b c a DeepMainmast and CryoREAD: Protein and DNA/RNA Structure Modeling for Cryo-EM using Deep Learning Genki Terashia, Xiao Wangb, Tsukasa Nakamuraa , Han Zhub and Daisuke Kiharaa, b References 1. Terashi, Genki, et al. "DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction." Nature Methods 21 (2023): 122-131. 2. Jumper, John, et al. "Highly accurate protein structure prediction with AlphaFold." Nature 596.7873 (2021): 583- 589. 3. Wang, Xiao, Genki Terashi, and Daisuke Kihara. "CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning." Nature Methods 20.11 (2023): 1739-1747. Funding: NIH: R01GM133840 and 3R01GM133840-02S1. a Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA, b Department of Computer Science, Purdue University, West Lafayette, Indiana, USA 21 157 155 22 Results Code Availability: The source code and Colab version of DeepMainmast CryoREAD are available at https://github.com/kiharalab/DeepMainMast. https://github.com/kiharalab/CryoREAD Kiharalab EMSuite Server: https://em.kiharalab.org Cryo-EM Map Structure Detection by Deep Learning CUGACAUACUUGUUCCACUCU AGCAGCACGUAAAUAUUGGCG UAGUGAAAUAUAUAUUAAACA CCAAUAUUACUGUGCUGCUU UAGUGUGACAGGGAUACAGC AA Cryo-EM Map Sugar Detection Phosphate Detection Base Detection Phosphate Nodes Sugar Nodes Base Nodes Structure Node Clustering Backbone Tracing Sugar Graph Sugar Backbone Sequence Assignment Sequence Input Fragment Assembling Base Assignment Full Atom Model S-P-B Backbone Atomic Structure Detection Map by DL Fragment Assignment T-T….C-A-C TT-….G-A-C TG-….-A-A TT-….-C-A G-TT….TT-T G-TT….TC-T Overview of the DeepMainmast protocol applied to the cryo-EM map EMD- 6551 at a resolution of 3.8 Å Overview of CryoREAD CryoREAD [3] is a deep learning-based fully automated de novo DNA/RNA atomic structure modeling method. It can identify phosphate, sugar, and base positions in a cryo-EM map using deep learning, which are traced and modeled into a 3D structure. Single-chain Modeling Results (178 targets) Multi-chain Modeling Results (20 targets) Examples: Multi-chain modeling EMD-6551, PDB 3JCF Chains: A-E DeepMainmast SeqID: 1.00, TM-Score: 0.99 EMD-6374, PDB 3JB0 Chains: A, B and C, D and E DeepMainmast SeqID: 1.00,TM-Score: 0.99 Overview of DeepMainmast DeepMainmast [1] is a new de novo protein main-chain tracing method that uses deep learning to identify the positions of Cα atoms and the types of amino acids. To enhance the modeling performance, AlphaFold2 [2] models can be used as input data. Results Performance Comparison Backbone Recall: the fraction of backbone atoms that were closer than 5 Å to corresponding atoms of matched nucleotides. Sequence Recall: the fraction of nucleotides that have matched nucleotides in predicted structures and share the same base types. Sequence Match: calculated sequence recall only considers nucleotides in the reference structure that have matches. BB Recall Seq Recall Modeling Performance Examples: DNA/RNA Modeling Examples: Protein-RNA Modeling d c a. EMD-7480: BB Recall: 0.852; Seq Recall: 0.557; RMSD: 2.20 Å. b. EMD-4868: BB Recall: 0.907; Seq Recall: 0.54; RMSD: 3.13 Å. c. EMD-20416, BB Recall: 0.842; Seq Recall: 0.474; RMSD: 3.25 Å. d. EMD-12217: BB Recall: 0.903; Seq Recall: 0.657; RMSD: 2.60 Å. Cryo-EM map Deep Learning Detection ■Native ■Predicted Model Cryo-EM map Deep Learning Detection ■Native ■Predicted Model EMD-8622 Resolution: 3.7Å NA BB Recall: 0.910 NA Seq Recall: 0.646 NA RMSD: 3.03 Protein TM-score: 0.810 Cryo-EM map Deep Learning Detection ■Native NA, ■Native Protein ■Predicted NA, ■Predicted Protein EMD-21907 Resolution: 2.9Å NA BB Recall: 0.903 NA Seq Recall: 0.660 NA RMSD: 3.131 Protein TM-score: 0.9730