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Evolutionary tinkering enriches the hierarchical and nested structures in amino acid sequences

Zecheng Zhang, Chunxiuzi Liu, Yingjun Zhu, Lü Peng, Weiyi Qiu, Qian-Yuan Tang, Liu He, Ke Zhang, Zengru Di, Yu Liu

2024Physical Review Research11 citationsDOIOpen Access PDF

Abstract

Genetic information often exhibits hierarchical and nested relationships, achieved through the reuse of repetitive subsequences such as duplicons and transposable elements, a concept termed “evolutionary tinkering” by François Jacob. Current bioinformatics tools often struggle to capture these, particularly the nested, relationships. To address this, we utilized ladderpath, an approach within the broader category of algorithmic information theory, introducing two key measures: order rate <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mi>η</a:mi></a:math> for characterizing sequence pattern repetitions and regularities, and ladderpath-complexity <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:mi>κ</b:mi></b:math> for assessing hierarchical and nested richness. Our analysis of amino acid sequences revealed that humans have more sequences with higher <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"><c:mi>κ</c:mi></c:math> values, and proteins with many intrinsically disordered regions exhibit increased <d:math xmlns:d="http://www.w3.org/1998/Math/MathML"><d:mi>η</d:mi></d:math> values. Additionally, it was found that extremely long sequences with low <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"><e:mi>η</e:mi></e:math> are rare. We hypothesize that this arises from varied duplication and mutation frequencies across different evolutionary stages, which in turn suggests a zigzag pattern for the evolution of protein complexity. This is supported by simulations and studies of protein families such as ubiquitin and NBPF, implying species-specific or environment-influenced protein elongation strategies. The ladderpath approach offers a quantitative lens to understand evolutionary tinkering and reuse, shedding light on the generative aspects of biological structures. Published by the American Physical Society 2024

Topics & Concepts

Evolutionary biologyComputational biologyCognitive scienceComputer scienceBiologyPsychologyRNA and protein synthesis mechanismsMachine Learning in BioinformaticsGenomics and Phylogenetic Studies