Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

Frontiers in Molecular Biosciences

Li Shen¹  Feng Wang¹  Hongyu Zhou¹  Peng Tao¹  Shouyi Wang²  Xinlei Wang³ 

[1] Department of Chemistry, Center for Scientific Computation, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX, United States;Department of Industrial, Manufacturing, and Systems Engineering, University of Texas at Arlington, Arlington, TX, United States;Department of Statistical Science, Southern Methodist University, Dallas, TX, United States;

关键词: TEM-1 β-lactamase;    functional binding modes;    structural analysis;    random forest classification;    machine learning;    molecular dynamics;   

DOI  :  10.3389/fmolb.2019.00047

来源: DOAJ

【 摘 要 】

TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-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 β-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 β-lactamases functional and evolutional studies in general.

【 授权许可】

Unknown