Lipid discovery enabled by sequence statistics and machine learning

Insights

Researchers explored bacterial MprF enzyme functions, discovering new cationic lipids like lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG) and diglucosyl-diacylglycerol (Glc2-DAG) in various bacteria. This work advances our understanding of bacterial membrane modifications.

Area of Science:

  • Microbiology
  • Biochemistry
  • Molecular Biology

Background:

  • Bacterial membranes are crucial for cell survival and are shaped by evolutionary pressures.
  • The MprF enzyme covalently modifies membrane lipids, influencing membrane composition and properties.
  • Previous work identified Streptococcus agalactiae MprF synthesizing lysyl-phosphatidylglycerol (Lys-PG) and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG).

Purpose of the Study:

  • To investigate the occurrence of Lys-Glc-DAG in other MprF-containing bacteria.
  • To identify novel MprF products and understand MprF substrate specificity.
  • To model evolutionary constraints on MprF sequences using machine learning.

Main Methods:

  • Comparative sequence analysis of MprF proteins across bacterial species.
  • Machine learning, specifically restricted Boltzmann machines, to predict MprF substrate specificity.
  • Investigating MprF activity in various bacterial genera, including Streptococcus and Enterococcus.

Main Results:

  • Several streptococcal MprF enzymes were confirmed to synthesize Lys-Glc-DAG.
  • A novel MprF substrate, diglucosyl-diacylglycerol (Glc2-DAG), was identified in Enterococcus species.
  • An expanded range of bacteria utilizing MprF for glycolipid modification was discovered.

Conclusions:

  • MprF exhibits broader substrate specificity than previously known, including glycolipids.
  • Machine learning approaches are effective in predicting enzyme function and discovering novel substrates.
  • This study reveals new cationic lipids and expands the known functions of MprF in bacterial membrane biosynthesis.