Lipases are enzymes that hydrolyze the ester bond between acyl groups and glycerol in triacylglycerides which gives the products of glycerol and fatty acids. Bacillus thermocatenulatus lipase (BTL2) has shown highest activity toward tributyrin (C4) as substrate. While broad selectivity on the chain length of the fatty acids has a key role in waste water treatment, and laundry formulations; short chain length specificity can be used in the food and cosmetic industry. In order to predict its chain length substrate specificity (tributyrin (C4)/tricaprylin (C8)) upon mutation, we developed a scoring function which combines in silico docking and molecular dynamics tools. After calibration on experimentally validated mutants, our scoring function is able to discriminate substrates specificities and predict the impact of a mutation (whether it enhances or reduces) in a rapid and accurate manner (overall correlation r=0.7930, p=0.0007). Also ranking of substrate specificities within the mutants were 100% correct. This method can be powerfully adapted to other protein families to predict the effect of a mutation for the one specific substrate or multiple substrates
