The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Deconstruction of the Ras switching cycle through saturation mutagenesis
Open accessAbstract
Ras proteins are highly conserved signaling molecules that exhibit regulated, nucleotide-dependent switching between active and inactive states. The high conservation of Ras requires mechanistic explanation, especially given the general mutational tolerance of proteins. Here, we use deep mutational scanning, biochemical analysis and molecular simulations to understand constraints on Ras sequence. Ras exhibits global sensitivity to mutation when regulated by a GTPase activating protein and a nucleotide exchange factor. Removing the regulators shifts the distribution of mutational effects to be largely neutral, and reveals hotspots of activating mutations in residues that restrain Ras dynamics and promote the inactive state. Evolutionary analysis, combined with structural and mutational data, argue that Ras has co-evolved with its regulators in the vertebrate lineage. Overall, our results show that sequence conservation in Ras depends strongly on the biochemical network in which it operates, providing a framework for understanding the origin of global selection pressures on proteins.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
























Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“RAS mutants were once viewed as being ‘locked’ in the GTP-bound ‘on’ state, impervious to GAP-stimulated or intrinsic GTP hydrolysis, and hence undruggable. However, detailed structural and biochemical analyses of various RAS mutants associated with cancer and ‘RASopathies’”
From The current state of the art and future trends in RAS-targeted cancer therapies (2022) · cites this paper as [48]
In the citing paper
“Some oncogenic mutants, notably KRASG12C (G12C) and, to a lesser extent, KRASG12D (G12D), retain significant intrinsic GTPase activity.”
From SHP2 inhibition diminishes KRASG12C cycling and promotes tumor microenvironment remodeling (2021) · cites this paper as Bandaru et al., 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies
Stamatakis, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu033 · Cited by 32,291
Coot : model-building tools for molecular graphics
Emsley, P., & Cowtan, K. (2004) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444904019158 · Cited by 30,571
PHENIX : a comprehensive Python-based system for macromolecular structure solution
Adams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L.-W., Kapral, G. J., Grosse-Kunstleve, R. W., et al. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909052925 · Cited by 23,716
Kabsch, W. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909047337 · Cited by 16,463
Overview of the CCP 4 suite and current developments
Winn, M. D., Ballard, C. C., Cowtan, K. D., Dodson, E. J., Emsley, P., Evans, P. R., Keegan, R. M., Krissinel, E. B., Leslie, A. G. W., McCoy, A., et al. (2011) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444910045749 · Cited by 12,353
REFMAC 5 for the refinement of macromolecular crystal structures
Murshudov, G. N., Skubák, P., Lebedev, A. A., Pannu, N. S., Steiner, R. A., Nicholls, R. A., Winn, M., Long, F., & Vagin, A. A. (2011) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444911001314 · Cited by 8,415
PHENIX: a comprehensive Python-based system for macromolecular structure solution
Adams, P. D., Afonine, P. V., Bunkòczi, G., Chen, V. B., Davis, I., Echols, N., Headd, J. J., Hung, L., Kapral, G. J., Grosse‐Kunstleve, R. W., et al. (2012) · International Tables for Crystallography · doi:10.1107/97809553602060000865 · Cited by 7,334
Improved side-chain torsion potentials for the Amber ff99SB protein force field
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J., Dror, R., & Shaw, D. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,056
The Pfam protein families database: towards a more sustainable future
Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., Potter, S. C., Punta, M., Qureshi, M., Sangrador-Vegas, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1344 · Cited by 5,975
Scaling and assessment of data quality
Evans, P. (2005) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444905036693 · Cited by 4,391
Research works printed but not linked: 1
- Case, 2014, Amber 14
Cited by
Sorted by citations
Disease variant prediction with deep generative models of evolutionary data
Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.07.09.450648 · Cited by 635
Deep generative models of genetic variation capture the effects of mutations
Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601
The current state of the art and future trends in RAS-targeted cancer therapies
Punekar, S. R., Velcheti, V., Neel, B. G., & Wong, K.-K. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00671-9 · Cited by 549
Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483
Genome-wide prediction of disease variant effects with a deep protein language model
Brandes, N., Goldman, G., Wang, C. H., Ye, C., & Ntranos, V. (2023) · Nature Genetics · doi:10.1038/s41588-023-01465-0 · Cited by 489
SHP2 inhibition diminishes KRASG12C cycling and promotes tumor microenvironment remodeling
Fedele, C., Li, S., Teng, K. W., Foster, C. J. R., Peng, D., Ran, H., Mita, P., Geer, M. J., Hattori, T., Koide, A., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20201414 · Cited by 265
Mapping the energetic and allosteric landscapes of protein binding domains
Faure, A. J., Domingo, J., Schmiedel, J. M., Hidalgo-Carcedo, C., Diss, G., & Lehner, B. (2022) · Nature · doi:10.1038/s41586-022-04586-4 · Cited by 255
Esposito, D., Weile, J., Shendure, J., Starita, L. M., Papenfuss, A. T., Roth, F. P., Fowler, D. M., & Rubin, A. F. (2019) · Genome biology · doi:10.1186/s13059-019-1845-6 · Cited by 260
Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics
Markin, C. J., Mokhtari, D. A., Sunden, F., Appel, M. J., Akiva, E., Longwell, S. A., Sabatti, C., Herschlag, D., & Fordyce, P. M. (2021) · Science · doi:10.1126/science.abf8761 · Cited by 217
(2023) · doi:10.1101/2023.12.07.570727 · Cited by 207
The landscape of tolerated genetic variation in humans and primates
(2023) · doi:10.1126/science.abn8197 · Cited by 182
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations
Livesey, B., & Marsh, J. A. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199380 · Cited by 164
Multiplexed assays of variant effects contribute to a growing genotype–phenotype atlas
Weile, J., & Roth, F. P. (2018) · Human Genetics · doi:10.1007/s00439-018-1916-x · Cited by 162
The energetic and allosteric landscape for KRAS inhibition
Weng, C., Faure, A. J., Escobedo, A., & Lehner, B. (2023) · Nature · doi:10.1038/s41586-023-06954-0 · Cited by 161
The reactivity-driven biochemical mechanism of covalent KRASG12C inhibitors
Hansen, R., Peters, U., Babbar, A., Chen, Y., Feng, J., Janes, M. R., Li, L.-S., Ren, P., Liu, Y., & Zarrinkar, P. P. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0061-5 · Cited by 153
Unsupervised evolution of protein and antibody complexes with a structure-informed language model
Shanker, V. R., Bruun, T. U. J., Hie, B., & Kim, P. S. (2024) · Science · doi:10.1126/science.adk8946 · Cited by 174
Protein language model embeddings for fast, accurate, alignment-free protein structure prediction
Weissenow, K., Heinzinger, M., & Rost, B. (2021) · doi:10.1101/2021.07.31.454572 · Cited by 139
In silico saturation mutagenesis of cancer genes
Muiños, F., Martínez-Jiménez, F., Pich, O., González-Pérez, A., & López-Bigas, N. (2021) · Nature · doi:10.1038/s41586-021-03771-1 · Cited by 125
Oncogenic Ras Isoforms Signaling Specificity at the Membrane
Nussinov, R., Tsai, C., & Jang, H. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-2727 · Cited by 124
Høie, M. H., Cagiada, M., Frederiksen, A. H. B., Stein, A., & Lindorff‐Larsen, K. (2022) · Cell Reports · doi:10.1016/j.celrep.2021.110207 · Cited by 118
