Sukanta Mondal

Chou׳s pseudo amino acid composition improves sequence-based antifreeze protein prediction

Antifreeze proteins (AFP) in living organisms play a key role in their tolerance to extremely cold temperatures and have a wide range of biotechnological applications. But on account of diversity, their identification has been challenging to biologists. Earlier work explored in this area has yet to cover introduction of sequence order information which is known to represent important properties of various proteins and protein systems for prediction purposes. In this study, the effect of Chous pseudo amino acid composition that presents sequence order of proteins was systematically explored using support vector machines for AFP prediction. Our findings suggest that introduction of sequence order information helps identify AFPs with an accuracy of 84.75% on independent test dataset, outperforming approaches such as AFP-Pred and iAFP. The relative performance calculated using Youdens Index (Sensitivity+Specificity-1) was found to be 0.71 for our predictor (AFP-PseAAC), 0.48 for AFP-Pred and 0.05 for iAFP. We hope this novel prediction approach will aid in AFP based research for biotechnological applications.

Sequence-based prediction of protein-protein interaction sites with L1-logreg classifier.

Protein-protein interactions are of central importance for virtually every process in a living cell. Information about the interaction sites in proteins improves our understanding of disease mechanisms and can provide the basis for new therapeutic approaches. Since a multitude of unique residue-residue contacts facilitate the interactions, protein-protein interaction sites prediction has become one of the most important and challenging problems of computational biology. Although much progress in this field has been reported, this problem is yet to be satisfactorily solved. Here, a novel method (LORIS: L1-regularized LOgistic Regression based protein-protein Interaction Sites predictor) is proposed, that identifies interaction residues, using sequence features and is implemented via the L1-logreg classifier. Results show that LORIS is not only quite effective, but also, performs better than existing state-of-the art methods. LORIS, available as standalone package, can be useful for facilitating drug-design and targeted mutation related studies, which require a deeper knowledge of protein interactions sites.

THGS: a web‐based database of Transmembrane Helices in Genome Sequences

Transmembrane Helices in Genome Sequences (THGS) is an interactive web-based database, developed to search the transmembrane helices in the user-interested gene sequences available in the Genome Database (GDB). The proposed database has provision to search sequence motifs in transmembrane and globular proteins. In addition, the motif can be searched in the other sequence databases (Swiss-Prot and PIR) or in the macromolecular structure database, Protein Data Bank (PDB). Further, the 3D structure of the corresponding queried motif, if it is available in the solved protein structures deposited in the Protein Data Bank, can also be visualized using the widely used graphics package RASMOL. All the sequence databases used in the present work are updated frequently and hence the results produced are up to date. The database THGS is freely available via the world wide web and can be accessed at http:// pranag.physics.iisc.ernet.in/thgs/ or http://144.16. 71.10/thgs/.

MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information.

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein–mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

Mycobacterium tuberculosis Cell Division Protein, FtsE, is an ATPase in Dimeric Form

FtsE is one of the earliest cell division proteins that assembles along with FtsX at the mid-cell site during cell division in Escherichia coli. Both these proteins are highly conserved across diverse bacterial genera and are predicted to constitute an ABC transporter type complex, in which FtsE is predicted to bind ATP and hydrolyse it, and FtsX is predicted to be an integral membrane protein. We had earlier reported that the MtFtsE of the human pathogen, Mycobacterium tuberculosis, binds ATP and interacts with MtFtsX on the cell membrane of M. tuberculosis and E. coli. In this study, we demonstrate that MtFtsE is an ATPase, the active form of which is a dimer, wherein the participating monomers are held together by non-covalent interactions, with the Cys84 of each monomer present at the dimer interface. Under oxidising environment, the dimer gets stabilised by the formation of Cys84–Cys84 disulphide bond. While the recombinant MtFtsE forms a dimer on the membrane of E. coli, the native MtFtsE seems to be in a different conformation in the M. tuberculosis membrane. Although disulphide bridges were not observed on the cytoplasmic side (reducing environment) of the membrane, the two participating monomers could be isolated as dimers held together by non-covalent interactions. Taken together, these findings show that MtFtsE is an ATPase in the non-covalent dimer form, with the Cys84 of each monomer present in the reduced form at the dimer interface, without participating in the dimerisation or the catalytic activity of the protein.

Daboialectin, a C-type lectin from Russell's viper venom induces cytoskeletal damage and apoptosis in human lung cancer cells in vitro

ABSTRACT ‘Daboialectin’, a low molecular weight C‐type lectin (18.5 kDa) isolated from Russells viper venom showed cytotoxic effects on human broncho‐alveolar carcinoma derived (A549) cell lines. Daboialectin induced inhibition of A549 cell growth was time and concentration dependent with severe morphological changes by altering the functions of small GTPases such as Rac, Rho and cdc42. ROS induced DNA damage may result in apoptosis by inducing disruption of membrane integrity, blebbing and nuclear disintegration by activating caspases. Our results indicate that Daboialectin, a snake c type lectin (Snaclec) isolated from RVV alters morphology of A549 cells via regulation of cytoskeleton through RHO‐GTPases. On other hand, the HSP70 and some other anti‐apoptotic proteins required for the survival of cancer cells was found to be down‐regulated in presence of Daboialectin. Daboialectin was also found to be inhibitory to anti‐adhesive and anti‐invasive to A549 cells in vitro. Daboialectin is the first Snaclec reported to induce cytoskeletal changes through regulation of RHO‐GTPases and blocking anti‐apoptotic pathway for a cancer cell line. HIGHLIGHTSA C‐type lectin (Daboialectin) was isolated from Russells viper venom.Daboialectin induced cytoskeletal changes through Rho GTPases.Daboialectin blocked the anti‐apoptotic pathway for A 549 cells.

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information

Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU) for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues) in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues) in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

2,2 `-Diaminodibenzyl: a rare case of crystallographically non-compliant molecular symmetry

The title compound,

Ensemble Architecture for Prediction of Enzyme–Ligand Binding Residues using Evolutionary Information

C_{14}H_{16}N_2

Applying Knowledge of Enzyme Biochemistry to the Prediction of Functional Sites for Aiding Drug Discovery

, adopts a trans-planar conformation. However, the molecule, which possesses