ivis Dimensionality Reduction Framework for Biomacromolecular Simulations

Abstract

Molecular dynamics (MD) simulations have been widely applied to study macro-molecules 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.

UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

“…The high degrees of freedom of protein molecules present challenges also referred to as the curse of dimensionality. To face this challenge, various dimensionality reduction methods have been applied to MD simulations under the assumption that a few degrees of freedom through coordinate projections could account for the majority of the protein functions. − The projections obtained can then be used as collective variables (CVs) to build a Markov state model (MSM). MSMs have been applied to identify protein functional states on the free-energy surface and to describe the transitions among them. − …”

Rheological Behavior of Polymer Nanocomposites Filled with Spherical Nanoparticles: Insights from Molecular Dynamics Simulation

“…16,42 To understand the mechanisms of the shear thinning behavior, the structural change of chains is examined with the . The chains' conformation is conveniently measured by calculating the mean-square end-to-end distance 19,23,[44][45][46] , which is higher than our simulated value.…”

Explore Protein Conformational Space With Variational Autoencoder

“…The correlation-based metrics have been widely applied in the comparison between dimensionality reduction methods for biomolecules ( Tian and Tao, 2020 ; Trozzi et al, 2021 ). They are used here for the encoder module to measure how well the information is preserved in the latent space.…”