A comprehensive survey on protein-ligand binding site prediction.
Ying Xia,Xiaoyong Pan,Hong-Bin Shen +2 more
9
TL;DR: This comprehensive survey on protein-ligand binding site prediction highlights key challenges, recent methods, and future directions for machine learning-based prediction, focusing on input features, algorithms, ligand types, and specificity of allosteric site identification.
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Abstract: Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.
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