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Distinct chemical environments in biomolecular condensates.
Kilgore, Henry R; Mikhael, Peter G; Overholt, Kalon J; Boija, Ann; Hannett, Nancy M; Van Dongen, Catherine; Lee, Tong Ihn; Chang, Young-Tae; Barzilay, Regina; Young, Richard A.
Affiliation
  • Kilgore HR; Whitehead Institute for Biomedical Research, Cambridge, MA, USA. hkilgore@wi.mit.edu.
  • Mikhael PG; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Overholt KJ; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Boija A; Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
  • Hannett NM; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Van Dongen C; Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
  • Lee TI; Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
  • Chang YT; Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
  • Barzilay R; Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
  • Young RA; Department of Chemistry, Pohang University of Science and Technology, Pohang, Republic of Korea.
Nat Chem Biol ; 20(3): 291-301, 2024 Mar.
Article in En | MEDLINE | ID: mdl-37770698
ABSTRACT
Diverse mechanisms have been described for selective enrichment of biomolecules in membrane-bound organelles, but less is known about mechanisms by which molecules are selectively incorporated into biomolecular assemblies such as condensates that lack surrounding membranes. The chemical environments within condensates may differ from those outside these bodies, and if these differed among various types of condensate, then the different solvation environments would provide a mechanism for selective distribution among these intracellular bodies. Here we use small molecule probes to show that different condensates have distinct chemical solvating properties and that selective partitioning of probes in condensates can be predicted with deep learning approaches. Our results demonstrate that different condensates harbor distinct chemical environments that influence the distribution of molecules, show that clues to condensate chemical grammar can be ascertained by machine learning and suggest approaches to facilitate development of small molecule therapeutics with optimal subcellular distribution and therapeutic benefit.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning / Biomolecular Condensates Language: En Journal: Nat Chem Biol Journal subject: BIOLOGIA / QUIMICA Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning / Biomolecular Condensates Language: En Journal: Nat Chem Biol Journal subject: BIOLOGIA / QUIMICA Year: 2024 Document type: Article Affiliation country: