Reused Protein Segments Linked to Functional Dynamics

Protein space is characterized by extensive recurrence, or "reuse," of parts, suggesting that new proteins and domains can evolve by mixing-and-matching of existing segments. From an evolutionary perspective, for a given combination to persist, the protein segments should presumably not only match geometrically but also dynamically communicate with each other to allow concerted motions that are key to function. Evidence from protein space supports the premise that domains indeed combine in this manner; we explore whether a similar phenomenon can be observed at the sub-domain level. To this end, we use Gaussian Network Models (GNMs) to calculate the so-called soft modes, or low-frequency modes of motion for a dataset of 150 protein domains. Modes of motion can be used to decompose a domain into segments of consecutive amino acids that we call "dynamic elements", each of which belongs to one of two parts that move in opposite senses. We find that, in many cases, the dynamic elements, detected based on GNM analysis, correspond to established "themes": Sub-domain-level segments that have been shown to recur in protein space, and which were detected in previous research using sequence similarity alone (i.e. completely independently of the GNM analysis). This statistically significant correlation hints at the importance of dynamics in evolution. Overall, the results are consistent with an evolutionary scenario where proteins have emerged from themes that need to match each other both geometrically and dynamically, e.g. to facilitate allosteric regulation.

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Publication Details

Journal
Molecular Biology and Evolution
Published
2024-09-01
DOI
https://doi.org/10.1093/molbev/msae184
Citations
3
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
0.70

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article

Reused Protein Segments Linked to Functional Dynamics

Nir Ben‐Tal, Türkan Haliloǧlu, Rachel Kolodny, Yiǧit Kutlu et al.
3 citations
Molecular Biology and Evolution
Protein Structure and Dynamics
0.70
article

Reused Protein Segments Linked to Functional Dynamics

Nir Ben‐Tal, Türkan Haliloǧlu, Rachel Kolodny, Yiǧit Kutlu, Gabriel Axel
article en
3 citations

Abstract

Protein space is characterized by extensive recurrence, or "reuse," of parts, suggesting that new proteins and domains can evolve by mixing-and-matching of existing segments. From an evolutionary perspective, for a given combination to persist, the protein segments should presumably not only match geometrically but also dynamically communicate with each other to allow concerted motions that are key to function. Evidence from protein space supports the premise that domains indeed combine in this manner; we explore whether a similar phenomenon can be observed at the sub-domain level. To this end, we use Gaussian Network Models (GNMs) to calculate the so-called soft modes, or low-frequency modes of motion for a dataset of 150 protein domains. Modes of motion can be used to decompose a domain into segments of consecutive amino acids that we call "dynamic elements", each of which belongs to one of two parts that move in opposite senses. We find that, in many cases, the dynamic elements, detected based on GNM analysis, correspond to established "themes": Sub-domain-level segments that have been shown to recur in protein space, and which were detected in previous research using sequence similarity alone (i.e. completely independently of the GNM analysis). This statistically significant correlation hints at the importance of dynamics in evolution. Overall, the results are consistent with an evolutionary scenario where proteins have emerged from themes that need to match each other both geometrically and dynamically, e.g. to facilitate allosteric regulation.

Molecular Biology and EvolutionVol. 41(9)
Tel Aviv University (IL), Boğaziçi University (TR), University of Haifa (IL)
Minerva Foundation, Israel Science Foundation, Tel Aviv University, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Openalex Percentile: Top 30%
Protein Structure and Dynamics
0.70
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