Beta-lactamases are characterized by their ability to hydrolyze beta-lactam antibiotics. Two common beta-lactamases, TEM-1 and TEM-52, are compared to better understand the role of dynamics in this process. TEM-1 is active against penicillin and first generation cephalosporins, whereas three mutations confer a much broader set of extended spectrum activities to TEM-52. Combined with geometric simulation results, we employ molecular dynamics (MD) simulation to explore motions within these enzymes. We generated eight all-atom 500 ns MD simulations in explicit solvent for both TEM-1 and TEM-52 starting from a variety of different crystal structures. We quantify dynamical differences using various types of Principal Component Analysis (PCA). PCA is commonly used to reduce the high dimensional space describing protein motion to a much smaller subspace in order to characterize essential motions. An important challenge facing protein modelling is the identification of the functional dynamical modes that are often hidden within nonfunctional global dynamics. We investigate the ability of PCA to identify functional modes that discriminate among proteins exhibiting different substrate specificities. We find that traditional Cartesian PCA methods perform well in identifying different dynamics between different crystal structure starting points, but they are mostly unable to distinguish dynamical differences due to sequence variation. We are optimizing methods to uncover functional difference by focusing on subsets of residues and utilizing multiple PCA methods in combination with each other. Moreover, we apply clustering and machine learning methods to better integrate and evaluate the different types of PCA results. We have been able to detect dynamical differences in the TEM-1 and TEM-52 sequence through the projection of top PCA modes onto the conformational ensembles in several instances and we are optimizing the method to obtain a robust measure of statistical significance.
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