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Computational investigation of missense somatic mutations in cancer and potential links to pH-dependence and proteostasis
Open accessPreprint — published as 10.1371/journal.pone.0314022
Abstract
Metabolic changes during tumour development lead to acidification of the extracellular environment and a smaller increase of intracellular pH. Searches for somatic missense mutations that could reveal adaptation to altered pH have focussed on arginine to histidine changes, part of a general arginine depletion that originates from DNA mutational mechanisms. Analysis of mutations to histidine, potentially a simple route to the introduction of pH-sensing, shows no clear biophysical separation overall of subsets that are more and less frequently mutated in cancer genomes. Within the more frequently mutated subset, individual sites predicted to mediate pH-dependence upon mutation include NDST1 (a Golgi-resident heparan sulphate modifying enzyme), the HLA-C chain of MHCI complex, and the water channel AQP-7. Arginine depletion is a general feature that persists in the more frequently mutated subset, and is complemented by over-representation of mutations to lysine. Arginine to lysine balance is a known factor in determining protein solubility, with higher lysine content being more favourable. Proteins with greater change in arginine to lysine balance are enriched for cell periphery location, where proteostasis is likely to be challenged in tumour cells. Somatic missense mutations in a cancer genome number only in the 10s typically, although can be much higher. Whether the altered arginine to lysine balance is of sufficient scale to play a role in tumour development is unknown.
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![Fig 5 Molecular environments of selected mutations to His. (A) PCDHGB4-D268H: left is a protein-sol image of predicted pKas around the wild type calcium binding site (in the absence of calcium ion), with colour scale by destabilisation (red) through ΔpKa close to zero (white) and stabilisation (red). Right is the equivalent plot for the D268H mutant, with a switch from destabilising at 268 to stabilising. (B) NDST1-Q793H: left is the sulfotransferase domain from a structure of the bifunctional Golgi enzyme (8ccy) [50], with ADP (cyan) marking the active site. Q793 is indicated with a rectangle, and that region expanded on the right, with a protein-sol pKa prediction showing destabilised Q793H and neighbouring H627. (C) HLAC-Y195H: mutation sites (instances ≥ 17 in COSMIC, green) are displayed in the peptide binding domain of structure 6pag [51], with bound peptide shown in purple. Mutations other than Y195H are S48A, L171W, T187M/T187P, A97T, L180R, A176V, L119I, all adjacent to the peptide binding groove, and R155S, D114A in this domain but not adjacent to the peptide binding groove. (D) AQP7-Y115H: left shows wild type Y115 in the AlphaFold2 protomer model, and right the modelled Y115H mutation. In both panels the neighbouring residue H92 is shown.](/paper-figures/f48c091257f442b98dde2c243dfa8e49/computational-investigation-of-missense-somatic-mutations-in-cancer-and-figure-5.jpg)




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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Solubility-Weighted Index: fast and accurate prediction of protein solubility
Bhandari, B. K., Gardner, P. P., & Lim, C. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa578 · Cited by 97
Research works printed but not linked: 78
- 10.1038/nrc.2017.77 · DOI
- 10.1038/nrc3366 · DOI
- 10.1098/rstb.2013.0098 · DOI
- 10.1038/s41568-023-00628-9 · DOI
