BIAdb: A curated database of benzylisoquinoline alkaloids

Background: Benzylisoquinoline is the structural backbone of many alkaloids with a wide variety of structures including papaverine, noscapine, codeine, morphine, apomorphine, berberine, protopine and tubocurarine. Many benzylisoquinoline alkaloids have been reported to show therapeutic properties and to act as novel medicines. Thus it is important to collect and compile benzylisoquinoline alkaloids in order to explore their usage in medicine. Description: We extract information about benzylisoquinoline alkaloids from various sources like PubChem, KEGG, KNApSAcK and manual curation from literature. This information was processed and compiled in order to create a comprehensive database of benzylisoquinoline alkaloids, called BIAdb. The current version of BIAdb contains information about 846 unique benzylisoquinoline alkaloids, with multiple entries in term of source, function leads to total number of 2504 records. One of the major features of this database is that it provides data about 627 different plant species as a source of benzylisoquinoline and 114 different types of function performed by these compounds. A large number of online tools have been integrated, which facilitate user in exploring full potential of BIAdb. In order to provide additional information, we give external links to other resources/databases. One of the important features of this database is that it is tightly integrated with Drugpedia, which allows managing data in fixed/flexible format. Conclusions: 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/

Identification of Proteins Secreted by Malaria Parasite into Erythrocyte using SVM and PSSM profiles

Background: Malaria parasite secretes various proteins in infected RBC for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine/drug against malaria. The existing motif-based methods have got limited success due to lack of universal motif in all secretory proteins of malaria parasite. Results: In this study a systematic attempt has been made to develop a general method for predicting secretory proteins of malaria parasite. All models were trained and tested on a non-redundant dataset of 252 secretory and 252 non-secretory proteins. We developed SVM models and achieved maximum MCC 0.72 with 85.65% accuracy and MCC 0.74 with 86.45% accuracy using amino acid and dipeptide composition respectively. SVM models were developed using split-amino acid and split-dipeptide composition and achieved maximum MCC 0.74 with 86.40% accuracy and MCC 0.77 with accuracy 88.22% respectively. In this study, for the first time PSSM profiles obtained from PSI-BLAST, have been used for predicting secretory proteins. We achieved maximum MCC 0.86 with 92.66% accuracy using PSSM based SVM model. All models developed in this study were evaluated using 5-fold cross-validation technique. Conclusion: 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

Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information

Background: Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly bound, sometimes covalently, flavin adenine dinucleotide (FAD) or flavin mono nucleotide (FMN). The interaction between flavin nucleotide and amino acids of flavoprotein is essential for their functionality. Thus identification of FAD interacting residues in a FBP is an important step for understanding their function and mechanism. Results: In this study, we describe models developed for predicting FAD interacting residues using 15, 17 and 19 window pattern. Support vector machine (SVM) based models have been developed using binary pattern of amino acid sequence of protein and achieved maximum accuracy 69.65% with Mathew's Correlation Coefficient (MCC) 0.39 and Area Under Curve (AUC) 0.773. The performance of these models have been improved significantly from 69.65% to 82.86% with MCC 0.66 and AUC 0.904, when evolutionary information is used as input in SVM. The evolutionary information was generated in form of position specific score matrix (PSSM) profile by using PSI-BLAST at e-value 0.001. All models were developed on 198 non-redundant FAD binding protein chains containing 5172 FAD interacting residues and evaluated using fivefold cross-validation technique. Conclusion: 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

Weak acids as an alternative anti-microbial therapy

Weak acids such as acetic acid and N-acetyl cysteine (NAC) at pH less than their pKa can effectively eradicate biofilms due to their ability to penetrate the biofilm matrix and the cell membrane. However, the optimum conditions for their activity against drug resistant strains, and safety, need to be understood for their application to treat infections or to inactivate biofilms on hard surfaces. Here, we investigate the efficacy and optimum conditions at which weak acids can eradicate biofilms. We compared the efficacy of various mono and triprotic weak acids such as N-acetyl cysteine (NAC), acetic acid, formic acid and citric acid, in eradicating biofilms. We found that monoprotic weak acids/acid drugs can kill mucoid P. aeruginosa mucA biofilm bacteria provided the pH is less than their pKa, demonstrating that the extracellular biofilm matrix does not protect the bacteria from the activity of the weak acids. Triprotic acids, such as citric acid, kill biofilm bacteria at pH ​< ​pKa1. However, at a pH between pKa1 and pKa2, citric acid is effective in killing the bacteria at the core of biofilm microcolonies but does not kill the bacteria on the periphery. The efficacy of a monoprotic weak acid (NAC) and triprotic weak acid (citric acid) were tested on biofilms formed by Klebsiella pneumoniae KP1, Pseudomonas putida OUS82, Staphylococcus aureus 15981, P. aeruginosa DK1-NH57388A, a mucoid cystic fibrosis isolate and P. aeruginosa PA_D25, an antibiotic resistant strain. We showed that weak acids have a broad spectrum of activity against a wide range of bacteria, including antibiotic resistant bacteria. Further, we showed that a weak acid drug, NAC, can kill bacteria without being toxic to human cells, if its pH is maintained close to its pKa. Thus weak acids/weak acid drugs target antibiotic resistant bacteria and eradicate the persister cells in biofilms which are tolerant to other conventional methods of biofilm eradication

