The functionalities of proteins rely
on protein conformational
changes during many processes. Identification of the protein conformations
and capturing transitions among different conformations are important
but extremely challenging in both experiments and simulations. In
this work, we develop a machine learning based approach to identify
a reaction coordinate that accelerates the exploration of protein
conformational changes in molecular simulations. We implement our
approach to study the conformational changes of human NTHL1 during
DNA repair. Our results identified three distinct conformations: open
(stable), closed (unstable), and bundle (stable). The existence of
the bundle conformation can rationalize recent experimental observations.
Comparison with an NTHL1 mutant demonstrates that a closely packed
cluster of positively charged residues in the linker could be a factor
to search when screening for genetic abnormalities. Results will lead
to a better modulation of the DNA repair pathway to protect against
carcinogenesis.