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 the biological system . To gain more insights into the protein structure-function relations, appropriate dimensionality reduction methods are needed to project simulations to low-dimensional spaces. Linear dimensionality reduction methods, such as principal component analysis (PCA) and time-structure based independent component analysis (t-ICA), fail to preserve enough 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. Here, we applied the ivis framework as a novel machine learning based dimensionality reduction method originally developed for single-cell datasets for analysis of macromolecular simulations . Compared with other methods, ivis is  superior in constructing Markov state model (MSM), preserving global distance and maintaining similarity between high dimension and low dimension with the least information loss. Moreover, the neuron weights in the hidden layer of supervised ivis framework provide new prospective for deciphering the allosteric process of proteins . Overall, ivis is a promising member in the analysis toolbox for proteins.
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.
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the biological system
biomacromolecules
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to
onto
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fail to preserve enough
could not preserve sufficient
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Here, we applied the ivis framework as a novel machine learning based
ivis is a novel deep learning-based
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for analysis of macromolecular simulations
. Here we applied this framework for the study of light, oxygen and voltage (LOV) domain of diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a)
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shown to be
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global distance
information of both local and global distances
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the neuron weights in the hidden layer of supervised ivis framework provide
ivis framework is capable of providing
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the allosteric process of proteins
residue-level protein allostery through the feature weights in the neural network