Enzyme-level molecular simulation-assisted screening of functional bacteria for rapid bioelectrochemical sensing

Area of Science:

• Environmental Science
• Biotechnology
• Computational Chemistry

Background:

• Biochemical oxygen demand (BOD) is a critical metric for assessing water quality, with current microbial fuel cell sensors facing limitations in response speed due to microbial electron transfer.
• Developing faster and more accurate BOD sensors is essential for effective water quality monitoring and pollution control.

Purpose of the Study:

• To propose and validate an integrated computational framework combining molecular dynamics (MD) simulations and molecular docking to enhance the performance of microbial fuel cell-based BOD sensors.
• To identify and select highly electroactive microorganisms for improved sensor efficiency.

Main Methods:

• Utilized molecular dynamics (MD) simulations and molecular docking to analyze the binding affinity and conformational stability of hexokinase enzymes with glucose.
• Investigated the mechanisms of electron generation and transfer within enzyme-substrate complexes.
• Integrated computational findings with experimental validation to assess sensor performance.

Main Results:

• Hexokinase enzymes formed stable complexes with glucose, exhibiting optimal binding affinity and conformational stability.
• The developed sensor demonstrated a significantly reduced response time of 10 minutes, a 50% improvement over previous methods.
• Achieved a detection range of 73.3–440.0 mg/L with a high correlation (R² = 0.987) between BOD concentration and sensor performance.

Conclusions:

• The integrated MD simulation and molecular docking framework effectively accelerates sensor response time and improves accuracy for BOD detection.
• This approach offers a novel strategy for optimizing microorganism selection at the enzyme level, paving the way for real-time water quality monitoring.