School of Natural and Computing Sciences, Institute of Mathematics, University of Aberdeen, Fraser Noble Building, Aberdeen, AB24 3UE, UK.
Programa de Pós-graduação em Ecologia e Conservação, Universidade Federal do Paraná, Curitiba, 82590-300, Brazil.
Sci Rep. 2024 Oct 26;14(1):25544. doi: 10.1038/s41598-024-77319-4.
Amino acids are the building blocks of proteins and enzymes which are essential for life. Understanding amino acid usage offers insights into protein function and molecular mechanisms underlying life histories. However, genome-wide patterns of amino acid usage across domains of life remain poorly understood. Here, we analysed the proteomes of 5590 species across four domains and found that only a few amino acids are consistently the most and least used. This differential usage results in lower amino acid usage diversity at the most and least frequent ranks, creating a ubiquitous inverted U-shape pattern of amino acid diversity and rank which we call an 'edge effect' across proteomes and domains of life. This effect likely stems from protein secondary structural constraints, not the evolutionary chronology of amino acid incorporation into the genetic code, highlighting the functional rather than evolutionary influences on amino acid usage. We also tested other contemporary hypotheses regarding amino acid usage in proteomes and found that amino acid usage varies across life's domains and is only weakly influenced by growth temperature. Our findings reveal a novel and pervasive amino acid usage pattern across genomes with the potential to help us probe deep evolutionary relationships and advance synthetic biology.
氨基酸是蛋白质和酶的组成部分,而蛋白质和酶对生命是必不可少的。了解氨基酸的使用情况可以深入了解蛋白质的功能和生命历史背后的分子机制。然而,生命领域中氨基酸使用的全基因组模式仍未得到很好的理解。在这里,我们分析了跨越四个领域的 5590 个物种的蛋白质组,发现只有少数氨基酸始终是使用最多和最少的。这种差异使用导致在最频繁和最不频繁的等级上的氨基酸使用多样性降低,在蛋白质组和生命领域中创建了普遍的氨基酸多样性和等级的倒 U 形模式,我们称之为“边缘效应”。这种效应可能源于蛋白质二级结构的限制,而不是氨基酸纳入遗传密码的进化时间顺序,突出了对氨基酸使用的功能而不是进化影响。我们还测试了蛋白质组中关于氨基酸使用的其他当代假设,发现氨基酸的使用在生命领域中有所不同,并且仅受生长温度的微弱影响。我们的研究结果揭示了一种新颖而普遍的氨基酸使用模式,跨越基因组,具有帮助我们深入探究深层进化关系和推进合成生物学的潜力。