The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
High-Resolution Epitope Mapping of hGH-Receptor Interactions by Alanine-Scanning Mutagenesis
Abstract
A strategy, called alanine-scanning mutagenesis, was used to identify specific side chains in human growth hormone (hGH) that strongly modulate binding to the hGH receptor cloned from human liver. Single alanine mutations (62 in total) were introduced at every residue contained within the three discontinuous segments of hGH (residues 2 to 19, 54 to 74, and 167 to 191) that have been implicated in receptor recognition. The alanine scan revealed a cluster of a dozen large side chains that when mutated to alanine each showed more than a four times lower binding affinity to the hGH receptor. Many of these residues that promote binding to the hGH receptor are altered in homologs of hGH (such as placental lactogens and prolactins) that do not bind tightly to the hGH receptor. The overall folding of these mutant proteins was indistinguishable from that of the wild-type hGH, as determined by strong cross-reactivities with seven different conformationally sensitive monoclonal antibodies. The alanine scan also identified at least one side chain, Glu 174 , that hindered binding because when it was mutated to alanine the receptor affinity increased by more than a factor of four.
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
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.
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
A rapid alkaline extraction procedure for screening recombinant plasmid DNA
Birnboim, H. C., & Doly, J. (1979) · Nucleic Acids Research · doi:10.1093/nar/7.6.1513 · Cited by 14,686
Cited by
Anatomy of hot spots in protein interfaces
Bogan, A. A., & Thorn, K. S. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.1843 · Cited by 1,988
Massova, I., & Kollman, P. A. (2000) · Perspectives in Drug Discovery and Design · doi:10.1023/a:1008763014207 · Cited by 1,015
Hot spots—A review of the protein–protein interface determinant amino‐acid residues
Moreira, I. S., Fernandes, P. A., & Ramos, M. J. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21396 · Cited by 760
Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?
Keskin, O., Gursoy, A., Ma, B., & Nussinov, R. (2008) · Chemical Reviews · doi:10.1021/cr040409x · Cited by 697
High-resolution mapping of protein sequence-function relationships
Fowler, D. M., Araya, C. L., Fleishman, S. J., Kellogg, E. H., Stephany, J. J., Baker, D., & Fields, S. (2010) · Nature Methods · doi:10.1038/nmeth.1492 · Cited by 516
Microtubule interaction site of the kinesin motor
Woehlke, G., Ruby, A. K., Hart, C. L., Ly, B., Hom-Booher, N., & Vale, R. D. (1997) · Cell · doi:10.1016/s0092-8674(00)80329-3 · Cited by 386
Insights into Cytokine–Receptor Interactions from Cytokine Engineering
Spangler, J. B., Moraga, I., Mendoza, J. L., & Garcia, K. C. (2015) · Annual Review of Immunology · doi:10.1146/annurev-immunol-032713-120211 · Cited by 304
A simple definition of structural regions in proteins and its use in analyzing interface evolution
Levy, E. D. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.09.028 · Cited by 254
Systematic Targeting of Protein–Protein Interactions
Modell, A. E., Blosser, S. L., & Arora, P. S. (2016) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2016.05.008 · Cited by 184
Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors
Jochim, A. L., & Arora, P. S. (2010) · ACS Chemical Biology · doi:10.1021/cb1001747 · Cited by 191
State-of-the-art strategies for targeting protein–protein interactions by small-molecule inhibitors
Sheng, C., Dong, G., Miao, Z., Zhang, W., & Wang, W. (2015) · Chemical Society Reviews · doi:10.1039/c5cs00252d · Cited by 165
The power of multiplexed functional analysis of genetic variants
Gasperini, M., Starita, L., & Shendure, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.135 · Cited by 160
Tang, X., & Bruce, J. E. (2010) · Molecular BioSystems · doi:10.1039/b920876c · Cited by 143
Kanaya, S., Katsuda-Nakai, C., & Ikehara, M. (1991) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)99002-2 · Cited by 116
Lise, S., Archambeau, C., Pontil, M., & Jones, D. T. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-365 · Cited by 107
Bumbaca Yadav, D., Sharma, V. K., Boswell, C. A., Hotzel, I., Tesar, D., Shang, Y., Ying, Y., Fischer, S. K., Grogan, J. L., Chiang, E. Y., et al. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.692434 · Cited by 82
Cardoso, R., Love, R., Nilsson, C. L., Bergqvist, S., Nowlin, D., Yan, J., Liu, K. K. ‐., Zhu, J., Chen, P., Deng, Y., et al. (2012) · Protein Science · doi:10.1002/pro.2172 · Cited by 79
Plucking the high hanging fruit: A systematic approach for targeting protein–protein interactions
Raj, M., Bullock, B. N., & Arora, P. S. (2013) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2012.11.023 · Cited by 66
Starita, L. M., & Fields, S. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top077503 · Cited by 32
Engineering an interfacial zinc site to increase hormone-receptor affinity
Matthews, D. J., & Wells, J. A. (1994) · Chemistry & Biology · doi:10.1016/1074-5521(94)90037-x · Cited by 12
Self-citationPakuła, K., Sequeiros-Borja, C., Biała-Leonhard, W., Pawela, A., Banasiak, J., Bailly, A., Radom, M., Geisler, M., Brezovsky, J., & Jasiński, M. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04751-6 · Cited by 11
Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA
Chandra, S., Manjunath, K., Asok, A., & Varadarajan, R. (2023) · Protein Science · doi:10.1002/pro.4580 · Cited by 7
Identification of PS1/gamma-secretase and glutamate transporter GLT-1 interaction sites
Perrin, F., Sinha, P., Mitchell, S. P. C., Sadek, M., Maesako, M., & Berezovska, O. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.107172 · Cited by 3
Metadata
- Authors
- Cunningham, B. C., & Wells, J. A.
- Venue
- Science
- Published
- 1989-06-02
- Volume
- 244
- Issue
- 4908
- Pages
- 1081-1085
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2042655455
- Cited by
- 1,367
- References
- 1
- FWCI
- 27.03
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.