Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling
Name
1.5133026.pdf
Description
Published version
Size
2.8 MB
Format
Adobe PDF
Checksum (MD5)
b94633dcb686c15f79ff79786cd2ce83
Author(s)
Yu, Chi Hua
Buehler, Markus J
Date Issued
March 2020
Journal
APL Bioengineering
Publisher
AIP Publishing
Citation
Yu, Chi-Hua, and Markus J. Buehler. “Sonification Based de Novo Protein Design Using Artificial Intelligence, Structure Prediction, and Analysis Using Molecular Modeling.” APL Bioengineering 4, 1 (March 2020): 016108. © 2020 Authors
Version
Final published version
Abstract
We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1063/1.5133026
