TY - JOUR
T1 - Protein Design Using Physics Informed Neural Networks
AU - Omar, Sara Ibrahim
AU - Keasar, Chen
AU - Ben-Sasson, Ariel J.
AU - Haber, Eldad
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.
AB - The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.
KW - binary optimization
KW - physics-informed neural networks
KW - protein design
UR - http://www.scopus.com/inward/record.url?scp=85151111270&partnerID=8YFLogxK
U2 - 10.3390/biom13030457
DO - 10.3390/biom13030457
M3 - Article
C2 - 36979392
AN - SCOPUS:85151111270
SN - 2218-273X
VL - 13
JO - Biomolecules
JF - Biomolecules
IS - 3
M1 - 457
ER -