The Vanderbilt Program in Personalized Structural Biology (PSB) collaborates extensively with the Vanderbilt Ingram Cancer Center (VICC) and other leading institutions to characterize variants of unknown significance (VUS) in cancer patients. Over the last decade, the coming-of-age of protein structure prediction has made it possible to create high-quality atomic structural models of proteins for which there are no known experimental structures. The PSB uses a combination of Rosetta1 and modern deep learning methodologies2,3 to build models of proteins with VUS. Subsequently, we run Rosetta energy calculations and molecular dynamics (MD) simulations of the protein models to make predictions about the effects of the VUS mutations on function and/or resistance to medical therapy4–9. The PSB is unique because of its organic relationship with practicing physicians and physician-scientists. We actively engage in formal and informal dialogues with our friends and colleagues in the clinics to make structural analysis of protein mutations a common and important component of clinical decision-making. As we and others continue to advance the technology to efficiently and comprehensively evaluate VUS, it is our vision that such analyses will become a routine component of modern health care delivery.
References:
[1]
Leman, J. K.; Weitzner, B. D.; Lewis, S. M.; Adolf-Bryfogle, J.; Alam, N.; Alford, R. F.; Aprahamian, M.; Baker, D.; Barlow, K. A.; Barth, P.; Basanta, B.; Bender, B. J.; Blacklock, K.; Bonet, J.; Boyken, S. E.; Bradley, P.; Bystroff, C.; Conway, P.; Cooper, S.; Correia, B. E.; Coventry, B.; Das, R.; De Jong, R. M.; DiMaio, F.; Dsilva, L.; Dunbrack, R.; Ford, A. S.; Frenz, B.; Fu, D. Y.; Geniesse, C.; Goldschmidt, L.; Gowthaman, R.; Gray, J. J.; Gront, D.; Guffy, S.; Horowitz, S.; Huang, P.-S.; Huber, T.; Jacobs, T. M.; Jeliazkov, J. R.; Johnson, D. K.; Kappel, K.; Karanicolas, J.; Khakzad, H.; Khar, K. R.; Khare, S. D.; Khatib, F.; Khramushin, A.; King, I. C.; Kleffner, R.; Koepnick, B.; Kortemme, T.; Kuenze, G.; Kuhlman, B.; Kuroda, D.; Labonte, J. W.; Lai, J. K.; Lapidoth, G.; Leaver-Fay, A.; Lindert, S.; Linsky, T.; London, N.; Lubin, J. H.; Lyskov, S.; Maguire, J.; Malmström, L.; Marcos, E.; Marcu, O.; Marze, N. A.; Meiler, J.; Moretti, R.; Mulligan, V. K.; Nerli, S.; Norn, C.; Ó’Conchúir, S.; Ollikainen, N.; Ovchinnikov, S.; Pacella, M. S.; Pan, X.; Park, H.; Pavlovicz, R. E.; Pethe, M.; Pierce, B. G.; Pilla, K. B.; Raveh, B.; Renfrew, P. D.; Burman, S. S. R.; Rubenstein, A.; Sauer, M. F.; Scheck, A.; Schief, W.; Schueler-Furman, O.; Sedan, Y.; Sevy, A. M.; Sgourakis, N. G.; Shi, L.; Siegel, J. B.; Silva, D.-A.; Smith, S.; Song, Y.; Stein, A.; Szegedy, M.; Teets, F. D.; Thyme, S. B.; Wang, R. Y.-R.; Watkins, A.; Zimmerman, L.; Bonneau, R. Macromolecular Modeling and Design in Rosetta: Recent Methods and Frameworks. Nat Methods 2020, 17 (7), 665–680.
[2]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S. A. A.; Ballard, A. J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A. W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly Accurate Protein Structure Prediction with AlphaFold. Nature 2021, 596 (7873), 583–589.
[3]
Accurate prediction of protein structures and interactions using a three-track neural network (accessed 2022 -02 -02).
