Kalakoti Yogesh, Sanjeev Airy, Wallner Björn
Linköping University, Division of Bioinformatics, Department of Physics, Chemistry and Biolog, Linköping, 58183, Sweden.
Linköping University, Division of Bioinformatics, Department of Physics, Chemistry and Biolog, Linköping, 58183, Sweden.
Curr Opin Struct Biol. 2025 Apr;91:103003. doi: 10.1016/j.sbi.2025.103003. Epub 2025 Feb 20.
Proteins are dynamic molecules that transition between conformational states to perform their functions, and characterizing the protein ensemble is important for understanding biology and therapeutic applications. While recent breakthroughs in machine learning have enabled the prediction of high-quality static models of individual proteins, generating reliable estimates of their conformational ensembles remains a challenge. Several recent methods have tried to utilize the evolutionary and structural features captured by effective sequence-to-structure models to enhance conformational diversity in generated models. Most of these approaches involve adapting existing inference pipelines, such as AlphaFold 2, combined with sampling techniques to induce the generation of diverse conformational states. Here, we describe the general problem of predicting structural variations in protein systems, explain the methods designed to address this challenge, explore why they are effective, discuss their limitations, and suggest potential future directions.
蛋白质是动态分子，它们在构象状态之间转换以执行其功能，表征蛋白质集合对于理解生物学和治疗应用很重要。虽然机器学习最近的突破使得能够预测单个蛋白质的高质量静态模型，但生成其构象集合的可靠估计仍然是一个挑战。最近的几种方法试图利用有效序列到结构模型捕获的进化和结构特征，以增强生成模型中的构象多样性。这些方法大多涉及调整现有的推理管道，如AlphaFold 2，并结合采样技术来诱导生成不同的构象状态。在这里，我们描述了预测蛋白质系统结构变化的一般问题，解释了为应对这一挑战而设计的方法，探讨了它们为何有效，讨论了它们的局限性，并提出了潜在的未来方向。
