Advancements in one-dimensional protein structure prediction using machine learning and deep learning.

Area of Science:

• Structural Bioinformatics
• Computational Biology
• Machine Learning

Background:

• Accurate protein structure prediction is crucial for understanding protein function.
• Traditional methods face limitations in predicting 1D protein structure annotations.
• Machine Learning (ML) and Deep Learning (DL) offer advanced solutions.

Purpose of the Study:

• To review the evolution of ML and DL in 1D protein structure prediction.
• To highlight key advancements like AlphaFold and protein language models (PLMs).
• To discuss challenges and future trends in the field.

Main Methods:

• Review of predictive methodologies from early ML to modern DL frameworks.
• Integration of sequence embeddings and pretrained language models.
• Exploration of specialized datasets and benchmarking competitions.

Main Results:

• DL frameworks and PLMs have achieved unprecedented accuracy in 1D protein prediction.
• AlphaFold has significantly impacted protein structure prediction.
• Multimodal integration enhances prediction model capabilities.

Conclusions:

• ML, DL, and PLMs have transformed 1D protein prediction.
• Addressing challenges in data quality, scalability, and interpretability is key.
• The field is rapidly evolving with promising future directions.