[4]
Brown, B. P.; Zhang, Y.-K.; Westover, D.; Yan, Y.; Qiao, H.; Huang, V.; Du, Z.; Smith, J. A.; Ross, J. S.; Miller, V. A.; Ali, S.; Bazhenova, L.; Schrock, A. B.; Meiler, J.; Lovly, C. M. On-Target Resistance to the Mutant-Selective EGFR Inhibitor Osimertinib Can Develop in an Allele-Specific Manner Dependent on the Original EGFR-Activating Mutation. Clin. Cancer. Res. 2019, 25 (11), 3341–3351.
[5]
Du, Z.; Brown, B. P.; Kim, S.; Ferguson, D.; Pavlick, D. C.; Jayakumaran, G.; Benayed, R.; Gallant, J.-N.; Zhang, Y.-K.; Yan, Y.; Red-Brewer, M.; Ali, S. M.; Schrock, A. B.; Zehir, A.; Ladanyi, M.; Smith, A. W.; Meiler, J.; Lovly, C. M. Structure–Function Analysis of Oncogenic EGFR Kinase Domain Duplication Reveals Insights into Activation and a Potential Approach for Therapeutic Targeting. Nature Communications 2021, 12 (1), 1382.
[6]
Hanker, A. B.; Brown, B. P.; Meiler, J.; Marín, A.; Jayanthan, H. S.; Ye, D.; Lin, C.-C.; Akamatsu, H.; Lee, K.-M.; Chatterjee, S.; Sudhan, D. R.; Servetto, A.; Brewer, M. R.; Koch, J. P.; Sheehan, J. H.; He, J.; Lalani, A. S.; Arteaga, C. L. Co-Occurring Gain-of-Function Mutations in HER2 and HER3 Modulate HER2/HER3 Activation, Oncogenesis, and HER2 Inhibitor Sensitivity. Cancer Cell 2021, 39 (8), 1099-1114.e8.
[7]
Gallant, J. N.; Sheehan, J. H.; Shaver, T. M.; Bailey, M.; Lipson, D.; Chandramohan, R.; Red Brewer, M.; York, S. J.; Kris, M. G.; Pietenpol, J. A.; Ladanyi, M.; Miller, V. A.; Ali, S. M.; Meiler, J.; Lovly, C. M. EGFR Kinase Domain Duplication (EGFR-KDD) Is a Novel Oncogenic Driver in Lung Cancer That Is Clinically Responsive to Afatinib. Cancer Discov. 2015, 5 (11), 1155–1163.
[8]
Konduri, K.; Gallant, J. N.; Chae, Y. K.; Giles, F. J.; Gitlitz, B. J.; Gowen, K.; Ichihara, E.; Owonikoko, T. K.; Peddareddigari, V.; Ramalingam, S. S.; Reddy, S. K.; Eaby-Sandy, B.; Vavala, T.; Whiteley, A.; Chen, H.; Yan, Y.; Sheehan, J. H.; Meiler, J.; Morosini, D.; Ross, J. S.; Stephens, P. J.; Miller, V. A.; Ali, S. M.; Lovly, C. M. EGFR Fusions as Novel Therapeutic Targets in Lung Cancer. Cancer Discov. 2016, 6 (6), 601–611.
[9]
Hanker, A. B.; Brewer, M. R.; Sheehan, J. H.; Koch, J. P.; Sliwoski, G. R.; Nagy, R.; Lanman, R.; Berger, M. F.; Hyman, D. M.; Solit, D. B.; He, J.; Miller, V.; Cutler, R. E.; Lalani, A. S.; Cross, D.; Lovly, C. M.; Meiler, J.; Arteaga, C. L. An Acquired HER2 T798I Gatekeeper Mutation Induces Resistance to Neratinib in a Patient with HER2 Mutant–Driven Breast Cancer. Cancer Discov 2017, 7 (6), 575–585.