Ntai, I., Fornelli, L., DeHart, C. J., Hutton, J. E., Doubleday, P. F., LeDuc, R. D., van Nispen, A. J., Fellers, R. T., Whiteley, G., Boja, E. S., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716122115 · Cited by 108
Updated benchmarking of variant effect predictors using deep mutational scanning
Livesey, B., & Marsh, J. A. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211474 · Cited by 118
Rhapsody: predicting the pathogenicity of human missense variants
Ponzoni, L., Peñaherrera, D., Oltvai, Z. N., & Bahar, İ. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa127 · Cited by 99
Johnson, C. W., Lin, Y.-J., Reid, D., Parker, J., Pavlopoulos, S., Dischinger, P., Graveel, C., Aguirre, A. J., Steensma, M., Haigis, K. M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.026 · Cited by 89
The Ras switch in structural and historical perspective
Gasper, R., & Wittinghofer, F. (2019) · Biological Chemistry · doi:10.1515/hsz-2019-0330 · Cited by 92
Predicting the pathogenicity of missense variants using features derived from AlphaFold2
Schmidt, A., Röner, S., Mai, K., Klinkhammer, H., Kircher, M., & Ludwig, K. U. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad280 · Cited by 79
The Interdependent Activation of Son-of-Sevenless and RasPreprint
Bandaru, P., Kondo, Y., & Kuriyan, J. (2018) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a031534 · Cited by 81
Self-citationExploring amino acid functions in a deep mutational landscape
Dunham, A. S., & Beltrão, P. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110305 · Cited by 70
Structural insight into the rearrangement of the switch I region in GTP-bound G12A K-Ras
Xu, S., Long, B. N., Boris, G. H., Chen, A., Ni, S., & Kennedy, M. A. (2017) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798317015418 · Cited by 62
Interpreting protein variant effects with computational predictors and deep mutational scanning
Livesey, B., & Marsh, J. A. (2022) · Disease Models & Mechanisms · doi:10.1242/dmm.049510 · Cited by 62
Thompson, S., Zhang, S., Ingle, C., Reynolds, K. A., & Kortemme, T. (2020) · eLife · doi:10.7554/elife.53476 · Cited by 65
Self-citationDeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction
Munro, D. B., & Singh, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1030 · Cited by 61
Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era
Porta‐Pardo, E., Valencia, A., & Godzik, A. (2020) · FEBS Letters · doi:10.1002/1873-3468.13781 · Cited by 57
Meta-Analysis and Systematic Review of the Genomics of Mucosal Melanoma
Broit, N., Johansson, P. A., Rodgers, C. B., Walpole, S., Newell, F., Hayward, N. K., & Pritchard, A. L. (2021) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-20-0839 · Cited by 55
Why Are Some Driver Mutations Rare?
Nussinov, R., Tsai, C., & Jang, H. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.10.003 · Cited by 47
Nussinov, R., Tsai, C., & Jang, H. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.01.002 · Cited by 42
Multiplexed assays of variant effect for clinical variant interpretation
McEwen, A. E., Tejura, M., Fayer, S., Starita, L. M., & Fowler, D. M. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00870-x · Cited by 52
Targeted insertional mutagenesis libraries for deep domain insertion profiling
Coyote‐Maestas, W., Nedrud, D. M., Okorafor, S., He, Y., & Schmidt, D. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1110 · Cited by 34
Pethe, M. A., Rubenstein, A. B., & Khare, S. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805256116 · Cited by 36
Insights into protein structure, stability and function from saturation mutagenesis
Gupta, K., & Varadarajan, R. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.02.006 · Cited by 34
Allostery and Epistasis: Emergent Properties of Anisotropic Networks
Campitelli, P., & Ozkan, S. B. (2020) · Entropy · doi:10.3390/e22060667 · Cited by 26
High throughput and quantitative enzymology in the genomic era
