Generative models for protein sequence modeling: recent advances and future directions.

Area of Science:

• Computational Biology
• Biotechnology
• Artificial Intelligence

Background:

• High-throughput omics technologies generate vast protein sequence data relevant to disease pathways.
• Limited experimental fitness annotations necessitate advanced machine learning (ML) methods.
• Self-supervised and unsupervised ML leverage unlabeled sequences for protein engineering.

Purpose of the Study:

• To provide an overview of successful ML models for sequence data analysis.
• To guide the implementation of ML models for protein fitness prediction and generation.
• To highlight successful applications of ML in protein engineering.

Main Methods:

• Review of ML architectures: variational autoencoders, autoregressive models, generative adversarial networks, and diffusion models.
• Guidance on applying ML models to protein sequence data for fitness prediction and generation.
• Compilation of case studies demonstrating ML in protein engineering tasks.

Main Results:

• Detailed explanation of ML model architectures and mathematical underpinnings.
• Practical strategies for implementing ML models on protein sequence data.
• Examples of ML applications including paratope prediction, subcellular localization, and de novo protein design.

Conclusions:

• ML, particularly generative AI, offers powerful tools for navigating protein fitness landscapes.
• Structured guidance and a robust framework are provided for ML-driven protein engineering.
• This work offers a prospective outlook on the future of ML in advancing protein engineering.