Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins

In this study, an attempt has been made to develop a neural network-based method for predicting segments in proteins containing aromatic-backbone NH (Ar-NH) interactions using multiple sequence alignment. We have analyzed 3121 segments seven residues long containing Ar-NH interactions, extracted from 2298 non-redundant protein structures where no two proteins have more than 25% sequence identity. Two consecutive feed-forward neural networks with a single hidden layer have been trained with standard back-propagation as learning algorithm. The performance of the method improves from 0.12 to 0.15 in terms of Matthews correlation coefficient (MCC) value when evolutionary information (multiple alignment obtained from PSI-BLAST) is used as input instead of a single sequence. The performance of the method further improves from MCC 0.15 to 0.20 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields an overall prediction accuracy of 70.1% and an MCC of 0.20 when tested by five-fold cross-validation. Overall the performance is 15.2% higher than the random prediction. The method consists of two neural networks: (i) a sequence-to-structure network which predicts the aromatic residues involved in Ar-NH interaction from multiple alignment of protein sequences and (ii) a structure-to structure network where the input consists of the output obtained from the first network and predicted secondary structure. Further, the actual position of the donor residue within the 'potential' predicted fragment has been predicted using a separate sequence-to-structure neural network. Based on the present study, a server Ar_NHPred has been developed which predicts Ar-NH interaction in a given amino acid sequence. The web server Ar_NHPred is available at and (mirror site).     

BTEVAL: a server for evaluation of beta-turn prediction methods

This paper describes a web server BTEVAL, developed for assessing the performance of newly developed beta-turn prediction method and it's ranking with respect to other existing beta-turn prediction methods. Evaluation of a method can be carried out on a single protein or a number of proteins. It consists of clean data set of 426 non-homologous proteins with seven subsets of these proteins. Users can evaluate their method on any subset or a complete set of data. The method is assessed at amino acid level and performance is evaluated in terms of Qtotal, Qpredicted, Qobserved and MCC measures. The server also compares the performance of the method with other existing beta-turn prediction methods such as Chou-Fasman algorithm, Thornton's algorithm, GORBTURN, 1-4 and 2-3 Correlation model, Sequence coupled model and BTPRED. The server is accessible from http://imtech.res.in/raghava/bteval/     

Prediction of beta-turns in proteins from multiple alignment using neural network

A neural network-based method has been developed for the prediction of beta-turns in proteins by using multiple sequence alignment. Two feed-forward back-propagation networks with a single hidden layer are used where the first-sequence structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position-specific scoring matrices. The initial predictions from the first network and PSIPRED-predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. A significant improvement in prediction accuracy has been achieved by using evolutionary information contained in the multiple sequence alignment. The final network yields an overall prediction accuracy of 75.5% when tested by sevenfold cross-validation on a set of 426 nonhomologous protein chains. The corresponding Q(pred), Q(obs), and Matthews correlation coefficient values are 49.8%, 72.3%, and 0.43, respectively, and are the best among all the previously published beta-turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach.     

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.     

Nucleosome, the main autoantigen in systemic lupus erythematosus, induces direct dendritic cell activation via a MyD88-independent pathway: consequences on inflammation

Nucleosome is the major autoantigen in systemic lupus erythematosus. It is found as a circulating complex in the sera of patients and seems to play a key role in disease development. In this study, we show for the first time that physiologic concentrations of purified nucleosomes directly induce in vitro dendritic cell (DC) maturation of mouse bone marrow-derived DC, human monocyte-derived DC (MDDC), and purified human myeloid DC as observed by stimulation of allogenic cells in MLR, cytokine secretion, and CD86 up-regulation. Importantly, nucleosomes act as free complexes without the need for immune complex formation or for the presence of unmethylated CpG DNA motifs, and we thus identified a new mechanism of DC activation by nucleosomes. We have clearly demonstrated that this activation is nucleosome-specific and endotoxin-independent. Particularly, nucleosomes induce MDDC to secrete cytokines known to be detected in high concentrations in the sera of patients. Moreover, activated MDDC secrete IL-8, a neutrophil chemoattractant also detected in patient sera, and thus might favor the inflammation observed in patients. Both normal and lupus MDDC are sensitive to nucleosome-induced activation. Finally, injection of purified nucleosomes to normal mice induces in vivo DC maturation. Altogether, these results strengthen the key role of nucleosomes in systemic lupus erythematosus and might explain how peripheral tolerance is broken in patients.     

Infection of Bacterial Endosymbionts in Insects: A Comparative Study of Two Techniques viz PCR and FISH for Detection and Localization of Symbionts in Whitefly,Bemisia tabaci

Bacterial endosymbionts have been associated with arthropods and large number of the insect species show interaction with such bacteria. Different approaches have been used to understand such symbiont- host interactions. The whitefly, Bemisia tabaci, a highly invasive agricultural pest, harbors as many as seven different bacterial endosymbionts. These bacterial endosymbionts are known to provide various nutritional, physiological, environmental and evolutionary benefits to its insect host. In this study, we have tried to compare two techniques, Polymerase chain reaction (PCR) and Flourescence in situ Hybridisation (FISH) commonly used for identification and localization of bacterial endosymbionts in B. tabaci as it harbors one of the highest numbers of endosymbionts which have helped it in becoming a successful global invasive agricultural pest. The amplified PCR products were observed as bands on agarose gel by electrophoresis while the FISH samples were mounted on slides and observed under confocal microscope. Analysis of results obtained by these two techniques revealed the advantages of FISH over PCR. On a short note, performing FISH, using LNA probes proved to be more sensitive and informative for identification as well as localization of bacterial endosymbionts in B. tabaci than relying on PCR. This study would help in designing more efficient experiments based on much reliable detection procedure and studying the role of endosymbionts in insects.     

The influence of cis‐acting P1 protein and translational elements on the expression of Potato virus Y helper‐component proteinase (HCPro) in heterologous systems and its suppression of silencing activity

In the Potyvirus genus, the P1 protein is the first N‐terminal product processed from the viral polyprotein, followed by the helper‐component proteinase (HCPro). In silencing suppression patch assays, we found that Potato virus Y (PVY) HCPro expressed from a P1‐HCPro sequence increased the accumulation of a reporter gene, whereas protein expressed from an HCPro sequence did not, even with P1 supplied in trans. This enhancing effect of P1 has been noted in other potyviruses, but has remained unexplained. We analysed the accumulation of PVY HCPro in infiltrated tissues and found that it was higher when expressed from P1‐HCPro than from HCPro sequences. Co‐expression of heterologous suppressors increased the steady‐state level of mRNA expressed from the HCPro sequence, but not that of protein. This suggests that, in the absence of P1 upstream, either HCPro acquires a conformation that affects negatively its activity or stability, or that its translation is reduced. To test these options, we purified HCPro expressed in the presence or absence of upstream P1, and found no difference in purification pattern and final soluble state. By contrast, alteration of the Kozak context in the HCPro mRNA sequence to favour translation increased partially suppressor accumulation and activity. Furthermore, protein activity was not lower than in protein expressed from P1‐HCPro sequences. Thus, a direct role for P1 on HCPro suppressor activity or stability, by influencing its conformation during translation, can be excluded. However, P1 could still have an indirect effect favouring HCPro accumulation. Our data highlight the relevance of cis‐acting translational elements in the heterologous expression of HCPro.