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Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes.

ABSTRACT: Recent single-molecule experiments have observed transition paths, i.e., brief events where molecules (particularly biomolecules) are caught in the act of surmounting activation barriers. Such measurements offer unprecedented mechanistic insights into the dynamics of biomolecular folding and binding, molecular machines, and biological membrane channels. A key challenge to these studies is to infer the complex details of the multidimensional energy landscape traversed by the transition paths from inherently low-dimensional experimental signals. A common minimalist model attempting to do so is that of one-dimensional diffusion along a reaction coordinate, yet its validity has been called into question. Here, we show that the distribution of the transition path time, which is a common experimental observable, can be used to differentiate between the dynamics described by models of one-dimensional diffusion from the dynamics in which multidimensionality is essential. Specifically, we prove that the coefficient of variation obtained from this distribution cannot possibly exceed 1 for any one-dimensional diffusive model, no matter how rugged its underlying free energy landscape is: In other words, this distribution cannot be broader than the single-exponential one. Thus, a coefficient of variation exceeding 1 is a fingerprint of multidimensional dynamics. Analysis of transition paths in atomistic simulations of proteins shows that this coefficient often exceeds 1, signifying essential multidimensionality of those systems.

SUBMITTER: Satija R

PROVIDER: S-EPMC7959536 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Hydrothermal systems are excellent natural laboratories for the study of how chemical energy landscapes shape microbial communities. Yet, only a few attempts have been made to quantify relationships between energy availability and microbial community structure in these systems. Here, we have investigated how microbial communities and chemical energy availabilities vary along cross-sections of two hydrothermal chimneys from the Soria Moria Vent Field and the Bruse Vent Field. Both vent fields are located on the Arctic Mid-Ocean Ridge, north of the Jan Mayen Island and the investigated chimneys were venting fluids with markedly different H2S:CH4 ratios. Energy landscapes were inferred from a stepwise in silico mixing of hydrothermal fluids (HFs) with seawater, where Gibbs energies of relevant redox-reactions were calculated at each step. These calculations formed the basis for simulations of relative abundances of primary producers in microbial communities. The simulations were compared with an analysis of 24 samples from chimney wall transects by sequencing of 16S rRNA gene amplicons using 454 sequencing. Patterns in relative abundances of sulfide oxidizing Epsilonproteobacteria and methane oxidizing Methylococcales and ANME-1, were consistent with simulations. However, even though H2 was present in HFs from both chimneys, the observed abundances of putative hydrogen oxidizing anaerobic sulfate reducers (Archaeoglobales) and methanogens (Methanococcales) in the inner parts of the Soria Moria Chimney were considerably higher than predicted by simulations. This indicates biogenic production of H2 in the chimney wall by fermentation, and suggests that biological activity inside the chimneys may modulate energy landscapes significantly. Our results are consistent with the notion that energy landscapes largely shape the distribution of primary producers in hydrothermal systems. Our study demonstrates how a combination of modeling and field observations can be useful in deciphering connections between chemical energy landscapes and metabolic networks within microbial communities.

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Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes.

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