Deep clustering of protein folding simulations

作     者：Bhowmik, Debsindhu Gao, Shang Young, Michael T. Ramanathan, Arvind 

作者机构：Oak Ridge Natl Lab Computat Sci & Engn Div One Bethel Valley RdMS6085 Oak Ridge TN 37830 USA 

出 版 物：《BMC BIOINFORMATICS》 (英国医学委员会:生物信息)

年 卷 期：2018年第19卷第Sup18期

页      面：47-58页

核心收录：

学科分类：0710[理学-生物学] 0836[工学-生物工程] 10[医学] 

基　　金：Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program Laboratory Director's Research and Development Fund National Cancer Institute (NCI) of the National Institutes of Health 

主　　题：Deep learning Variational autoencoder Protein folding Conformational substates 

摘      要：BackgroundWe examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological *** use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 s aggregate sampling), villin head piece (single trajectory of 125 s) and - - (BBA) protein (223 + 102 s sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural ***, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.