Advancements And Future Directions In Machine Learning-assisted Protein Engineering

Protein engineering is a rapidly evolving field that has the potential to revolutionize various sectors. Leveraging large protein databases and advanced ML models, especially those inspired by NLP, has significantly accelerated the process of protein engineering. Machine learning-assisted protein engineering (MLPE) involves a comprehensive approach integrating data collection, feature extraction, model training, and iterative validation, supported by high-throughput sequencing and screening technologies. Advanced mathematical tools such as TDA and NLP-based models play a crucial role in data representation, which is vital for accurate model training and prediction. Despite substantial advancements, challenges like data preprocessing, feature extraction, and iterative optimization persist. The review addresses these issues and discusses potential future directions in the field, aiming to improve the methodologies and outcomes of MLPE further.