Role of active site rigidity in activity: MD simulation and fluorescence study on a lipase mutant.

Relationship between stability and activity of enzymes is maintained by underlying conformational flexibility. In thermophilic enzymes, a decrease in flexibility causes low enzyme activity while in less stable proteins such as mesophiles and psychrophiles, an increase in flexibility is associated with enhanced enzyme activity. Recently, we identified a mutant of a lipase whose stability and activity were enhanced simultaneously. In this work, we probed the conformational dynamics of the mutant and the wild type lipase, particularly flexibility of their active site using molecular dynamic simulations and time-resolved fluorescence techniques. In contrast to the earlier observations, our data show that active site of the mutant is more rigid than wild type enzyme. Further investigation suggests that this lipase needs minimal reorganization/flexibility of active site residues during its catalytic cycle. Molecular dynamic simulations suggest that catalytically competent active site geometry of the mutant is relatively more preserved than wild type lipase, which might have led to its higher enzyme activity. Our study implies that widely accepted positive correlation between conformation flexibility and enzyme activity need not be stringent and draws attention to the possibility that high enzyme activity can still be accomplished in a rigid active site and stable protein structures. This finding has a significant implication towards better understanding of involvement of dynamic motions in enzyme catalysis and enzyme engineering through mutations in active site.

A hierarchical approach to protein fold prediction.

Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes.

SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids.

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-ß, α/ß and α + ß. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.