蛋白质结构预测对于理解其功能和行为很重要。本综述研究对用于预测蛋白质结构的计算模型进行了全面综述。它涵盖了从已建立的蛋白质建模到最先进的人工智能(AI)框架的发展。本文将首先简要介绍蛋白质的结构,蛋白质建模,和AI。关于已建立的蛋白质建模的部分将讨论同源性建模,从头开始建模,和线程。下一部分是基于深度学习的模型。它介绍了一些最先进的人工智能模型,例如AlphaFold(AlphaFold,AlphaFold2,AlphaFold3),RoseTTAFold,ProteinBERT,等。本节还讨论了人工智能技术如何集成到瑞士模型等既定框架中,罗塞塔,还有我-TASSER.使用CASP14(结构预测的关键评估)和CASP15的排名比较模型性能。CASP16正在进行中,其结果不包括在本次审查中。连续自动模型评估(CAMEO)补充了两年一次的CASP实验。模板建模得分(TM-score),全球距离测试总分(GDT_TS),还讨论了局部距离差异测试(LDDT)得分。然后,本文承认预测蛋白质结构的持续困难,并强调了动态蛋白质行为等额外搜索的必要性。构象变化,和蛋白质-蛋白质相互作用。在应用程序部分,本文介绍了药物设计等各个领域的应用,工业,教育,和新型蛋白质的开发。总之,本文全面概述了已建立的蛋白质建模和基于深度学习的蛋白质结构预测模型的最新进展。它强调了人工智能取得的重大进展,并确定了进一步调查的潜在领域。
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.