Mokhtari, D. A., Appel, M. J., Fordyce, P. M., & Herschlag, D. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.07.010 · Cited by 26
McFall, T. B., & Stites, E. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110096 · Cited by 30
A saturation-mutagenesis analysis of the interplay between stability and activation in Ras
Hidalgo, F., Nocka, L. M., Shah, N. H., Gorday, K., Latorraca, N. R., Bandaru, P., Templeton, S., Lee, D., Karandur, D., Pelton, J. G., et al. (2022) · eLife · doi:10.7554/elife.76595 · Cited by 27
Self-citationShah, N. H., & Kuriyan, J. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0175-9 · Cited by 27
Self-citationUltra-fast protein structure prediction to capture effects of sequence variation in mutation moviesPreprint
Weißenow, K., Heinzinger, M., Steinegger, M., & Rost, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.14.516473 · Cited by 29
RAS‐targeted cancer therapy: Advances in drugging specific mutations
Liu, C., Ye, D., Yang, H., Chen, X., Su, Z., Li, X., Ding, M., & Liu, Y. (2023) · MedComm · doi:10.1002/mco2.285 · Cited by 25
Mavor, D., Barlow, K. A., Asarnow, D. E., Birman, Y., Britain, D., Chen, W., Green, E. M., Kenner, L. R., Mensa, B., Morinishi, L. S., et al. (2018) · Biology Open · doi:10.1242/bio.036103 · Cited by 29
Self-citationzu Belzen, J. U., Buergel, T., Holderbach, S., Bubeck, F., Adam, L., Gandor, C. L. A., Klein, M., Mathony, J., Pfuderer, P. L., Platz, L., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0049-9 · Cited by 26
Genome-wide prediction of disease variants with a deep protein language model
Brandes, N., Goldman, G., Wang, C. H., Ye, C. J., & Ntranos, V. (2022) · doi:10.1101/2022.08.25.505311 · Cited by 24
Genome-wide base editor screen identifies regulators of protein abundance in yeast
Schubert, O. T., Bloom, J. S., Sadhu, M. J., & Kruglyak, L. (2022) · eLife · doi:10.7554/elife.79525 · Cited by 21
Deep generative models of genetic variation capture mutation effectsPreprint
Riesselman, A. J., Ingraham, J., & Marks, D. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/235655 · Cited by 29
Rubin, A. F., Min, J., Rollins, N. J., Da, E. Y., Esposito, D., Harrington, M., Stone, J., Bianchi, A. H., Dias, M., Frazer, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.29.470445 · Cited by 25
A complete allosteric map of a GTPase switch in its native cellular network
Mathy, C. J. P., Mishra, P., Flynn, J. M., Perica, T., Mavor, D., Bolon, D. N. A., & Kortemme, T. (2023) · Cell Systems · doi:10.1016/j.cels.2023.01.003 · Cited by 20
Self-citationHalder, A., Anto, A., Subramanyan, V., Bhattacharyya, M., Vishveshwara, S., & Vishveshwara, S. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.596945 · Cited by 21
Self-citationSaturation mutagenesis of a predicted ancestral Syk‐family kinase
Hobbs, H. T., Shah, N. H., Shoemaker, S. R., Amacher, J. F., Marqusee, S., & Kuriyan, J. (2022) · Protein Science · doi:10.1002/pro.4411 · Cited by 21
Self-citationScheele, R. A., Lindenburg, L. H., Petek, M., Schober, M., Dalby, K. N., & Hollfelder, F. (2022) · Nature Communications · doi:10.1038/s41467-022-28396-4 · Cited by 20
Badonyi, M., & Marsh, J. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63234-3 · Cited by 19
Extracting phylogenetic dimensions of coevolution reveals hidden functional signals
Colavin, A., Atolia, E., Bitbol, A., & Huang, K. C. (2022) · Scientific Reports · doi:10.1038/s41598-021-04260-1 · Cited by 21
Shanker, V. R., Bruun, T. U. J., Hie, B., & Kim, P. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.19.572475 · Cited by 18
Emerging maps of allosteric regulation in cellular networks
Mathy, C. J. P., & Kortemme, T. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102602 · Cited by 20
Self-citationBalance between promiscuity and specificity in phage λ host range
Andrews, B. L., & Fields, S. (2021) · The ISME Journal · doi:10.1038/s41396-021-00912-2 · Cited by 16
