PDF(Pubmed)
Abstract：
The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins-the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared with the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein \"vocabulary.\" A major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago or have never evolved (yet). By merging evolutionary algorithms, machine learning, and bioinformatics, we can develop highly customized \"designer proteins.\" We dub the new subfield of computational evolution, which employs evolutionary algorithms with DNA string representations, biologically accurate molecular evolution, and bioinformatics-informed fitness functions, Evolutionary Algorithms Simulating Molecular Evolution.
摘要：
生命基本功能的基因蓝图被编码在DNA中，它被翻译成蛋白质-驱动我们大部分代谢过程的引擎。基因组测序的最新进展揭示了各种各样的蛋白质家族，但是与所有可能的氨基酸序列的大量搜索空间相比，已知功能家族的集合是最小的。人们可以说大自然有一个有限的蛋白质“词汇。“计算生物学家的一个主要问题，因此,是这个词汇是否可以扩展到包括很久以前灭绝或从未进化的有用蛋白质。通过合并进化算法，机器学习,和生物信息学，我们可以开发高度定制的“设计师蛋白质”。\"我们配音计算进化的新的子领域，它采用了DNA字符串表示的进化算法，生物精确的分子进化，和生物信息学信息健身功能，模拟分子进化的进化算法。