Identification of NAD interacting residues in proteins

Background: Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD+ or NAD) is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP) have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD+ to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs) from amino acid sequence using bioinformatics tools. Results: We extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His) in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM) based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC) 0.47 with accuracy 74.13% at window length 17. We also developed a SVM based method using evolutionary information in the form of position specific scoring matrix (PSSM) and obtained maximum MCC 0.75 with accuracy 87.25%. Conclusion: 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/

Interleukin-1 regulates multiple atherogenic mechanisms in response to fat feeding

Background: Atherosclerosis is an inflammatory process that develops in individuals with known risk factors that include hypertension and hyperlipidaemia, influenced by diet. However, the interplay between diet, inflammatory mechanisms and vascular risk factors requires further research. We hypothesised that interleukin-1 (IL-1) signaling in the vessel wall would raise arterial blood pressure and promote atheroma. Methodology/Principal Findings: Apoe(-/-) and Apoe(-/-)/IL-1R1(-/-) mice were fed high fat diets for 8 weeks, and their blood pressure and atherosclerosis development measured. Apoe(-/-)/IL-R1(-/-) mice had a reduced blood pressure and significantly less atheroma than Apoe(-/-) mice. Selective loss of IL-1 signaling in the vessel wall by bone marrow transplantation also reduced plaque burden (p<0.05). This was associated with an IL-1 mediated loss of endothelium-dependent relaxation and an increase in vessel wall Nox 4. Inhibition of IL-1 restored endothelium-dependent vasodilatation and reduced levels of arterial oxidative stress. Conclusions/Significance: The IL-1 cytokine system links atherogenic environmental stimuli with arterial inflammation, oxidative stress, increased blood pressure and atherosclerosis. This is the first demonstration that inhibition of a single cytokine can block the rise in blood pressure in response to an environmental stimulus. IL-1 inhibition may have profound beneficial effects on atherogenesis in man

A High-Value, Low-Cost Bubble Continuous Positive Airway Pressure System for Low-Resource Settings: Technical Assessment and Initial Case Reports

Acute respiratory infections are the leading cause of global child mortality. In the developing world, nasal oxygen therapy is often the only treatment option for babies who are suffering from respiratory distress. Without the added pressure of bubble Continuous Positive Airway Pressure (bCPAP) which helps maintain alveoli open, babies struggle to breathe and can suffer serious complications, and frequently death. A stand-alone bCPAP device can cost $6,000, too expensive for most developing world hospitals. Here, we describe the design and technical evaluation of a new, rugged bCPAP system that can be made in small volume for a cost-of-goods of approximately$350. Moreover, because of its simple designﾗconsumergrade pumps, medical tubing, and regulators—it requires only the simple replacement of a ,$1 diaphragm approximately every 2 years for maintenance. The low-cost bCPAP device delivers pressure and flow equivalent to those of a reference bCPAP system used in the developed world. We describe the initial clinical cases of a child with bronchiolitis and a neonate with respiratory distress who were treated successfully with the new bCPAP device

Analysis and prediction of cancerlectins using evolutionary and domain information

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.</p> <p>Results</p> <p>All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.</p> <p>Conclusion</p> <p>BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at <url>http://www.imtech.res.in/raghava/cancer_pred/</url></p

Dynamics of growth and weight transitions in a pediatric cohort from India

<p>Abstract</p> <p>Background</p> <p>There is paucity of information regarding time trends of weight status in children from rapidly developing economies like India. The aim of the study was to analyse the dynamics of growth and weight transitions in a cohort of school children from India.</p> <p>Methods</p> <p>A population of 25 228 children was selected using stratified random sampling method from schools in a contiguous area in Ernakulam District, Kerala, India. Weight and height were measured at two time points, one in 2003-04 and another in 2005-06. The paired data of 12 129 children aged 5-16 years were analysed for the study.</p> <p>Results</p> <p>The mean interval between the two surveys was 2.02 ± 0.32 years. The percentage of underweight, normal weight, overweight and obese children in the year 2003-04 were 38.4%, 56.6%, 3.7%, and 1.3% respectively. The corresponding figures in year 2005-06 were 29.9%, 63.6%, 4.8% and 1.7% respectively. Among the underweight children, 34.8% migrated to normal weight status and 0.1% migrated to overweight status. Conversion of underweight to normal weight predominated in urban area and girls. Among the normal weight children, 8.6% migrated to underweight, 4.1% migrated to overweight and 0.4% migrated to obesity. Conversion of normal weight to overweight status predominated in urban area, private schools and boys. Conversion of normal weight to underweight predominated in rural area, government schools and boys. Among the overweight children, 26.7% migrated to normal weight status, 16.4% became obese and 56.9% retained their overweight status. Of the obese children, 6.2% improved to normal weight status, 25.3% improved to overweight status and 68.5% remained as obese in 2005-06. There was significant difference in trends between socio demographic subgroups regarding conversion of underweight status to normal weight as well as normal weight status to overweight.</p> <p>Conclusion</p> <p>The study population is experiencing rapid growth and nutritional transitions characterised by a decline in the underweight population coupled with an escalation of the overweight population. The heterogeneous nature of this transition appears to be due to differences in socio demographic factors.</p

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

<p>Abstract</p> <p>Background</p> <p>The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.</p> <p>Results</p> <p>Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.</p> <p>Conclusion</p> <p>These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at <url>http://www.imtech.res.in/raghava/eslpred2/</url>.</p