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Computational methods are being employed to predict active binding sites of enzymes. The principal reasons are that the pace of discovery of new proteins is increasing, outpacing the ability to characterize them in conventional biochemical and structural techniques; in addition advances in computational, structural and force data are used in an iterative manner to improve accuracy of active site prediction. From methods using amino acid and nucleotide sequences evidence is available that residues in the enzyme core are selected for stability while those at the surface, which are sites of protein interaction, trade off stability for ligand interactivity. In THEMATICS method the underlying concept is that active site residues involved in specific biochemical actions have predictable chemical properties which are identified using pH titrations of the enzyme activity. A more recent method uses a more comprehensive physico-chemical and electrical interaction map of amino acids at the surface of the protein which are likely to be sites of interactions with other proteins and chemicals. In summary, computational tools offer simulation and analytical opportunities to characterize, identify and modify proteins with appropriate characteristics to catalyze the specific biochemical reaction that is required. In this review the enzyme butyrylcholinesterase, involved in the expression of metabolic syndrome is presented, and recently available methods to predict protein structure, function and active sites are summarized. Background Insulin resistance and type 2 diabetes are traditionally understood to result from a mismatch between insulin requirement and insulin supply [1]. Although essentially the concept still holds true, recent advances in biochemistry including bioinformatics are bringing to the fore newer players in the expression of these conditions [2]. While newer generation genomic methods have uncovered unsuspected proteins in the etiology, a long established protein, namely, butyrylcholinesterase is being studied in relation to the expression of insulin resistance and related conditions [3]. Butyrylcholinesterase (BChE) belongs to the esterase family of proteins, of which acetylcholinesterase is the most well known. The most well known role of BChE is the hydrolysis of succinylcholine, a muscle relaxant employed in general anesthesia. Individuals who had variant forms of the enzyme developed prolonged apnea with succinylcholine, and the enzyme was studied principally in terms of pharmacogenetics [3]. However, other roles for the enzyme were also studied. Evidence were presented accounting for its relation to dietary fat intake, obesity, insulin resistance, type 2 diabetes mellitus and Alzheimer’s disease [4]. Its role as a treatment for cocaine toxicity was also evaluated. Recent studies are bringing up the intriguing possibility of studying individuals harboring variant forms of the enzyme for their health outcomes [5,6]. Thus BChE is a protein in search of functions and may be considered as a prototype for the rapid identification of proteins by newer technologies. Therefore newer methods of assessing their structure and function are being developed. Bioinformatics tools are increasingly being used in this endeavour. This review provides an overview of bioinformatics and computational biological tools used in the characterization of such proteins. It is well known that enzymes are protein molecules which perform a variety of cellular and regulatory functions by interacting with ligands at specific locations called ‘active sites.’ (Figure 1). The active site depends on a variety of factors, ranging from the sequence of amino acids (primary structure) to arrangement of protein chain in space and time (secondary, tertiary and quaternary structures). Interest in the structure and evolution of proteins in a broader perspective or ‘protein space from a global perspective’ [7] is gaining prominence. A network analysis of evolutionary path provides information about protein evolution and thereby development of proteins with unique properties. The genome sequencing methods resulted in identification of large number of linear amino acid sequences without knowing the biological role of these proteins [8]. The active site is dynamic with interactions occurring with a variety of physical and chemical factors in the surroundings. With more data flooding than can be annotated, computational methods are employed in this area [9]. Predicting the

Modulator of Diabetes and Metabolic Syndrome: Silent Proteins, newer Insights