title: Enzyme clustering accelerates processing of intermediates through metabolic channeling.
abstract: We present a quantitative model to demonstrate that coclustering multiple enzymes into compact agglomerates accelerates the processing of intermediates, yielding the same efficiency benefits as direct channeling, a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation.
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
authors.forename: Michele
authors.initials: M
authors.lastname: Castellana
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Maxwell Z
authors.initials: MZ
authors.lastname: Wilson
authors.affiliation: 1] Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Yifan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Preeti
authors.initials: P
authors.lastname: Joshi
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Ileana M
authors.initials: IM
authors.lastname: Cristea
authors.affiliation: 1] Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. [2] Department of Chemistry, Princeton University, Princeton, New Jersey, USA.
authors.forename: Joshua D
authors.initials: JD
authors.lastname: Rabinowitz
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Zemer
authors.initials: Z
authors.lastname: Gitai
authors.affiliation: 1] Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA. [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Ned S
authors.initials: NS
authors.lastname: Wingreen
mesh_terms: D001120:Arginine / Q000378:metabolism; D019295:Computational Biology; D004798:Enzymes / Q000378:metabolism / Q000502:physiology; D004926:Escherichia coli / Q000201:enzymology / Q000378:metabolism / Q000502:physiology; D029968:Escherichia coli Proteins / Q000378:metabolism / Q000502:physiology; D006706:Homeostasis; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological; D011743:Pyrimidines / Q000378:metabolism
chemical_list: D004798:Enzymes; D029968:Escherichia coli Proteins; D011743:Pyrimidines; D001120:Arginine; C030986:pyrimidine