Computational Enzymology Scientist: Enzyme Design

Bazeta; Cambridge (MA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Dayhoff Labs in Cambridge, MA seeks a Research Scientist in Computational Enzymology to simulate enzyme-catalysed reactions from physics up. You’ll map reaction pathways in the active site with QM/MM and free-energy methods, identify transition states, barriers, and residues driving catalysis.

You’ll collaborate with the ML and wet-lab teams, turning results into testable proposals and feeding data back into models while assessing method choices and computational cost.

Qualifications

• Expertise in enzymatic reaction modelling using QM/MM and free energy methods.
• Strong grasp of enzyme catalysis: mechanism, kinetics, cofactors, and active-site chemistry.
• Good understanding of methodological limitations and computational cost.
• Experience in integrating physics-based modelling with machine learning.
• Ability to design informed wet-lab experiments based on simulations.

Responsibilities

• Characterise mechanisms, transition states, and activation barriers in the active site.
• Pin down origins of catalysis and how active-site motion shapes the reaction using MD.
• Turn results into concrete, testable proposals for the wet lab and feed data back into models.
• Collaborate with physics-based and learned methods to strengthen each other.

Skills

QM/MM modelling

MD simulations

Free energy methods

Enzymatic catalysis knowledge

method selection

experimental collaboration

Tools

QM/MM tools

Molecular Dynamics

Free energy calculation tools

Reactive ML potentials

Job description

Dayhoff Labs in Cambridge, MA seeks a Research Scientist in Computational Enzymology to simulate enzyme-catalysed reactions from physics up. You’ll map reaction pathways in the active site with QM/MM and free-energy methods, identify transition states, barriers, and residues driving catalysis.

You’ll collaborate with the ML and wet-lab teams, turning results into testable proposals and feeding data back into models while assessing method choices and computational cost.