Sex and smoking bias in the selection of somatic mutations in human bladder
Calvet, F., Martínez-Illescas, R. B., Muiños, F., Tretiakova, M., Latorre-Esteves, E. S., Fredrickson, J., Andrianova, M. A., Pellegrini, S., Huber, A. R., Ramis-Zaldivar, J. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09521-x · Cited by 21
Padigepati, S. R., Stafford, D. A., Tan, C. A., Silvis, M. R., Jamieson, K., Keyser, A., Nunez, P. A. C., Nicoludis, J. M., Manders, T. R., Frésard, L., et al. (2024) · Human Genetics · doi:10.1007/s00439-024-02691-0 · Cited by 12
Tripathi, S., Dsouza, N. R., Urrutia, R. A., & Zimmermann, M. T. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa972 · Cited by 11
Kwon, J. J., Dilly, J., Liu, S., Kim, E., Bian, Y., Dharmaiah, S., Tran, T. H., Kapner, K. S., Ly, S. H., Yang, X., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.22.618529 · Cited by 13
Zhao, Y., Lan, T., Zhong, G., Hagen, J., Pan, H., Chung, W. K., & Shen, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-59937-2 · Cited by 11
Regulation of the Small GTPase Ras and Its Relevance to Human Disease
Kulhanek, K. R., Roose, J. P., & Rubio, I. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1190-6_2 · Cited by 10
Bao, H., Wang, W., Sun, H., & Chen, J. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2213355 · Cited by 10
Global Dynamics Renders Protein Sites with High Functional Response
Kutlu, Y., Ben‐Tal, N., & Haliloǧlu, T. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c02511 · Cited by 9
Moesslacher, C. S., Kohlmayr, J. M., & Stelzl, U. (2021) · Microbial Cell · doi:10.15698/mic2021.08.756 · Cited by 9
HPLC Method to Resolve, Identify and Quantify Guanine Nucleotides Bound to the GTPase Ras
Hannan, J. P., Swisher, G. H., Martyr, J. G., Cordaro, N. J., Erbse, A. H., & Falke, J. J. (2021) · Analytical Biochemistry · doi:10.1016/j.ab.2021.114338 · Cited by 8
Global mapping of the energetic and allosteric landscapes of protein binding domainsPreprint
Faure, A. J., Domingo, J., Schmiedel, J. M., Hidalgo-Carcedo, C., Diss, G., & Lehner, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.14.460249 · Cited by 9
Simon, J. J., Fowler, D. M., & Maly, D. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02456-7 · Cited by 10
Fitness and Functional Landscapes of theE. coliRNase III Genernc
Weeks, R., & Ostermeier, M. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad047 · Cited by 12
Functional assays for transcription mechanisms in high-throughput
Qiu, C., & Kaplan, C. D. (2019) · Methods · doi:10.1016/j.ymeth.2019.02.017 · Cited by 9
Proteomic analyses identify targets, pathways, and cellular consequences of oncogenic KRAS signaling
Kabella, N., Bayer, F. P., Stamatiou, K., Abele, M., Sakhteman, A., Chang, Y.-C., Wagner, V., Gabriel, A., Krumm, J., Reinecke, M., et al. (2025) · Science Signaling · doi:10.1126/scisignal.adt6552 · Cited by 8
Why variant effect predictors and multiplexed assays agree and disagree
Livesey, B., & Marsh, J. A. (2026) · Nature Communications · doi:10.1038/s41467-026-77211-x · Cited by 8
Fu, Y., Bedő, J., Papenfuss, A. T., & Rubin, A. F. (2022) · GigaScience · doi:10.1093/gigascience/giad073 · Cited by 5
Sequence Determinants of Allosteric Back-to-front Control of the Arf Nucleotide Switch
Hantman, N., Koduru, T. N., Peters, E. V., Jaworek, M. W., McCallum, S. A., Gillilan, R. E., Winter, R., Cherfils, J., & Royer, C. A. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169361 · Cited by 5
M-Ras distinct activation scenarios: A mechanistic outlook and targeting
Xu, L., Liu, Y., Jang, H., & Nussinov, R. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.025 · Cited by 4
Deep mutational scanning to probe specificity determinants in proteins
Bhowmick, J., Chandra, S., & Varadarajan, R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99533-7.00005-4 · Cited by 5
Optimized conditions for GTP loading of Ras
Vish, K. J., Paul, M. E., Rollins, A. P., & Boggon, T. J. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110923 · Cited by 4
