Silver Surfer: Navigating the Parametric Protein Space with Genetic Algorithms

Designing and synthesising proteins with specific physicochemical properties pose significant challenges in biotechnology, environmental sciences, and pharmaceuticals. Recent advancements in machine learning have opened up possibilities but have often been constrained by their focus on limited facets of the multifaceted nature of proteins. This paper presents the Silver Surfer, a new platform utilising a customised genetic algorithm designed to explore the complex space of protein sequences. The Silver Surfer implementation offers a powerful, easily extensible tool to meet diverse scientific needs, operating based on four general-use protein properties: Instability Index, Monoisotopic Mass, Grand Average Hydrophobicity Score, and Isoelectric Point. By harnessing the generative capabilities of genetic algorithms, the Silver Surfer project opens new horizons in protein design.