- 10.1007/s00424-023-02876-y · DOI
- 10.1016/j.it.2023.08.008 · DOI
- 10.1039/c8ra02095g · DOI
- 10.1158/0008-5472.CAN-18-1604 · DOI
- 10.1146/annurev-biophys-050511-102349 · DOI
- 10.1093/nar/gkad986 · DOI
- 10.1016/j.bbadis.2015.11.006 · DOI
- 10.1371/journal.pone.0183273 · DOI
- 10.3390/biom7030049 · DOI
- 10.3390/cancers13246274 · DOI
- 10.1126/scisignal.aam9931 · DOI
- 10.1021/acs.biochem.1c00059 · DOI
- 10.1083/jcb.201712041 · DOI
- 10.1021/ct100578z · DOI
- 10.1093/bioinformatics/btaa646 · DOI
- 10.1038/s41598-017-15997-z · DOI
- 10.1016/j.sbi.2022.102498 · DOI
- 10.1021/acs.jcim.3c00165 · DOI
- 10.1110/ps.04785604 · DOI
- 10.3389/fmolb.2022.834011 · DOI
- 10.1093/nar/gky1049 · DOI · Paper page
- 10.1126/science.1260419 · DOI · Paper page
- 10.1093/nar/gkr407 · DOI
- 10.1093/bioinformatics/bth456 · DOI · Paper page
- 10.1002/elps.1150181505 · DOI
- 10.1002/pro.4769 · DOI
- 10.1002/path.5912 · DOI
- 10.1111/php.12377 · DOI · Paper page
- 10.3389/fgene.2012.00330 · DOI
- 10.1186/s13073-023-01261-9 · DOI · Paper page
- 10.1042/BST20210277 · DOI
- 10.1016/S0065-230X(06)97001-8 · DOI
- 10.1186/s13059-015-0864-1 · DOI
- 10.1016/bs.ctdb.2021.12.006 · DOI
- 10.1038/s41586-019-1089-3 · DOI · Paper page
- 10.7554/eLife.20930 · DOI
- 10.1038/s41467-024-45419-4 · DOI
- 10.1038/s41467-021-22359-x · DOI
- 10.3389/fgene.2021.736156 · DOI
- 10.3727/096504018X15478559215014 · DOI
- 10.3389/fcell.2019.00093 · DOI
- 10.26355/eurrev_201907_18443 · DOI
- 10.3390/ijms23084157 · DOI
- 10.2174/138161212799504821 · DOI
- 10.3389/fonc.2022.943806 · DOI
- 10.1016/j.str.2019.11.011 · DOI
- 10.1038/s41467-018-07176-z · DOI
- 10.1038/nature10370 · DOI
- 10.1038/s41467-023-40058-7 · DOI
- 10.1042/BCJ20200311 · DOI
- 10.7554/eLife.29002 · DOI
- 10.1083/jcb.201302131 · DOI
- 10.1074/jbc.M116.736215 · DOI
- 10.1007/s12032-009-9343-4 · DOI
- 10.1073/pnas.1209312109 · DOI
- 10.1038/s41586-023-06328-6 · DOI · Paper page
- 10.1021/mp4004749 · DOI
- 10.1093/bioinformatics/btx345 · DOI
- 10.3389/fcell.2023.1143532 · DOI
- 10.1007/978-1-4939-3756-1_2 · DOI
- 10.3390/cells11162556 · DOI
- 10.1016/j.tcb.2015.10.011 · DOI
- 10.1073/pnas.1910444117 · DOI
- 10.1016/j.ygeno.2018.05.013 · DOI
- 10.6061/clinics/2021/e2052 · DOI
- 10.1016/j.csbj.2021.02.012 · DOI
- 10.1007/s00232-011-9381-7 · DOI
- 10.1038/s41467-019-10920-8 · DOI
- 10.1038/s41467-023-36496-y · DOI
- 10.3390/membranes12040393 · DOI
- 10.1126/science.aab4082 · DOI · Paper page
- 10.3390/cancers12102760 · DOI
- 10.1016/j.xphs.2022.10.009 · DOI
- 10.1016/j.celrep.2023.112601 · DOI
Metadata
- Authors
- Sallah, S., & Warwicker, J.
- Affiliations
- University of Manchester (GB)
- Corresponding author
- Warwicker, Jim
- Venue
- PLoS ONE
- Publisher
- openRxiv
- Published
- 2024-07-19
- Volume
- 19
- Issue
- 11
- Pages
- e0314022-e0314022
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC11575792
- OpenAlex
- W4404522313
- Cited by
- 3
- References
- 2
- Citations by year
- 2025: 3
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS ONE (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- Funders
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (BB/V006592/1, BB/V0065921/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- somatic missense mutations, cancer, extracellular pH, intracellular pH, arginine deprivation, histidine mutations, lysine mutations, protein solubility, proteostasis, pH sensing, NDST1, HLA-C, AQP7, Golgi apparatus, tumor microenvironment
- MeSH terms
- Proteostasis, Arginine, Histidine, Humans, Hydrogen-Ion Concentration, Lysine, Neoplasms, Computational Biology, Mutation, Missense
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