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Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti‐Inflammatory and Gene Therapy Applications
Author(s) -
Patat Ayşenur Soytürk,
Nalbantoğlu Özkan Ufuk
Publication year - 2025
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.26810
Subject(s) - computer science , flexibility (engineering) , heuristic , protein design , task (project management) , stability (learning theory) , artificial intelligence , similarity (geometry) , computational biology , protein engineering , sequence (biology) , machine learning , protein structure , engineering , biology , mathematics , systems engineering , genetics , biochemistry , statistics , image (mathematics) , enzyme
ABSTRACT Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning‐based approaches developed to address this problem have achieved significant success. However, these approaches often do not adequately emphasize the functional properties of proteins. In this study, we developed a heuristic optimization method to enhance key functionalities such as solubility, flexibility, and stability, while preserving the structural integrity of proteins. This method aims to reduce laboratory demands by enabling a design that is both functional and structurally sound. This approach is particularly valuable for the synthetic production of proteins with anti‐inflammatory properties and those used in gene therapy. The designed proteins were initially evaluated for their ability to preserve natural structures using recovery and confidence metrics, followed by assessments with the AlphaFold tool. Additionally, natural protein sequences were mutated using a genetic algorithm and compared with those designed by our method. The results demonstrate that the protein sequences generated by our method exhibit much greater similarity to native protein sequences and structures. The code and sequences for the designed proteins are available at https://github.com/aysenursoyturk/HMHO .