Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Mol Syst Biol. 2024 Mar;20(3):162-169. doi: 10.1038/s44320-024-00016-x. Epub 2024 Jan 30.
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.
蛋白质是调控细胞所有生物过程的关键分子机器。大多数蛋白质折叠成三维形状，这对其功能至关重要。研究蛋白质的 3D 形状可以使我们了解活细胞中生物过程的机制，并在疾病突变的研究或新型药物治疗的发现中具有实际应用。在这里，我们回顾了基于序列的蛋白质结构预测方法的进展，重点介绍了超越单体结构预测的应用。这包括深度学习方法在蛋白质复合物结构、不同构象、蛋白质结构进化以及这些方法在蛋白质设计中的应用。这些发展为研究创造了新的机会，将对生物医学研究的许多领域产生影响。
