Inferring protein conformational ensemble using deep learning and evolutionary couplings

Protein conformational dynamics is crucial for understanding protein structure-function relationship, but hard to sample and analyze due to their high-dimensional nature. Evolutionary couplings (ECs) have been proven to contain information about the structure and function of proteins, thus can be used for predicting residue contact or functional effect of mutations. We here show that ECs can also be used to infer protein dynamics. We present dynamicEC, a deep learning model based on ResNet that takes multiple kinds of features to predict dynamic contact residue pairs. After being trained on a dataset of over 220000 experimental protein structures, our model achieved over 95\% in overall accuracy, and can achieve over 0.89 on the area under the receiver operating characteristic (AUROC) on the test set. By using the inter-residue distance between the predicted residue pairs to construct Markov State Models and computing GMRQ score, we further prove that the predicted residue pairs of our model can be used as reaction coordinates for efficient sampling of protein conformational landscape and analysis of molecular dynamics simulation. Our work has proven that the evolutional correlation between residues can provide not only information on static structures, but also dynamical insights of proteins. It can be used as a cheap and fast way to guide enhanced sampling with little prior knowledge required (just sequence information). The predicted residue pairs can serve as reaction coordinates and be used for MD data analysis such as dimensionality reduction and understanding the slow motions of proteins. Overall, our work provides a data-driven method for characterizing the conformational ensemble of protein from sequential data using deep learning.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Biophysics & Quant Biology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Xue, Zhengyuan

Contributors dc:contributor

• Shukla, Diwakar

• Protein Conformational Dynamics
• Deep Learning

Statement dc:rights

• Copyright 2022 Zhengyuan Xue

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115600

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115600