High oxalate level in blood and urine may cause oxalate-related disorders, particularly kidney stone disease. To unravel disease mechanisms, investigations of oxalate level and its binding proteins are required. However, the information on oxalate-binding proteins is limited due to a lack of appropriate tool for their investigations. Therefore, we have developed a freely accessible web-based tool, namely OxaBIND (https://www.stonemod.org/oxabind.php), to identify oxalate-binding site(s) in any proteins of interest. The prediction model was generated by recruiting all of the known oxalate-binding proteins with solid experimental evidence (from PubMed and RCSB Protein Data Bank). The potential oxalate-binding domains/motifs were predicted from these oxalate-binding proteins using PRATT tool and used to discriminate these known oxalate-binding proteins from the known non-oxalate-binding proteins. The best one, which provided highest fitness score, sensitivity and specificity, was then implemented to create the OxaBIND tool. After inputting protein identifier or sequence (which can be single or multiple), details of all the identified oxalate-binding site(s), if any, are presented in both textual and graphical formats. OxaBIND also provides theoretical three-dimensional (3D) structure of the protein with oxalate-binding site(s) being highlighted. This tool will be beneficial for future research on the oxalate-binding proteins, which play important roles in the oxalate-related disorders.
Keywords: Bioinformatics; Computational biology; Kidney stone; Model; Oxalate-binding protein; Prediction tool.
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