Mamun Yasir, Aguado Ally, Preza Ana, Kadel Abhilasha, Mogallur Anjani, Gonzalez Briana, De La Rosa Jayleen, Diaz Daniel, Evdokimova Polina, Karki Ukesh, Tse-Dinh Yuk-Ching, Chapagain Prem
Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA.
Department of Physics, Florida International University, Miami, FL 33199, USA.
Comput Struct Biotechnol J. 2025 Mar 27;27:1342-1349. doi: 10.1016/j.csbj.2025.03.041. eCollection 2025.
Advancements in biophysical techniques such as X-ray crystallography and Cryo-EM have allowed the determination of three-dimensional structures of many proteins and nucleic acids. There, however, is still a lack of 3D structures of proteins that are difficult to crystallize or proteins in complex with other macromolecules. With the advent of deep learning applications such as AlphaFold and RoseTTAFold, it is becoming possible to obtain 3D structures of proteins from their 1D sequences while also generating models of protein-nucleic acid complexes that have been difficult to capture through traditional methods. In this project, we utilized AlphaFold3 (AF3) to create a large number of predicted complexes of two type IA topoisomerases: human topoisomerase 3 beta (hTOP3B) and topoisomerase I bound to a single-stranded DNA (ssDNA). Topoisomerases are enzymes responsible for resolving topological barriers that arise during regular cellular activity. Obtaining structures of topoisomerase complexed with a ssDNA will allow us to discover possible sequence preferences of this enzyme and obtain structures that can be used to screen potential inhibitors. Our analysis showed that AF3 can predict the structure of the enzymes, especially the N-terminal domain, with high confidence. However, predicted protein-DNA complexes, especially with longer (> 25-mer) oligos, are unreliable. The models generated with shorter (9-mer) oligos are obtained with improved confidence and the substrates are placed similarly to crystal structures, but they do not reliably replicate the sequence specificity of the DNA binding of topoisomerase observed in biochemical assays and crystal structures.
诸如X射线晶体学和冷冻电镜等生物物理技术的进步，使得许多蛋白质和核酸的三维结构得以确定。然而，仍然缺乏难以结晶的蛋白质或与其他大分子复合的蛋白质的三维结构。随着深度学习应用如AlphaFold和RoseTTAFold的出现，从一维序列获得蛋白质的三维结构并生成通过传统方法难以捕捉的蛋白质-核酸复合物模型成为可能。在这个项目中，我们利用AlphaFold3（AF3）创建了大量两种IA型拓扑异构酶的预测复合物：人类拓扑异构酶3β（hTOP3B）和与单链DNA（ssDNA）结合的拓扑异构酶I。拓扑异构酶是负责解决正常细胞活动中出现的拓扑障碍的酶。获得与ssDNA复合的拓扑异构酶结构将使我们能够发现这种酶可能的序列偏好，并获得可用于筛选潜在抑制剂的结构。我们的分析表明，AF3能够高置信度地预测酶的结构，尤其是N端结构域。然而，预测的蛋白质-DNA复合物，特别是与较长（>25聚体）寡核苷酸形成的复合物，是不可靠的。用较短（9聚体）寡核苷酸生成的模型置信度有所提高，并且底物的放置与晶体结构相似，但它们不能可靠地复制在生化分析和晶体结构中观察到的拓扑异构酶DNA结合的序列特异性。
