Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein–protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.

蛋白质结构预测是一种弥合序列-结构差距的方法，这是计算生物学和化学领域的主要挑战之一。预测任何蛋白质的准确结构对科学界至关重要，因为这些结构控制着它们的功能。此外，这是计算生物学家曾经面临的复杂优化问题之一。实验性蛋白质结构测定方法包括 X 射线晶体学、核磁共振光谱和电子显微镜。所有这些都是繁琐且耗时的程序，需要专业知识。为了让这个过程不那么麻烦，科学家们使用预测工具作为计算方法的一部分，使用整合在蛋白质库中的数据。最近几年，机器学习方法引起了结构预测社区的兴趣。大多数用于蛋白质结构预测的机器学习方法都集中在基于协同进化的方法上。这些方法的准确性取决于数据库中可用的同源蛋白质序列的数量。对于许多蛋白质，尤其是那些没有足够序列同源物的蛋白质，预测问题变得具有挑战性。深度学习方法允许从蛋白质序列数据中提取复杂的特征，而无需做出任何直觉。准确预测的蛋白质结构用于药物发现、抗体设计、理解蛋白质-蛋白质相互作用以及与其他分子的相互作用。本文综述了蛋白质结构预测中的传统和深度学习方法。