Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost
Journal article, 2025
Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, an amino acid sequence is tailored to meet the required abundance of a proteome, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here we show that the amino acid sequence alone encodes over half of protein abundance variation across all domains of life, ranging from bacteria to mouse and human. With an attempt to go beyond predictions, we trained a manageable-size Transformer model to interpret latent factors predictive of protein abundances. Intuitively, the model's attention focused on the protein's structural features linked to stability and metabolic costs related to protein synthesis. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations which increase predicted abundance have significantly altered protein polarity and hydrophobicity, underscoring a connection between protein structural features and abundance. Through molecular dynamics simulations we revealed that abundance-enhancing mutations possibly contribute to protein thermostability by increasing rigidity, which occurs at a lower synthesis cost.
protein stability
protein engineering
language models
deep learning
protein sequence
protein expression
molecular dynamics
explainable machine learning
proteome
Author
Filip Buric
Chalmers, Life Sciences, Systems and Synthetic Biology
Sandra Viknander
Chalmers, Life Sciences, Systems and Synthetic Biology
Xiaozhi Fu
Chalmers, Life Sciences, Systems and Synthetic Biology
Oliver Lemke
Charité University Medicine Berlin
Oriol Gracia Carmona
Faculty of Life Sciences & Medicine
University College London (UCL)
Jan Zrimec
Chalmers, Life Sciences, Systems and Synthetic Biology
National Institute of Biology Ljubljana
Lukasz Szyrwiel
Charité University Medicine Berlin
Michael Mülleder
Charité University Medicine Berlin
M. Ralser
Charité University Medicine Berlin
Aleksej Zelezniak
Faculty of Life Sciences & Medicine
Chalmers, Life Sciences, Systems and Synthetic Biology
Vilnius University
Protein Science
0961-8368 (ISSN) 1469896x (eISSN)
Vol. 34 1 e5239MetaPlast: Exploiting microbial dark matter for engineering plastic degradation microbial system
Formas (2019-01403), 2020-01-01 -- 2023-12-31.
Using AI to unravel "DNA grammar" for synthetic biology applications
Swedish Research Council (VR) (2019-05356), 2020-01-01 -- 2024-12-31.
Subject Categories
Biochemistry and Molecular Biology
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
Chalmers e-Commons
DOI
10.1002/pro.5239
PubMed
39665261
Related datasets
URI: https://github.com/fburic/protein-mgem DOI: https://doi.org/10.5281/zenodo.8377126