K<Emphasis Type="Italic">i</Emphasis> DoQ: using docking based energy scores to develop ligand based model for predicting antibacterials

Background  

Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general.     

Stabilization of type I collagen against collagenases (type I) and thermal degradation using iron complex

The widespread application of collagen as a biomaterial warrants research in understanding the stabilization of the same. In this study, interaction of iron-tetrakis (hydroxymethyl) phosphonium (THP) complex with type I collagen has been investigated. DSC and hydrothermal measurement studies reveal that the shrinkage temperature of iron-THP treated rat tail tendon (RTT) collagen is 33 degrees C higher than that of native RTT collagen. Fe-THP complex also brings about high degree of enzymatic stability to type I collagen. The effect of Fe-THP on the conformation of collagen was studied using circular dichroism and it was found that no major alterations in the triple helical structure of collagen occur on treatment with Fe-THP. It is observed from viscosity experiment results that though Fe-THP complex is able to bring about long range ordering of collagen, as evident from the thermal and enzymatic stability imparted to collagen, this ordering does not lead to any aggregation of collagen. Since THPS is reducing in nature, it is expected to keep iron in the +2 state and if THP chelates to Fe(II), the hydrolytic behavior of iron can also be controlled.     

Prediction of mitochondrial proteins using support vector machine and hidden Markov model

Mitochondria are considered as one of the core organelles of eukaryotic cells hence prediction of mitochondrial proteins is one of the major challenges in the field of genome annotation. This study describes a method, MitPred, developed for predicting mitochondrial proteins with high accuracy. The data set used in this study was obtained from Guda, C., Fahy, E. & Subramaniam, S. (2004) Bioinformatics 20, 1785-1794. First support vector machine-based modules/methods were developed using amino acid and dipeptide composition of proteins and achieved accuracy of 78.37 and 79.38%, respectively. The accuracy of prediction further improved to 83.74% when split amino acid composition (25 N-terminal, 25 C-terminal, and remaining residues) of proteins was used. Then BLAST search and support vector machine-based method were combined to get 88.22% accuracy. Finally we developed a hybrid approach that combined hidden Markov model profiles of domains (exclusively found in mitochondrial proteins) and the support vector machine-based method. We were able to predict mitochondrial protein with 100% specificity at a 56.36% sensitivity rate and with 80.50% specificity at 98.95% sensitivity. The method estimated 9.01, 6.35, 4.84, 3.95, and 4.25% of proteins as mitochondrial in Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, mouse, and human proteomes, respectively. MitPred was developed on the above hybrid approach.     

Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network

In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, C alpha-H...O and C alpha-H...pi interactions in proteins using artificial neural network. Both standard feed-forward neural network (FNN) and recurrent neural networks (RNN) have been trained and tested using five-fold cross-validation on a non-homologous dataset of 2298 protein chains where no pair of sequences has more than 25% sequence identity. It has been found that the prediction accuracy varies with the separation distance between donor and acceptor residues. The maximum sensitivity achieved with RNN for C alpha-H...O is 51.2% when donor and acceptor residues are four residues apart (i.e. at delta D-A = 4) and for C alpha-H...pi is 82.1% at delta D-A = 3. The performance of RNN is increased by 1-3% for both types of interactions when PSIPRED predicted protein secondary structure is used. Overall, RNN performs better than feed-forward networks at all separation distances between donor-acceptor pair for both types of interactions. Based on the observations, a web server CHpredict (available at http://www.imtech.res.in/raghava/chpredict/) has been developed for predicting donor and acceptor residues in C alpha-H...O and C alpha-H...pi interactions in proteins.     

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network

B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.     

VICMpred： An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

In this study, an attempt has been made to predict the major functions of gramnegative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins （255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors）. First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins （http：//www.imtech.res.in/raghava/vicmpred/）.     

Genome sequence of the halotolerant bacterium Imtechella halotolerans K1T

We report the 3.087-Mb genome sequence of Imtechella halotolerans K1(T), isolated from an estuarine water sample collected from Kochi, Kerala, India. Strain K1 was recently reported as a novel genus of the family Flavobacteriaceae.     

Luminescence properties of Tb3+ doped Sr2SnO4 green phosphor in UV/VUV regions

Polycrystalline Sr₂SnO₄ phosphors doped with Tb³⁺ were prepared by conventional solid‐state reaction method. Materials were characterized by powder XRD and EDS techniques. The luminescence properties of these materials were investigated under UV and VUV excitation. Upon excitation at 272 nm, phosphors exhibited intense emissions at 492 and 543 nm due to ⁵D₄ → ⁷ F₆ and ⁵D₄ → ⁷ F₅ transitions of Tb³⁺ ions, respectively. Materials also exhibited strong emissions from these transitions under VUV excitation at 147, 173 and 230 nm. Quantitative analysis of the spectra indicated probable applications of these phosphors for PDP and other display devices as green emitting phosphors. Copyright © 2012 John Wiley & Sons, Ltd.     

Identification of Bilirubin Binding Site in Type I Collagen

The interaction of bilirubin with collagen in the significance of jaundice incidence have been previously reported and investigated. The novel peptide sequences containing bilirubin binding domain was identified and located to develop a basis for further studies investigating the interactions of collagen with bilirubin in the present study. In this study an intricate interaction between bilirubin and collagen was characterized and their binding domain has been established using in-gel digestion and LC–MS/MS analysis based on the collagen sequencing and peptide mass fingerprinting. The biotinylated bilirubin derivatives bind to α₁(I) chain but not to α₂(I) chains which clearly designates that bilirubin shows greater affinity to α₁ chains of collagen. The intact proteins collected after analyzing the resulting complex mixture of peptides was used for peptide mapping. Using the electrospray method, among the other peptide sequence information obtained, the molecular weight of collagen alpha-2(I) chain was obtained by locating a 130 kDa weight peptide sequences with greater pi value (9.14) with 1,364 amino acid residues and collagen alpha-1(I) chain with 1,463 amino acid residues with 138.9 kDa molecular weight. This information leads to locate the exact sequence of these helices focussing on the domain identification. The total charge of the peptide domain sequences infers that the bilirubin participates in the electrostatic mode of interaction with collagen peptide. Moreover, other modes of interactions such as hydrogen bonding, covalent interactions and hydrophobic interactions are possible.