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一种基于遗传密码的DNA序列进化研究算法。

An algorithm for the study of DNA sequence evolution based on the genetic code.

作者信息

Sirakoulis G Ch, Karafyllidis I, Sandaltzopoulos R, Tsalides Ph, Thanailakis A

机构信息

Department of Electrical and Computer Engineering, Democritus University of Thrace, 671 00 Xanthi, Greece.

出版信息

Biosystems. 2004 Nov;77(1-3):11-23. doi: 10.1016/j.biosystems.2004.02.006.

Abstract

Recent studies of the quantum-mechanical processes in the DNA molecule have seriously challenged the principle that mutations occur randomly. The proton tunneling mechanism causes tautomeric transitions in base pairs resulting in mutations during DNA replication. The meticulous study of the quantum-mechanical phenomena in DNA may reveal that the process of mutagenesis is not completely random. We are still far away from a complete quantum-mechanical model of DNA sequence mutagenesis because of the complexity of the processes and the complex three-dimensional structure of the molecule. In this paper we have developed a quantum-mechanical description of DNA evolution and, following its outline, we have constructed a classical model for DNA evolution assuming that some aspects of the quantum-mechanical processes have influenced the determination of the genetic code. Conversely, our model assumes that the genetic code provides information about the quantum-mechanical mechanisms of mutagenesis, as the current code is the product of an evolutionary process that tries to minimize the spurious consequences of mutagenesis. Based on this model we develop an algorithm that can be used to study the accumulation of mutations in a DNA sequence. The algorithm has a user-friendly interface and the user can change key parameters in order to study relevant hypotheses.

摘要

最近对DNA分子中量子力学过程的研究严重挑战了突变随机发生的原则。质子隧穿机制导致碱基对中的互变异构转变,从而在DNA复制过程中产生突变。对DNA中量子力学现象的细致研究可能会揭示,诱变过程并非完全随机。由于过程的复杂性以及分子复杂的三维结构,我们距离完整的DNA序列诱变量子力学模型仍有很大差距。在本文中,我们建立了DNA进化的量子力学描述,并按照其框架构建了一个DNA进化的经典模型,假设量子力学过程的某些方面影响了遗传密码的确定。相反,我们的模型假设遗传密码提供了有关诱变量子力学机制的信息,因为当前的密码是一个试图将诱变的虚假后果降至最低的进化过程的产物。基于此模型,我们开发了一种可用于研究DNA序列中突变积累的算法。该算法具有用户友好的界面,用户可以更改关键参数以研究相关假设。

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