Multimeric protein interaction and complex prediction: Structure, dynamics and function.

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Multimeric protein complexes are crucial for biological functions, disease mechanisms, and drug design.
• Predicting protein multimers is challenging due to complex structures and limited data, unlike monomer prediction.
• Recent advancements aim to overcome these challenges in protein complex analysis.

Purpose of the Study:

• To review recent advancements in protein multimer prediction and analysis.
• To compare multimer prediction methods with monomer prediction techniques.
• To explore the impact of AlphaFold2 & 3 and deep learning on the field.

Main Methods:

• Overview of classical and modern methodologies for multimer prediction.
• Analysis of state-of-the-art approaches from CASP16, including stoichiometry and supercomplex prediction.
• Evaluation of deep learning models for interaction analysis and quality assessment.

Main Results:

• AlphaFold2 & 3 show promise but have limitations in predicting functional interactions and dynamics.
• Deep learning methods enhance multimer interaction analysis and quality assessment.
• CASP16 highlights progress in predicting complex features like conformational ensembles.

Conclusions:

• Accurate protein multimer prediction is advancing, driven by deep learning and tools like AlphaFold.
• Future research should focus on improving prediction accuracy, functional interpretation, and dynamic mechanisms.
• Enhanced understanding of protein complexes will accelerate biomedical research and drug discovery.