doi.bio/frank_dimaio

Frank DiMaio

Early Life and Education

Frank DiMaio is an Associate Professor at the Institute for Protein Design at the University of Washington, Seattle. He holds a PhD and is focused on protein structure determination from sparse and noisy experimental data.

Career and Research

DiMaio's research interests lie in developing novel computational tools to determine high-resolution protein structures using only low-resolution data. He aims to draw information from previously solved structures to fill in the gaps in our understanding of protein conformation.

He has developed methods for solving difficult molecular replacement problems and elucidated over a dozen structures that were previously unsolved by expert crystallographers. DiMaio has also created a new approach for low-resolution crystal refinement, outperforming state-of-the-art refinement packages.

Notable Works

DiMaio, F., Terwilliger, T., Read, R., Wlodawer, A., Oberdorfer, G., Valkov, E. et al. (2011). Improving molecular replacement by density- and energy-guided protein structure optimization. Nature.
Alon, A., Grossman, I., Gat, Y., Kodali, V.K., DiMaio, F., Mehlman, T. et al. (2012). The dynamic disulphide relay of quiescin sulphydryl oxidase. Nature.
Valkov, E., Stamp, A., DiMaio, F., Baker, D., Verstak, B., Roversi, P. et al. (2011). Crystal structure of Toll-like receptor adaptor MAL/TIRAP reveals the molecular basis for signal transduction and disease protection. Proceedings of the National Academy of Sciences.
DiMaio, F., Echols, N., Headd, J., Terwilliger, T., Adams, P., Baker, D. (2013). Improved protein crystal structures at low resolution by integrated refinement with Phenix and Rosetta. Nature Methods.
Chen, Z., Johnson, M.C., Chen, J., Bick, M.J., Boyken, S.E., Lin, B. et al. (2019). Self-Assembling 2D Arrays with de Novo Protein Building Blocks. J Am Chem Soc.
Farrell, D.P., Anishchenko, I., Shakeel, S., Lauko, A., Passmore, L.A., Baker, D., DiMaio, F. (2019). Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM. Iucrj.
Xu, C., Lu, P., Gamal El-Din, T.M., Pei, X.Y., Johnson, M.C., Uyeda, A. et al. (2020). Computational design of transmembrane pores. Nature.
Koepnick, B., Flatten, J., Husain, T., Ford, A., Silva, D.A., Bick, M.J. et al. (2019). De novo protein design by citizen scientists. Nature.
Frenz, B., Walls, A., Egelman, E., Veesler, D., DiMaio, F. (2017). A greedy conformational sampling strategy enables automated interpretation of difficult cryoEM maps. Nature Methods.
Wang, R.Y., Song, Y., Barad, B., Cheng, Y., Fraser, J.S., DiMaio, F. (2016). Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta. eLife.
DiMaio, F., N. Echols, J. Headd, T. Terwilliger, P. Adams, D. Baker (2013). Improved protein crystal structures at low resolution by integrated refinement with Phenix and Rosetta. Nature Methods.

Awards and Recognition

DiMaio was named on the Highly Cited Researchers 2022 and 2023 lists from Clarivate, recognising his impact in his field.

Google Scholar Profile

Frank DiMaio)

Google Scholar

Frank DiMaio University of Washington N/A Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, … Science 373 (6557), 871-876, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:hkOj_22Ku90C Cited by: 3649

The Rosetta all-atom energy function for macromolecular modeling and design RF Alford, A Leaver-Fay, JR Jeliazkov, MJ O’Meara, FP DiMaio, H Park, … Journal of chemical theory and computation 13 (6), 3031-3048, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:t7zJ5fGR-2UC Cited by: 1306

High-resolution comparative modeling with RosettaCM Y Song, F DiMaio, RYR Wang, D Kim, C Miles, TJ Brunette, J Thompson, … Structure 21 (10), 1735-1742, 2013 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:sSrBHYA8nusC Cited by: 1159

EMRinger: side chain–directed model and map validation for 3D cryo-electron microscopy BA Barad, N Echols, RYR Wang, Y Cheng, F DiMaio, PD Adams, … Nature methods 12 (10), 943-946, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:LPZeul_q3PIC Cited by: 752

