Recent advancements in genome sequencing have revealed a vast diversity of protein families, yet the known functional families are minimal compared to all possible amino acid sequences. The proposal introduces EASME, merging evolutionary algorithms, machine learning, and bioinformatics to develop completely novel proteins. By simulating molecular evolution, EASME aims to expand the set of extant proteins by colonizing new islands in the sea of invalidity. The intersection of computational evolution and biology remains under-explored but holds promising discoveries for biotechnological applications. The focus on solving biological problems through EASME could lead to significant advancements in various fields such as agriculture and synthetic biology.
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by James S. L. ... alle arxiv.org 03-15-2024
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