mCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance
Open accessAbstract
The ability to predict how a mutation affects ligand binding is an essential step in understanding, anticipating and improving the design of new treatments for drug resistance, and in understanding genetic diseases. Here we present mCSM-lig, a structure-guided computational approach for quantifying the effects of single-point missense mutations on affinities of small molecules for proteins. mCSM-lig uses graph-based signatures to represent the wild-type environment of mutations, and small-molecule chemical features and changes in protein stability as evidence to train a predictive model using a representative set of protein-ligand complexes from the Platinum database. We show our method provides a very good correlation with experimental data (up to ρ = 0.67) and is effective in predicting a range of chemotherapeutic, antiviral and antibiotic resistance mutations, providing useful insights for genotypic screening and to guide drug development. mCSM-lig also provides insights into understanding Mendelian disease mutations and as a tool for guiding protein design. mCSM-lig is freely available as a web server at http://structure.bioc.cam.ac.uk/mcsm_lig.
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
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?
The outliers usually correspond to mutations with extreme experimental values.
“Consistent with the observation of Pires et al. (Pires et al., 2016), the outliers usually correspond to those mutations with extreme experimental values (highly decreasing/increasing binding affinity)”
From MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions (2020) · cites this paper as Pires et al., 2016
The graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability, dynamics and interactions with other molecules.
“Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability (20,26), dynamics (27) and interactions with other molecules (20,28–34).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [31]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711 · see citing works
Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00104 · Cited by 5,995 · see citing works
Self-citationThe FoldX web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki387 · Cited by 2,759 · see citing works
Tackling antibiotic resistance: the environmental framework
Berendonk, T. U., Manaia, C. M., Merlin, C., Fatta‐Kassinos, D., Cytryn, E. J., Walsh, F., Bürgmann, H., Sørum, H. R., Norström, M., Pons, M., et al. (2015) · Nature Reviews Microbiology · doi:10.1038/nrmicro3439 · Cited by 2,365 · see citing works
I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure
Capriotti, E., Fariselli, P., & Casadio, R. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki375 · Cited by 2,058 · see citing works
Exome sequencing identifies the cause of a mendelian disorder
Ng, S. B., Buckingham, K. J., Lee, C., Bigham, A. W., Tabor, H. K., Dent, K. M., Huff, C. D., Shannon, P. T., Jabs, E. W., Nickerson, D. A., et al. (2010) · Nature Genetics · doi:10.1038/ng.499 · Cited by 1,957 · see citing works
mCSM: Predicting the effects of mutations in proteins using graph-based signatures
Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt691 · Cited by 1,066 · see citing works
Self-citationPires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku411 · Cited by 932 · see citing works
Self-citationSoverini, S., Hochhaus, A., Nicolini, F. E., Gruber, F., Lange, T., Saglio, G. N., Pane, F., Müller, M. C., Ernst, T., Rosti, G., et al. (2011) · Blood · doi:10.1182/blood-2010-12-326405 · Cited by 593 · see citing works
ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions
Kumar, M. D. S. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj103 · Cited by 416 · see citing works
Dehouck, Y., Grosfils, A., Folch, B., Gilis, D., Bogaerts, P., & Rooman, M. J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp445 · Cited by 409 · see citing works
BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations
Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt450 · Cited by 338 · see citing works
Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works
Perryman, A. L., Lin, J., & McCammon, J. A. (2004) · Protein Science · doi:10.1110/ps.03468904 · Cited by 233 · see citing works
Topham, C. M., Srinivasan, N., & Blundell, T. L. (1997) · Protein Engineering Design and Selection · doi:10.1093/protein/10.1.7 · Cited by 209 · see citing works
Self-citationTan, K. P., Nguyen, T., Patel, S., Varadarajan, R., & Madhusudhan, M. S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt503 · Cited by 202 · see citing works
Abed, Y., Baz, M., & Boivin, G. (2006) · Antiviral Therapy · doi:10.1177/135965350601100804 · Cited by 166 · see citing works
Current Perspectives on HIV-1 Antiretroviral Drug Resistance