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutationsPreprint
Livesey, B., & Marsh, J. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/855957 · Cited by 4
Random Embeddings and Linear Regression can Predict Protein FunctionPreprint
Lu, T., Lu, A. X., & Moses, A. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14661 · Cited by 5
Masso, M., Bansal, A., Prem, P., Gajjala, A., & Vaisman, I. I. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e01884 · Cited by 3
Multiple conserved states characterize the twist landscape of the bacterial actin homolog MreB
Knapp, B. D., Ward, M. D., Bowman, G. R., Shi, H., & Huang, K. C. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.10.008 · Cited by 3
Sparse networks of conformational fluctuations communicate signals within proteinsPreprint
Trenfield, K., & Lin, M. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.28.656549 · Cited by 3
Bhattarai, N., & Buck, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103183 · Cited by 2
Discovering mechanisms of human genetic variation and controlling cell states at scale
Frenkel, M. P. C., & Raman, S. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.03.010 · Cited by 2
Insights into direct KRAS inhibition strategies for cancer treatment
Li, T., Gu, C., Zhou, C., Mao, C., Yang, K., Xu, J., Lu, T., & Chen, J. (2024) · Future Medicinal Chemistry · doi:10.1080/17568919.2024.2424149 · Cited by 3
Michaelis-Menten kinetics of RasGAP proteins by a rapid fluorescence-based assay
Paul, M. E., Vish, K. J., & Boggon, T. J. (2025) · Methods · doi:10.1016/j.ymeth.2025.09.003 · Cited by 2
Mutagenesis-Visualization: Analysis of Site-Saturation Mutagenesis Datasets in PythonPreprint
Hidalgo, F., Templeton, S., Gallegos, C. O., & Wang, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.08.463725 · Cited by 2
Simon, J. J., Fowler, D. M., & Maly, D. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.19.590094 · Cited by 2
Gibney, T. V., Latifi, L., Mardick, J. I., Rasmussen, N. R., Lyons, M. C., Sundaram, M. V., Reiner, D. J., & Pani, A. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648432 · Cited by 2
Galeazzi, F. M., da Silva, G. M., Arantes, P. R., Varghese, I., Shukla, A., & Rubenstein, B. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653048 · Cited by 2
Ghose, D. A., Soderstrom, C. B. W., Mahoney, E. M., & Laub, M. T. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116446 · Cited by 1
Polarized SOS activity orchestrates FGF-directed cell migration in vivo
Gibney, T. V., Latifi, L., Mardick, J. I., Rasmussen, N. R., Lyons, M. C., Sundaram, M., Reiner, D. J., & Pani, A. M. (2026) · Current Biology · doi:10.1016/j.cub.2026.03.021 · Cited by 3
Metadata
- Authors
- Bandaru, P., Shah, N. H., Bhattacharyya, M., Barton, J. P., Kondo, Y., Cofsky, J. C., Gee, C. L., Chakraborty, A. K., Kortemme, T., Ranganathan, R., & Kuriyan, J.
- Affiliations
- QB3 (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- University of California, San Francisco (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Ranganathan, Rama
- Kuriyan, John
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2017-07-07
- Volume
- 6
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC5538825
- OpenAlex
- W2727251566
- Cited by
- 117
- References
- 91
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 7 · 2023: 14 · 2022: 16 · 2021: 19 · 2020: 11 · 2019: 11 · 2018: 13 · 2017: 3
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Damon Runyon Cancer Research Foundation
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- Ras proteins, deep mutational scanning, saturation mutagenesis, GTPase-activating protein, nucleotide exchange factor, mutational tolerance, sequence conservation
- MeSH terms
- DNA Mutational Analysis, Humans, Mutagenesis, Conserved Sequence, ras Proteins, Evolution, Molecular, Protein Interaction Maps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review