The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases

Abstract

Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A β-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A β-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A β-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A β-lactamases.

Allosteric cooperation in β-lactam binding to a non-classical transpeptidase

“…Also, through network analysis, we identify the pathways of allosteric communications that flow from the S-pocket via YkuD flap and directly to the catalytic site via a hydrophobic core to modulate the β-lactam binding ( Figure 3C and Figure 3—figure supplement 3 ). The role of hydrophobic core residues in channelling allosteric communications to couple with catalysis has been observed in a β-lactam hydrolyzing class A β-lactamase ( Olehnovics et al, 2021 ). Our study has also led to identification of a new residue S351 in the catalytic site that can be modulated by allostery ( Figure 3 , Figure 3—figure supplement 2 ).…”

Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase

“…A CVAE-based unsupervised deep learning was implemented to resolve the dynamics of the L1 MBL. The CVAE model has been previously used successfully to study many multiple cases including protein folding(Bhowmik et al, 2018), enzyme dynamics (Akere et al, 2020; Romero et al, 2019), COVID-19 related molecular mechanisms (Cho et al, 2021) and more specifically β-lactamases (Olehnovics et al, 2021). The CVAE can identify subtle differences in conformations of 3D structures influenced by various local and global dynamics and is able to cluster them in different microstates (Yoginath et al, 2019).…”

“…Deep learning analysis of α3-β7 and β12-α5 interactions L1 MBL, like all other β-lactamases, comprises of a hydrophobic core, that is stabilized by packing interactions that prevent long distance motions during simulations (Galdadas et al, 2021(Galdadas et al, , 2018Olehnovics et al, 2021). This indicates that thermal vibrations at fast timescales may significantly obscure potentially important slow dynamics.…”

“…A 22 x 22 residue symmetric distance matrix, representing the Cα residues of α3-β7 (S150a-D160) and β12-α5 loops (S223-P238), was used to featurize the dynamics of the loops. A previously tested protocol comprising of 64 filters in each of the 4 convolutional layers, and no pooling layers maintained a deep representational space of trainable parameters in each hidden layer (Olehnovics et al, 2021).…”

“…COVID-19 related molecular mechanisms (Cho et al, 2021) and more specifically β-lactamases (Olehnovics et al, 2021). The CVAE can identify subtle differences in conformations of 3D structures influenced by various local and global dynamics and is able to cluster them in different microstates (Yoginath et al, 2019).…”