LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Screening membraneless organelle participants with machine-learning models that integrate multimodal features

Photo from wikipedia

Significance Proteins that undergo phase separation promote biomolecular condensate formation and play a significant role in many biological processes. We divided these proteins into two categories according to their underlying… Click to show full abstract

Significance Proteins that undergo phase separation promote biomolecular condensate formation and play a significant role in many biological processes. We divided these proteins into two categories according to their underlying driving force when forming condensates: self-assembling proteins, which interact with the same protein species, and partner-dependent proteins, which interact with different biomolecule species. Most of the current computational tools preferentially predict self-assembling proteins and perform poorly in screening partner-dependent proteins. We thus built machine-learning models to predict the two protein categories separately. Further validation on the condensate proteome revealed that partner-dependent proteins are widespread in cells. We also developed a web server that integrates multiple phase-separation predictors, providing a convenient way for biologists to discover candidate phase-separating proteins.

Keywords: partner dependent; machine learning; dependent proteins; learning models; screening membraneless

Journal Title: Proceedings of the National Academy of Sciences of the United States of America
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.