun SharmaJasjit KaurBharat PanwarG.P.S. Raghava2011-12-08T19:39:37Z2011-12-08T19:39:37Zhttp://crdd.osdd.net/open/id/eprint/598This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5982011-12-08T19:39:37ZSearching haptens, carrier proteins, and anti-hapten antibodies.Haptens are small molecules that are usually nonimmunogenic unless coupled to some carrier proteins. The generation of anti-hapten antibodies is important for the development of immunodiagnostics and therapeutics. Recently, our group has developed a database called HaptenDB, which provides comprehensive information about 1,087 haptens. In this chapter, we describe following web tools integrated in HaptenDB: (i) keyword search facility allows search on major fields, (ii) browsing service, to display all haptens, carrier proteins and antibodies, and (iii) structure similarity search, which allows the users to search their structure against hapten structures.Shilpy SrivastavaMahender Kumar SinghG.P.S. RaghavaGrish C Varshney2011-12-08T19:38:22Z2011-12-08T19:38:22Zhttp://crdd.osdd.net/open/id/eprint/589This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/5892011-12-08T19:38:22ZMatrix-assisted refolding and redox properties of WhiB3/Rv3416 of Mycobacterium tuberculosis H37Rv.Redox stress is one of the major challenges faced by Mycobacterium tuberculosis during early infection and latency. The mechanism of sensing and adaptation to altered redox conditions is poorly understood. whiB family of Mtb is emerging as an important class of stress responsive genes. WhiB3/Rv3416 has been shown to be important for pathogenesis in animal model and was recently shown to co-ordinate a Fe-S cluster. Here, we report a simple, rapid and efficient matrix-assisted refolding method and important redox properties of WhiB3. Similar to other WhiB proteins, WhiB3 also has four conserved cysteine residues, where two of them are present in a CXXC motif. The Fe-S cluster of WhiB3 remained bound in the presence of strong protein denaturant. Upon cluster removal due to oxidation, the four cysteine residues which are ligands of Fe-S cluster, formed two intra-molecular disulfide bridges where one of them is possibly between the cysteines of CXXC motif, an important feature of several thiol-disulfide oxido-reductases. Far-UV CD spectroscopy revealed the presence of both alpha-helices and beta-strands in apo WhiB3. The secondary structural elements of apo WhiB3 were found resistant for thermal denaturation. The results demonstrated that apo WhiB3 functions as a protein disulfide reductase similar to thioredoxins. The importance of WhiB3 in redox sensing and its possible role in mycobacterial physiology has been discussed.Md Suhail AlamPushpa Agrawal2011-11-30T09:10:01Z2011-11-30T09:10:28Zhttp://crdd.osdd.net/open/id/eprint/618This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6182011-11-30T09:10:01ZInterdomain interaction reconstitutes the functionality of PknA, a eukaryotic type Ser/Thr kinase from Mycobacterium tuberculosis.Eukaryotic type Ser/Thr protein kinases have recently been shown to regulate a variety of cellular functions in bacteria. PknA, a transmembrane Ser/Thr protein kinase from Mycobacterium tuberculosis, when constitutively expressed in Escherichia coli resulted in cell elongation and therefore has been thought to be regulating morphological changes associated with cell division. Bioinformatic analysis revealed that PknA has N-terminal catalytic, juxtamembrane, transmembrane, and C-terminal extracellular domains, like known eukaryotic type Ser/Thr protein kinases from other bacteria. To identify the minimum region capable of exhibiting phosphorylation activity of PknA, we created several deletion mutants. Surprisingly, we found that the catalytic domain itself was not sufficient for exhibiting phosphorylation ability of PknA. However, the juxtamembrane region together with the kinase domain was necessary for the enzymatic activity and thus constitutes the catalytic core of PknA. Utilizing this core, we deduce that the autophosphorylation of PknA is an intermolecular event. Interestingly, the core itself was unable to restore the cell elongation phenotype as manifested by the full-length protein in E. coli; however, its co-expression along with the C-terminal region of PknA can associate them in trans to reconstitute a functional protein in vivo. Therefore, these findings argue that the transmembrane and extracellular domains of PknA, although dispensable for phosphorylation activities, are crucial in responding to signals. Thus, our results for the first time establish the significance of different domains in a bacterial eukaryotic type Ser/Thr kinase for reconstitution of its functionality.Meghna ThakurRachna ChabaAlok K MondalPradip K Chakraborti2013-01-23T09:52:39Z2013-01-23T09:52:39Zhttp://crdd.osdd.net/open/id/eprint/1276This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/12762013-01-23T09:52:39ZVIRsiRNAdb: a curated database of experimentally validated viral siRNA/shRNA.RNAi technology has been emerging as a potential modality to inhibit viruses during past decade. In literature a few siRNA databases have been reported that focus on targeting human and mammalian genes but experimentally validated viral siRNA databases are lacking. We have developed VIRsiRNAdb, a manually curated database having comprehensive details of 1358 siRNA/shRNA targeting viral genome regions. Further, wherever available, information regarding alternative efficacies of above 300 siRNAs derived from different assays has also been incorporated. Important fields included in the database are siRNA sequence, virus subtype, target genome region, cell type, target object, experimental assay, efficacy, off-target and siRNA matching with reference viral sequences. Database also provides the users with facilities of advance search, browsing, data submission, linking to external databases and useful siRNA analysis tools especially siTarAlign which align the siRNA with reference viral genomes or user defined sequences. VIRsiRNAdb contains extensive details of siRNA/shRNA targeting 42 important human viruses including influenza virus, hepatitis B virus, HPV and SARS Corona virus. VIRsiRNAdb would prove useful for researchers in picking up the best viral siRNA for antiviral therapeutics development and also for developing better viral siRNA design tools. The database is freely available at http://crdd.osdd.net/servers/virsirnadb.Nishant ThakurAbid QureshiManoj Kumar2011-12-08T19:40:41Z2011-12-08T19:40:41Zhttp://crdd.osdd.net/open/id/eprint/604This item is in the repository with the URL: http://crdd.osdd.net/open/id/eprint/6042011-12-08T19:40:41ZIdentification of proteins secreted by malaria parasite into erythrocyte using SVM and PSSM profiles.This study demonstrates that secretory proteins have different residue composition than non-secretory proteins. Thus, it is possible to predict secretory proteins from its residue composition-using machine learning technique. The multiple sequence alignment provides more information than sequence itself. Thus performance of method based on PSSM profile is more accurate than method based on sequence composition. A web server PSEApred has been developed for predicting secretory proteins of malaria parasites,the URL can be found in the Availability and requirements section.Ruchi VermaAjit TiwariSukhwinder KaurGrish C VarshneyG.P.S. Raghava