College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong 518118, China.
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, Guangdong, China.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae289.
Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.
蛋白质功能预测对于理解细胞的生理和生化过程至关重要,它为疾病研究和药物发现等领域的进展开辟了新的可能性。在过去的几十年中,随着蛋白质序列数据的指数级增长,已经提出了许多用于预测蛋白质功能的计算方法。因此,有必要对这些方法进行系统的回顾和比较。在这项研究中,我们将这些方法分为基于序列的方法、基于 3D 结构的方法、基于 PPI 网络的方法和基于混合信息的方法四种不同的类别。此外,我们还讨论了它们的优缺点,然后对它们的性能进行了全面的评估和比较。最后,我们讨论了该领域存在的挑战和机遇。