期刊
FRONTIERS IN MOLECULAR BIOSCIENCES
卷 6, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2019.00047
关键词
TEM-1 beta-lactamase; functional binding modes; structural analysis; random forest classification; machine learning; molecular dynamics
资金
- National Science Foundation under a CAREER Grant [1753167]
- SMU Dean's Research Council research grant
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1753167] Funding Source: National Science Foundation
TEM family of enzymes is one of the most commonly encountered beta-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM beta-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 beta-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for beta-lactamases functional and evolutional studies in general.
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