Microscopic Origins\nof Flow Activation Energy in Biomolecular\nCondensates

The material properties of biomolecular condensates govern\ntheir\ndynamics and functions by influencing the molecular diffusion rates\nand biochemical interactions. A recent report has identified a characteristic\ntimescale of temperature-dependent viscosity in biomolecular condensates\narising from an activated dissociation events collectively referred\nto as flow activation energy. The microscopic origin of this activation\nenergy is a complex function of sequence, stoichiometry, and external\nconditions. In this study, we elucidate the microscopic origins of\nflow activation energy in single and multicomponent condensates formed\nfrom model peptide sequences with varying “sticker”\nand “spacer” motifs, incorporating RNA as a secondary\ncomponent. We examined how condensate density, RNA stoichiometry,\nand peptide sequence patterning impact these properties through detailed\nsequence-resolved coarse-grained simulations. Our findings reveal\nthat flow activation energy is closely tied to the lifetime of sticker–sticker\ninteractions under specific conditions. However, the presence of multiple\ncompeting stickers may complicate this relationship leading to frustrated\ninteractions in condensates and lowering of activation energy. The\nfindings of this study should help to create predictive models of\nmaterial properties of condensates, which in turn can facilitate a\nmore profound understanding of functions and programmable design principles\nof biomolecular condensates.

Publication Details

Journal
Figshare
Published
2024-12-05
DOI
https://doi.org/10.1021/acs.jpcb.4c05834.s001
Primary Topic
Diffusion and Search Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Microscopic Origins\nof Flow Activation Energy in Biomolecular\nCondensates

Figshare
Diffusion and Search Dynamics
article

Microscopic Origins\nof Flow Activation Energy in Biomolecular\nCondensates

article en

Abstract

The material properties of biomolecular condensates govern\ntheir\ndynamics and functions by influencing the molecular diffusion rates\nand biochemical interactions. A recent report has identified a characteristic\ntimescale of temperature-dependent viscosity in biomolecular condensates\narising from an activated dissociation events collectively referred\nto as flow activation energy. The microscopic origin of this activation\nenergy is a complex function of sequence, stoichiometry, and external\nconditions. In this study, we elucidate the microscopic origins of\nflow activation energy in single and multicomponent condensates formed\nfrom model peptide sequences with varying “sticker”\nand “spacer” motifs, incorporating RNA as a secondary\ncomponent. We examined how condensate density, RNA stoichiometry,\nand peptide sequence patterning impact these properties through detailed\nsequence-resolved coarse-grained simulations. Our findings reveal\nthat flow activation energy is closely tied to the lifetime of sticker–sticker\ninteractions under specific conditions. However, the presence of multiple\ncompeting stickers may complicate this relationship leading to frustrated\ninteractions in condensates and lowering of activation energy. The\nfindings of this study should help to create predictive models of\nmaterial properties of condensates, which in turn can facilitate a\nmore profound understanding of functions and programmable design principles\nof biomolecular condensates.

Figshare
Affordable and clean energy
Openalex Percentile: Top 73%
Diffusion and Search Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.