Computational design of protein complexes: influence of binding affinity

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology
• Artificial Intelligence in Life Sciences

Background:

• Molecular interactions, including protein-protein, protein-nucleic acid, and protein-carbohydrate binding, are fundamental to cellular processes.
• Binding affinity and specificity are dictated by the 3D structures and dynamics of biomolecular complexes.
• Accurate prediction and engineering of binding affinities remain significant challenges despite advances in AI-driven structure prediction.

Purpose of the Study:

• To review computational strategies for predicting and designing binding affinities in biomolecular complexes.
• To highlight the role of machine learning and deep learning in affinity modeling.
• To discuss future directions for affinity-guided design of functional biomolecular assemblies.

Main Methods:

• Review of emerging computational strategies for affinity prediction and rational design.
• Discussion of structure-based and sequence-based affinity models utilizing machine learning and deep learning.
• Assessment of current databases, benchmarks, and tools for mutation effect prediction.

Main Results:

• AI and deep learning are advancing structure-based and sequence-based affinity models.
• Databases and benchmarks are crucial for evaluating prediction accuracy.
• Tools exist for predicting mutation effects on binding affinity.

Conclusions:

• Computational strategies, particularly AI-driven approaches, are key to predicting and engineering biomolecular binding affinities.
• Integration of AI, high-throughput screening, and data-driven modeling holds promise for designing functional biomolecular assemblies.
• Further research is needed to overcome current challenges in affinity prediction and design.