Robert T. McDonnell, Aaron N. Henderson, Adrian H. Elcock
{"title":"Structure Prediction of Large RNAs with AlphaFold3 Highlights its Capabilities and Limitations","authors":"Robert T. McDonnell, Aaron N. Henderson, Adrian H. Elcock","doi":"10.1016/j.jmb.2024.168816","DOIUrl":null,"url":null,"abstract":"<div><div>DeepMind’s AlphaFold3 webserver offers exciting new opportunities to make structural predictions of heterogeneous macromolecular systems. Here we attempt to apply AlphaFold3 to large RNA molecules whose 3D atomic structures are unknown but whose physical dimensions have been studied experimentally. One difficulty that we encounter is that models returned by AlphaFold3 often contain severe steric clashes and, less frequently, clear breaks in the phosphodiester backbone, with the probability of both events increasing with the length of the RNA. Restricting attention to those RNAs for which non-clashing models can be obtained, we find that hydrodynamic radii computed from the AlphaFold3 models are much larger than those reported experimentally under low salt conditions but are in better agreement with those reported in the presence of polyvalent cations. For two RNAs whose shapes have been imaged experimentally, the computed anisotropies of the AlphaFold3-predicted structures are too low, indicating that they are excessively spherical; extending this analysis to larger RNAs shows that they become progressively more spherical with increasing length. Overall, the results suggest that AlphaFold3 is capable of producing plausible models for RNAs up to ∼2000 nucleotides in length, but that thousands of predictions may be required to obtain models free of geometric problems.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022283624004388","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
DeepMind’s AlphaFold3 webserver offers exciting new opportunities to make structural predictions of heterogeneous macromolecular systems. Here we attempt to apply AlphaFold3 to large RNA molecules whose 3D atomic structures are unknown but whose physical dimensions have been studied experimentally. One difficulty that we encounter is that models returned by AlphaFold3 often contain severe steric clashes and, less frequently, clear breaks in the phosphodiester backbone, with the probability of both events increasing with the length of the RNA. Restricting attention to those RNAs for which non-clashing models can be obtained, we find that hydrodynamic radii computed from the AlphaFold3 models are much larger than those reported experimentally under low salt conditions but are in better agreement with those reported in the presence of polyvalent cations. For two RNAs whose shapes have been imaged experimentally, the computed anisotropies of the AlphaFold3-predicted structures are too low, indicating that they are excessively spherical; extending this analysis to larger RNAs shows that they become progressively more spherical with increasing length. Overall, the results suggest that AlphaFold3 is capable of producing plausible models for RNAs up to ∼2000 nucleotides in length, but that thousands of predictions may be required to obtain models free of geometric problems.
期刊介绍:
Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions.
Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.