Crystal structure of a monomeric retroviral protease solved by protein folding game players F Khatib, F DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, … Nature structural & molecular biology 18 (10), 1175-1177, 2011 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:UebtZRa9Y70C Cited by: 673

Macromolecular modeling and design in Rosetta: recent methods and frameworks JK Leman, BD Weitzner, SM Lewis, J Adolf-Bryfogle, N Alam, RF Alford, … Nature methods 17 (7), 665-680, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:5ugPr518TE4C Cited by: 609

Structure prediction for CASP8 with all‐atom refinement using Rosetta S Raman, R Vernon, J Thompson, M Tyka, R Sadreyev, J Pei, D Kim, … Proteins: Structure, Function, and Bioinformatics 77 (S9), 89-99, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:vV6vV6tmYwMC Cited by: 595

Cryo-electron microscopy structure of a coronavirus spike glycoprotein trimer AC Walls, MA Tortorici, BJ Bosch, B Frenz, PJM Rottier, F DiMaio, FA Rey, … Nature 531 (7592), 114-117, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:abG-DnoFyZgC Cited by: 548

De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, … Nature 620 (7976), 1089-1100, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:b1wdh0AR-JQC Cited by: 455

De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, … Nature 600 (7889), 547-552, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:_Re3VWB3Y0AC Cited by: 437

Simultaneous optimization of biomolecular energy functions on features from small molecules and macromolecules H Park, P Bradley, P Greisen Jr, Y Liu, VK Mulligan, DE Kim, D Baker, … Journal of chemical theory and computation 12 (12), 6201-6212, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:CHSYGLWDkRkC Cited by: 431

Glycan shield and epitope masking of a coronavirus spike protein observed by cryo-electron microscopy AC Walls, MA Tortorici, B Frenz, J Snijder, W Li, FA Rey, F DiMaio, … Nature structural & molecular biology 23 (10), 899-905, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:M3NEmzRMIkIC Cited by: 404

Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta RYR Wang, Y Song, BA Barad, Y Cheng, JS Fraser, F DiMaio Elife 5, e17219, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:VOx2b1Wkg3QC Cited by: 401

Relaxation of backbone bond geometry improves protein energy landscape modeling P Conway, MD Tyka, F DiMaio, DE Konerding, D Baker Protein science 23 (1), 47-55, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:4fKUyHm3Qg0C Cited by: 376

De novo design of protein homo-oligomers with modular hydrogen-bond network–mediated specificity SE Boyken, Z Chen, B Groves, RA Langan, G Oberdorfer, A Ford, … Science 352 (6286), 680-687, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:Zph67rFs4hoC Cited by: 361

Alternate states of proteins revealed by detailed energy landscape mapping MD Tyka, DA Keedy, I André, F DiMaio, Y Song, DC Richardson, … Journal of molecular biology 405 (2), 607-618, 2011 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:dhFuZR0502QC Cited by: 361

Near-atomic model of microtubule-tau interactions EH Kellogg, NMA Hejab, S Poepsel, KH Downing, F DiMaio, E Nogales Science 360 (6394), 1242-1246, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:B3FOqHPlNUQC Cited by: 345

Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement F DiMaio, Y Song, X Li, MJ Brunner, C Xu, V Conticello, E Egelman, … Nature methods 12 (4), 361-365, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:zYLM7Y9cAGgC Cited by: 333

Refinement of protein structures into low-resolution density maps using rosetta F DiMaio, MD Tyka, ML Baker, W Chiu, D Baker Journal of molecular biology 392 (1), 181-190, 2009 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:HoB7MX3m0LUC Cited by: 323

High thermodynamic stability of parametrically designed helical bundles PS Huang, G Oberdorfer, C Xu, XY Pei, BL Nannenga, JM Rogers, … science 346 (6208), 481-485, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:maZDTaKrznsC Cited by: 320

Google Scholar

Frank DiMaio

University of Washington

N/A

Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, … Science 373 (6557), 871-876, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=jQpFYpIAAAAJ&citationforview=jQpFYpIAAAAJ:hkOj_22Ku90C