Neural network‐derived Potts models for structure‐based protein design using backbone atomic coordinates and tertiary motifs
Name
Protein Science - 2022 - Li - Neural network‐derived Potts models for structure‐based protein design using backbone atomic.pdf
Description
Published version
Size
2.21 MB
Format
Adobe PDF
Checksum (MD5)
40e1826cdb179762c312bbbbfd7ba1b0
Author(s)
Li, Alex J
Lu, Mindren
Desta, Israel
Sundar, Vikram
Grigoryan, Gevorg
Keating, Amy E
Date Issued
February 2023
Journal
Protein Science
Publisher
Wiley
Citation
Li, Alex J, Lu, Mindren, Desta, Israel, Sundar, Vikram, Grigoryan, Gevorg et al. 2023. "Neural network‐derived Potts models for structure‐based protein design using backbone atomic coordinates and tertiary motifs." Protein Science, 32 (2).
Version
Final published version
Abstract
Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/pro.4554
