Prediction of liquid-liquid phase separating proteins using machine learning.

Journal: BMC bioinformatics
Published Date: Feb 15, 2022

Abstract

BACKGROUND: The liquid-liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation of LLPS is implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS.

Authors

  • Xiaoquan Chu
    College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
  • Tanlin Sun
    Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
  • Qian Li
    Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Youjun Xu
    Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, ‡Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, and ¶Peking-Tsinghua Center for Life Sciences, Peking University , Beijing 100871, China.
  • Zhuqing Zhang
    College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China. zhuqingzhang@ucas.ac.cn.
  • Luhua Lai
    Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
  • Jianfeng Pei
    Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.

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