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PreprintCited by 12Protein Structure and DynamicsRNA and protein synthesis mechanismsMachine Learning in Bioinformatics

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The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost

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Buric, Filip, Viknander, Sandra, Fu, Xiaozhi, Lemke, Oliver, Zrimec, Jan, Szyrwiel, Lukasz, Mueleder, Michael, Ralser, Markus, Zelezniak, Aleksej·2023·Protein Science
DOI 10.1101/2023.10.02.560091PMC PMC11635393

Preprint — published as 10.1002/pro.5239

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Abstract

Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, the amino acid sequence is tailored to meet the required abundance of proteomes, 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 demonstrate that the amino acid sequence predicts abundance by shaping a protein’s conformational stability. We show that increasing the abundance provides metabolic cost benefits, underscoring the evolutionary advantage of maintaining a highly abundant and stable proteome. Specifically, using a deep learning model (BERT), we predict 56% of protein abundance variation in Saccharomyces cerevisiae solely based on amino acid sequence. The model reveals latent factors linking sequence features to protein stability. 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 increasing abundance significantly alter protein polarity and hydrophobicity, underscoring a connection between protein stability and abundance. Through molecular dynamics simulations and in vivo experiments in yeast, we confirm that abundance-enhancing mutations result in longer-lasting and more stable protein expression. Importantly, these sequence changes also reduce metabolic costs of protein synthesis, elucidating the evolutionary advantage of cost-effective, high-abundance, stable proteomes. Our findings support the role of amino acid sequence as a pivotal determinant of protein abundance and stability, revealing an evolutionary optimization for metabolic efficiency.

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Metadata

Authors
Buric, F., Viknander, S., Fu, X., Lemke, O., Zrimec, J., Szyrwiel, L., Mueleder, M., Ralser, M., & Zelezniak, A.
Affiliations
  • Chalmers University of Technology (SE)
  • Charité – Universitätsmedizin Berlin (DE)
  • National Institute of Biology (SI)
  • Vilnius University (LT)
Corresponding author
Zelezniak, Aleksej
ORCID
Venue
Protein Science
Publisher
openRxiv
Published
2023-10-04
Volume
34
Issue
1
Pages
e5239-e5239
ISSN
0961-8368 (ISSN-L)
DOI
10.1101/2023.10.02.560091
PMC
PMC11635393
OpenAlex
W4405330338
Cited by
12
References
1
Citations by year
2026: 4 · 2025: 4 · 2024: 3 · 2023: 1
FWCI
1.49
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Protein Science (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DiRROS repository (University of Maribor) (submittedVersion, cc-by)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders
  • Svenska Forskningsrådet Formas (2018‐05973, 2022–06725, 2019-01403)
  • National Science Council (2018-05973)
  • Chalmers Tekniska Högskola
  • Knut och Alice Wallenbergs Stiftelse (2018-05973, 2021.0198)
  • Vetenskapsrådet (05973, 2019-01403, 2018-, 2019-05356, 201805973, 2022-06725, No. 2018-05973)
  • Science for Life Laboratory (2019-01403)
  • National Supercomputer Centre, Linköpings Universitet (2018–05973, 2022-06725)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
protein abundance, amino acid sequence, protein stability, sequence-structure-function relationships, transformer attention, protein polarity, protein hydrophobicity, protein thermal stability, protein rigidity, molecular dynamics simulation
MeSH terms
Amino Acid Sequence, Animals, Humans, Mutation, Proteins, Protein Biosynthesis, Mice, Protein Stability, Molecular Dynamics Simulation, Hydrophobic and Hydrophilic Interactions

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