Leveraging Deep Generative Model For Computational Protein Design And Optimization

Abstract

Proteins are the fundamental macromolecules that play diverse and crucial roles in all living matter and have tremendous implications in healthcare, manufacturing, and biotechnology. Their functions are largely determined by the sequences of amino acids that compose them and their unique three-dimensional structures when folded. The recent surge in highly accurate computational protein structure prediction tools has equipped scientists with the means to derive preliminary structural insights without the onerous costs of experimental structure determination. These breakthroughs hold profound promise for building robust and efficient in silico protein design systems. While the prospect of designing de novo proteins with precise computational accuracy remains a grand challenge in biochemical engineering, conventional assembly-based and rational design methods often grapple with the expansive design space, resulting in suboptimal design success rates. Despite recently emerged deep learning-based models have shown promise in improving the efficiency of the computational protein design process, a significant gap persists between current design paradigms and their experimental realization. This thesis will investigate the potential of deep generative models in refining protein structure and sequence design methods, aiming to develop frameworks capable of crafting novel protein sequences with predetermined structures or specific functionalities. By harnessing extensive protein databases and cutting-edge neural architectures, this research aims to enhance precision and robustness in current protein design paradigms, potentially paving the way for advancements across various scientific fields.