Yiğit Kutlu, Gabriel Axel, Rachel Kolodny, Nir Ben-Tal, Turkan Haliloglu
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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.</p>","PeriodicalId":18730,"journal":{"name":"Molecular biology and evolution","volume":" ","pages":""},"PeriodicalIF":11.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412252/pdf/","citationCount":"0","resultStr":"{\"title\":\"Reused Protein Segments Linked to Functional Dynamics.\",\"authors\":\"Yiğit Kutlu, Gabriel Axel, Rachel Kolodny, Nir Ben-Tal, Turkan Haliloglu\",\"doi\":\"10.1093/molbev/msae184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":18730,\"journal\":{\"name\":\"Molecular biology and evolution\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":11.0000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412252/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular biology and evolution\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/molbev/msae184\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular biology and evolution","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/molbev/msae184","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
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.
期刊介绍:
Molecular Biology and Evolution
Journal Overview:
Publishes research at the interface of molecular (including genomics) and evolutionary biology
Considers manuscripts containing patterns, processes, and predictions at all levels of organization: population, taxonomic, functional, and phenotypic
Interested in fundamental discoveries, new and improved methods, resources, technologies, and theories advancing evolutionary research
Publishes balanced reviews of recent developments in genome evolution and forward-looking perspectives suggesting future directions in molecular evolution applications.