Ensemble Architecture for Prediction of Enzyme-ligand Binding Residues Using Evolutionary Information.

Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods.

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

Enzymes are biological catalysts that play an important role in determining the patterns of chemical transformations pertaining to life. Many milestones have been achieved in unraveling the mechanisms in which the enzymes orchestrate various cellular processes using experimental and computational approaches. Experimental studies generating nearly all possible mutations of target enzymes have been aided by rapid computational approaches aiming at enzyme functional classification, understanding domain organization, functional site identification.

Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach.

Protein interactions with ribonucleic acids (RNA) are well-known to be crucial for a wide range of cellular processes such as transcriptional regulation, protein synthesis or translation, and post-translational modifications. Identification of the RNA-interacting residues can provide insights into these processes and aid in relevant biotechnological manipulations. Owing to their eventual potential in combating diseases and industrial production, several computational attempts have been made over years using sequence- and structure-based information.

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.

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.

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.

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.