GAN-GA: A Deep-Evolutionary Approach for Synthesizing Functionally Similar Heat Shock Proteins.

Abstract

De novo protein design has emerged as a transformative approach for generating functional proteins without relying on naturally occurring templates, offering vast potential in biomedical and industrial applications. The integration of generative deep learning and evolutionary computation has opened new avenues in synthetic protein engineering. In this study, a hybrid framework, termed GAN-GA (Generative Adversarial Networks + Genetic Algorithm), has been proposed to address the limitations of traditional sequence based generation methods. Specifically, Wasserstein GANs have been employed to produce novel protein sequences that resemble natural proteins. However, their outputs have often lacked functional and physicochemical validation. To improve the biological relevance of these sequences, a multi-objective genetic algorithm, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), has been incorporated as a post-generation refinement stage. Through this optimization, sequences satisfying essential physicochemical criteria have been obtained. The proposed framework has been applied to design synthetic Heat Shock Proteins(HSPs), particularly HSP70 and HSP90. Two configurations of the framework have been developed; one focusing on dual-objective optimization (GAN-GA$_{2}$) and another extending the optimization to six key physicochemical attributes (GAN-GA$_{6}$). The resulting sequences have been evaluated and compared with those produced by other state of the art algorithms such as GAN, ProtBert, ESM, Variational Autoencoder (VAE), and Reinforcement Learning (RL) approaches. The optimization in the second stage ensures both structural soundness and biological plausibility of the generated protein sequences. This establishes GAN-GA as a promising strategy for de novo protein engineering in therapeutic and synthetic biology applications.