  Molecular dynamics (MD) simulations have been widely applied to study
macromolecules including proteins. However, high-dimensionality of the datasets
produced by simulations makes it difficult for thorough analysis, and further
hinders a deeper understanding of biomacromolecules. To gain more insights into
the protein structure-function relations, appropriate dimensionality reduction
methods are needed to project simulations onto low-dimensional spaces. Linear
dimensionality reduction methods, such as principal component analysis (PCA)
and time-structure based independent component analysis (t-ICA), could not
preserve sufficient structural information. Though better than linear methods,
nonlinear methods, such as t-distributed stochastic neighbor embedding (t-SNE),
still suffer from the limitations in avoiding system noise and keeping
inter-cluster relations. ivis is a novel deep learning-based dimensionality
reduction method originally developed for single-cell datasets. Here we applied
this framework for the study of light, oxygen and voltage (LOV) domain of
diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a). Compared with other
methods, ivis is shown to be superior in constructing Markov state model (MSM),
preserving information of both local and global distances and maintaining
similarity between high dimension and low dimension with the least information
loss. Moreover, ivis framework is capable of providing new prospective for
deciphering residue-level protein allostery through the feature weights in the
neural network. Overall, ivis is a promising member in the analysis toolbox for
proteins.
