Ω-Loop mutations control dynamics of the active site by modulating the hydrogen-bonding network in PDC-3 β-lactamase
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eLife Assessment
This manuscript uses simulations to describe the dynamics of the Pseudomonas-derived cephalosporinase PDC-3 β-lactamase and its mutants to better understand antibiotic resistance. The finding that clinically observed mutations alter the flexibility of the Ω- and R2-loops, reshaping the cavity of the active site, is useful to the field. However, the evidence is considered incomplete; there is a lack of description of methods, and there is a need for additional analysis to demonstrate statistical significance, visualisation of the Markov states, analysis to explain changes due to the different mutations, and possible simulations in the presence of substrates to shed direct light on modulation mechanisms.
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Abstract
Abstract
The expression of antibiotic-inactivating enzymes, such as Pseudomonas-derived cephalosporinase-3 (PDC-3), is a major mechanism of intrinsic resistance in bacteria. Using reinforcement learning-driven molecular dynamics simulations and constant pH MD, we investigate how clinically observed mutations in the Ω-loop (at amino acids V211, G214, E219, and Y221) alter the structure and function of PDC-3. Our findings reveal that these substitutions modulate the dynamic flexibility of the Ω-loop and the R2-loop, reshaping the cavity of the active site. In particular, E219K and Y221A disrupt the tridentate hydrogen bond network around K67, thus lowering its pKa and promoting proton transfer to the catalytic residue S64. Markov state models reveal that E219K achieves enhanced catalysis by adopting stable, long-lived ‘active’ conformations, whereas Y221A facilitates activity by rapidly toggling between bond-formed and bond-broken states. In addition, substitutions influence key hydrogen bonds that control the opening and closure of the active-site pocket, consequently influencing the overall size. The pocket expands in all nine clinically identified variants, creating additional space to accommodate bulkier R1 and R2 cephalosporin side chains. Taken together, these results provide a mechanistic basis for how single residue substitutions in the Ω-loop affect catalytic activity. Insights into the structural dynamics of the catalytic site advance our understanding of emerging β-lactamase variants and can inform the rational design of novel inhibitors to combat drug-resistant P. aeruginosa.
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eLife Assessment
This manuscript uses simulations to describe the dynamics of the Pseudomonas-derived cephalosporinase PDC-3 β-lactamase and its mutants to better understand antibiotic resistance. The finding that clinically observed mutations alter the flexibility of the Ω- and R2-loops, reshaping the cavity of the active site, is useful to the field. However, the evidence is considered incomplete; there is a lack of description of methods, and there is a need for additional analysis to demonstrate statistical significance, visualisation of the Markov states, analysis to explain changes due to the different mutations, and possible simulations in the presence of substrates to shed direct light on modulation mechanisms.
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Reviewer #1 (Public review):
Summary:
This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.
Strengths:
The authors simulate an array of variants and perform numerous analyses to support their conclusions.
The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.
Weaknesses:
I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).
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Reviewer #1 (Public review):
Summary:
This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.
Strengths:
The authors simulate an array of variants and perform numerous analyses to support their conclusions.
The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.
Weaknesses:
I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).
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