Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
Curr Opin Struct Biol. 2024 Jun;86:102793. doi: 10.1016/j.sbi.2024.102793. Epub 2024 Mar 5.
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.
蛋白质-配体结合位点预测对于蛋白质功能注释和药物发现至关重要。生物实验耗时且需要大量的设备、材料和劳动力资源。因此，开发准确高效的计算方法来预测蛋白质-配体相互作用至关重要。在这里，我们总结了与配体结合位点（LBS）预测相关的关键挑战，并介绍了最近发表的基于其输入特征、计算算法和配体类型的方法。此外，我们研究了变构位点鉴定作为一种特殊的 LBS 类型的特异性。最后，我们讨论了基于机器学习的 LBS 预测在不久的将来的前景方向。
