Abstract
BackgroundMannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs.ResultsThis paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/).ConclusionsCompositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.
Highlights
Carbohydrates are important component of life, they are known as third molecular chain of life, after DNA and proteins [1]
We got 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity
Mannose binding site is defined as the site present on the surface of protein, where mannose atoms interact with the amino acids of protein within a distance-cutoff of 4 Au. Sequences of these 120 mannosebinding proteins with their Protein Databank (PDB) ID and chain name are available at http://www.imtech.res.in/raghava/premier/data.php, where MI Rs are in lowercase and non-mannose interacting residues (MIRs) are in uppercase
Summary
Carbohydrates are important component of life, they are known as third molecular chain of life, after DNA and proteins [1]. Limited number of methods has been developed to identify residues in proteins that interact with carbohydrate covalently (glycosylation) or non-covalently (carbohydrate binding sites) [14,15,16,17,18,19,20,21]. Balaji et al (2004) developed a program COTRAN to predict galactose-binding site in known protein complexes and achieved 76% sensitivity [19]. Mannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. It is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs
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