Iyidogan, P., & Anderson, K. S. (2014) · Viruses · doi:10.3390/v6104095 · Cited by 159 · see citing works
Flexibility and small pockets at protein–protein interfaces: New insights into druggability
Jubb, H. C., Blundell, T. L., & Ascher, D. B. (2015) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2015.01.009 · Cited by 154 · see citing works
Self-citationWang, J., Ma, C., Fiorin, G., et al. (2011) · Journal of the American Chemical Society · doi:10.1021/ja204969m · Cited by 141 · see citing works
Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw458 · Cited by 134 · see citing works
Self-citationPhelan, J. E., Coll, F., McNerney, R., Ascher, D. B., Pires, D. E. V., Furnham, N., Coeck, N., Hill-Cawthorne, G. A., Nair, M. B., Mallard, K., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0575-9 · Cited by 118 · see citing works
Self-citationPotent hepatitis C inhibitors bind directly to NS5A and reduce its affinity for RNA
Ascher, D. B., Wielens, J., Nero, T. L., Doughty, L., Morton, C. J., & Parker, M. W. (2014) · Scientific Reports · doi:10.1038/srep04765 · Cited by 116 · see citing works
Self-citationCSM-lig: a web server for assessing and comparing protein-small molecule affinities
Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw390 · Cited by 115 · see citing works
Self-citationAlbiston, A. L., Morton, C. J., Ng, H. L., Pham, V., Yeatman, H. R., Ye, S., Fernando, R. N., Bundel, D. D., Ascher, D. B., Mendelsohn, F. A. O., et al. (2008) · The FASEB Journal · doi:10.1096/fj.08-112227 · Cited by 110 · see citing works
Self-citationNemethova, M., Radvánszky, J., Kádaši, Ľ., et al. (2015) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.60 · Cited by 102 · see citing works
Pires, D. E. V., Chen, J., Blundell, T. L., & Ascher, D. B. (2016) · Scientific Reports · doi:10.1038/srep19848 · Cited by 102 · see citing works
Self-citationGermline Mutations in the CDKN2B Tumor Suppressor Gene Predispose to Renal Cell Carcinoma
Jafri, M., Wake, N. C., Ascher, D. B., Pires, D. E. V., Gentle, D., Morris, M. R., Rattenberry, E., Simpson, M. A., Trembath, R. C., Weber, A. P., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-14-1096 · Cited by 95 · see citing works
Self-citationPires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku966 · Cited by 91 · see citing works
Self-citationMechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions
Betts, M. J., Lu, Q., Jiang, Y., Drusko, A., Wichmann, O., Utz, M., Valtierra-Gutiérrez, I. A., Schlesner, M., Jäger, N., Jones, D., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1094 · Cited by 90 · see citing works
Development of cognitive enhancers based on inhibition of insulin-regulated aminopeptidase
Chai, S. Y., Yeatman, H. R., Parker, M. W., Ascher, D. B., Thompson, P. E., Mulvey, H. T., & Albiston, A. L. (2008) · BMC Neuroscience · doi:10.1186/1471-2202-9-s2-s14 · Cited by 84 · see citing works
Self-citationGibbons, D. L., Pricl, S., Posocco, P., Laurini, E., Fermeglia, M., Sun, H., Talpaz, M., Donato, N. J., & Quintás‐Cardama, A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1321173111 · Cited by 77 · see citing works
Usher, J. L., Ascher, D. B., Pires, D. E. V., et al. (2014) · JIMD Reports · doi:10.1007/8904_2014_380 · Cited by 56 · see citing works
Self-citationABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex
Anand, P., Nagarajan, D., Mukherjee, S., & Chandra, N. R. (2014) · F1000Research · doi:10.12688/f1000research.5165.1 · Cited by 47 · see citing works
Regulation of Insulin-Regulated Membrane Aminopeptidase Activity by Its C-Terminal Domain
Ascher, D. B., Cromer, B. A., Morton, C. J., et al. (2011) · Biochemistry · doi:10.1021/bi101893w · Cited by 30 · see citing works
Self-citationABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex
Anand, P., Nagarajan, D., Mukherjee, S., et al. (2014) · F1000Research · doi:10.12688/f1000research.5165.2 · Cited by 29 · see citing works
Sigurdardottir, A., Winter, A., Sobkowicz, A., Fragai, M., Chirgadze, D. Y., Ascher, D. B., Blundell, T. L., & Gherardi, E. (2015) · Chemical Science · doi:10.1039/c5sc02155c · Cited by 25 · see citing works
Self-citationYe, S., Chai, S. Y., Lew, R. A., Ascher, D. B., Morton, C. J., Parker, M. W., & Albiston, A. L. (2008) · Biochemistry and Cell Biology · doi:10.1139/o08-037 · Cited by 22 · see citing works
Self-citationPDBest: a user–friendly platform for manipulating and enhancing protein structures
Gonçalves, W. R. S., Gonçalves-Almeida, V. M., Arruda, A. L., et al. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv223 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Ascher D. B. et al. In Multifaceted Roles of Crystallography in Modern Drug Discovery NATO Science for Peace and Security Series A: Chemistry and Biology (eds Giovanna Scapin Disha Patel & Eddy Arnold) Ch. 12, 141– 163 (Springer: Netherlands,, 2015).
