Evolutionary Algorithms Simulating Molecular Evolution: A New Field Proposal

TL;DR

This paper proposes a new computational approach combining evolutionary algorithms, machine learning, and bioinformatics to generate novel proteins, potentially expanding the known protein vocabulary beyond natural evolution.

Contribution

It introduces a novel sub-field called evolutionary algorithms simulating molecular evolution (EASME) for designing new proteins.

Findings

Framework for integrating evolutionary algorithms with ML and bioinformatics

Potential to discover proteins beyond natural evolutionary constraints

Lays groundwork for a new research area in computational molecular evolution

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 to 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." The major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago, or maybe never evolved in the first place. We outline a computational approach to solving this problem. By merging evolutionary algorithms, machine learning (ML), and bioinformatics, we can facilitate the development of completely novel proteins which have never existed before. We envision this work forming a new sub-field of computational evolution we dub evolutionary algorithms simulating molecular evolution (EASME).