Explore Protein Conformational Space With Variational Autoencoder

Abstract

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

“…MDS in the context of MD was also described by Troyer and Cohen (1995) , Andrecut (2009) , Tribello and Gasparotto (2019) , and Srivastava et al (2020) . There are also examples of the application of other approaches: isometric feature mapping ( Stamati et al, 2010 ), kernel PCA ( Antoniou and Schwartz, 2011 ), diffusion map ( Rohrdanz et al, 2011 ; Zheng et al, 2011 ; Zheng et al, 2013a ; Zheng et al, 2013b ; Preto and Clementi, 2014 ), t-SNE ( Zhou et al, 2018 ; Zhou et al, 2019 ; Spiwok and Kříž, 2020 ), and VAE ( Hernández et al, 2018 ; Shamsi et al, 2018 ; Moritsugu, 2021 ; Tian et al, 2021 ).…”

“…For example, a number of DL-based, approaches have already been proposed, such as variational autoencoders (VAEs), which significantly increases sampling “power”, if combined with MD potential. Tian et al (2021) demonstrated successful protein sampling with VAEs on the example of adenosine kinase (ADK) conformational change from its closed state to the open one. Decoded conformations were similar to the training ones.…”

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction

“…In the remainder of this subsection, we show the details of the CNN structures of these two nonlinear mappings G k and G k and how to make the implicit residual update (7) explicit by leveraging the robust shrinkage selection operator.…”

“…Combining the parametrized nonlinear operators G k and G k and the shrinkage operator S k into (8), we obtain an explicit update rule of (7) given by…”

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

“…The past decade has witnessed the rapid development of machine learning in chemistry and biology ( Zhang et al 2020 ; Chen L. et al 2021 ; Tian et al 2020 ; Tian et al 2021b ; Tian et al 2022 ). ML methods have been shown to be superior in the classification of protein allosteric pockets.…”