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Machine Learning Based Dimensionality Reduction Facilitates Ligand Diffusion Paths Assessment: A Case of Cytochrome P450cam
journal contribution
posted on 2016-03-18, 00:00 authored by J. Rydzewski, W. NowakIn
this work we propose an application of a nonlinear dimensionality
reduction method to represent the high-dimensional configuration space
of the ligand–protein dissociation process in a manner facilitating
interpretation. Rugged ligand expulsion paths are mapped into 2-dimensional
space. The mapping retains the main structural changes occurring during
the dissociation. The topological similarity of the reduced paths
may be easily studied using the Fréchet distances, and we show
that this measure facilitates machine learning classification of the
diffusion pathways. Further, low-dimensional configuration space allows
for identification of residues active in transport during the ligand
diffusion from a protein. The utility of this approach is illustrated
by examination of the configuration space of cytochrome P450cam involved
in expulsing camphor by means of enhanced all-atom molecular dynamics
simulations. The expulsion trajectories are sampled and constructed
on-the-fly during molecular dynamics simulations using the recently
developed memetic algorithms [Rydzewski,
J.; Nowak, W. J. Chem. Phys. 2015, 143 (12), 124101]. We show that the memetic algorithms are effective for enforcing
the ligand diffusion and cavity exploration in the P450cam–camphor
complex. Furthermore, we demonstrate that machine learning techniques
are helpful in inspecting ligand diffusion landscapes and provide
useful tools to examine structural changes accompanying rare events.