Vancomycin is a glycopeptide antibiotic used for the treatment of serious infections by Gram-positive pathogens. Vancomycin inhibits cell wall biosynthesis by targeting the d-Ala-d-Ala terminus of peptidoglycan (PG). The highly cross-linked heptapeptide aglycon structure of vancomycin is the d-Ala-d-Ala binding site. The first residue of vancomycin is N-methyl-leucine, which is crucial for the dipeptide binding. The removal of N-methyl-leucine by Edman degradation results in desleucyl-vancomycin devoid of antimicrobial activities. To investigate the function of N-methyl-leucine for the dipeptide binding in vancomycin, molecular dynamics simulations of vancomycin and three N-terminus-modified vancomycin derivatives: desleucyl-vancomycin, vancomycin_NtoC, and vancomycin_Sar, binding to a PG unit of the sequence l-Ala-d-iso-Gln-l-Lys-d-Ala-d-Ala with an intact pentaglycine bridge structure attached to the bridge link of l-Lys were carried out. Glycopeptide–PG binding interactions were characterized by root-mean-square-deviation contour analysis of atomic positions in vancomycin and its three analogues bound to a PG unit. The overall sampling space for four glycopeptide–PG complexes shows four distinct distributions with a continuous change between the conformational spaces. The hydrogen bond analyses show that multiple hydrogen bonds between the d-Ala-d-Ala and the vancomycin aglycon structure strengthened the dipeptide binding. The simulations revealed that the removal or chemical modification of N-methyl-leucine significantly weakens the dipeptide binding to the aglycon structure and provides interesting structural insights into glycopeptide–PG binding interactions.

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.