- Rasmussen C. E. & Williams C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). (The MIT Press, 2005).
Cited by
Sorted by citations
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
Rodrigues, C. H., Pires, D. E., & Ascher, D. B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky300 · Cited by 1,086 · see citing works
Self-citationmCSM-PPI2: predicting the effects of mutations on protein–protein interactions
Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz383 · Cited by 369 · see citing works
Self-citationMutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions
Zhang, N., Chen, Y., Lu, H., Zhao, F., Alvarez, R. V., Goncearenco, A., Panchenko, A. R., & Li, M. (2020) · iScience · doi:10.1016/j.isci.2020.100939 · Cited by 199 · see citing works
mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions
Pires, D. E. V., & Ascher, D. B. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx236 · Cited by 127 · see citing works
Self-citationKinact: a computational approach for predicting activating missense mutations in protein kinases
Rodrigues, C. H., Ascher, D. B., & Pires, D. E. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky375 · Cited by 31 · see citing works
Self-citationRecent advances in automated protein design and its future challenges
Setiawan, D., Brender, J., & Zhang, Y. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1465922 · Cited by 26 · see citing works
- Authors
- Pires, D. E. V., Blundell, T. L., & Ascher, D. B.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Pires, Douglas E. V.
- Blundell, Tom L.
- Ascher, David B.
- Venue
- Scientific Reports
- Published
- 2016-07-07
- Volume
- 6
- Issue
- 1
- Pages
- 29575-29575
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4935856
- OpenAlex
- W2475308546
- Cited by
- 143
- References
- 39
- Citations by year
- 2026: 10 · 2025: 7 · 2024: 9 · 2023: 13 · 2022: 14 · 2021: 22 · 2020: 24 · 2019: 19 · 2018: 16 · 2017: 7 · 2016: 2
- FWCI
- 13.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Americanae (AECID Library) (submittedVersion, other-oa)
- Arca - Repositório Institucional da Fiocruz (submittedVersion, other-oa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Wellcome Trust
- Research Councils UK
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais (MR/M026302/1)
- Fundação Oswaldo Cruz
- Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa
- Medical Research Council (MR/M026302/1, MR/N501864/1)
- Biotechnology and Biological Sciences Research Council (1103577)
- National Health and Medical Research Council (1072476)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- missense mutations, protein-ligand binding affinity, drug resistance, genetic disorders, structure-based prediction, protein stability, chemoresistance, antiviral resistance, antibiotic resistance, monogenic disorders, protein design, web server
- MeSH terms
- Alkaptonuria, Computer Graphics, Drug Resistance, Humans, Proteins, Software, Databases, Factual, Genes, abl, Point Mutation, Drug Resistance, Neoplasm, Computational Biology, Mutation, Missense, Drug Resistance, Viral, Databases, Protein, HIV Reverse Transcriptase, Small Molecule Libraries
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
