CoChat
Sign Up
Cited by 11Genomics and Rare DiseasesCancer Genomics and DiagnosticsProtein Structure and Dynamics

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations

Open access
Wang, Boshen, Lei, Xue, Tian, Wei, Perez-Rathke, Alan, Tseng, Yan-Yuan, Liang, Jie·2023·Briefings in Bioinformatics
DOI 10.1093/bib/bbad206PMC PMC10359089

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/bib/bbad206

Cites

4 of 4 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Flagged first, then by citations

Research works printed but not linked: 76

  1. Yang, 2014, Molecular findings among patients referred for clinical whole-exome sequencing, JAMA, 312, 1870, 10.1001/jama.2014.14601 · DOI · Paper page
  2. Ng, 2010, Whole genome sequencing, Genetic Variation: Methods Protocols, 215, 10.1007/978-1-60327-367-1_12 · DOI
  3. Belkadi, 2015, Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants, Proc Natl Acad Sci, 112, 5473, 10.1073/pnas.1418631112 · DOI
  4. Lek, 2016, Analysis of protein-coding genetic variation in 60,706 humans, Nature, 536, 285, 10.1038/nature19057 · DOI · Paper page
  5. 1000 Genomes Project Consortium, 2010, A map of human genome variation from population scale sequencing, Nature, 467, 1061, 10.1038/nature09534 · DOI · Paper page
  6. Tate, 2019, Cosmic: the catalogue of somatic mutations in cancer, Nucleic Acids Res, 47, D941, 10.1093/nar/gky1015 · DOI · Paper page
  7. Labelle, 1993, Characterization of the human fumarylacetoacetate hydrolase gene and identification of a missense mutation abolishing enzymatic activity, Hum Mol Genet, 2, 941, 10.1093/hmg/2.7.941 · DOI · Paper page
  8. Kawaguchi, 2005, The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library, Oncogene, 24, 6976, 10.1038/sj.onc.1208839 · DOI
  9. Dietlein, 2020, Identification of cancer driver genes based on nucleotide context, Nat Genet, 52, 208, 10.1038/s41588-019-0572-y · DOI
  10. Stratton, 2009, The cancer genome, Nature, 458, 719, 10.1038/nature07943 · DOI
  11. Vogelstein, 2013, Cancer genome landscapes, Science, 339, 1546, 10.1126/science.1235122 · DOI · Paper page
  12. Stephens, 2012, The landscape of cancer genes and mutational processes in breast cancer, Nature, 486, 400, 10.1038/nature11017 · DOI
  13. Rose, 2021, Rcsb protein data bank: architectural advances towards integrated searching and efficient access to macromolecular structure data from the pdb archive, J Mol Biol, 433, 166704, 10.1016/j.jmb.2020.11.003 · DOI
  14. Ilari, 2008, Protein structure determination by x-ray crystallography, Bioinformatics: Data Sequence Anal Evol, 63
  15. Wüthrich, 1990, Protein structure determination in solution by nmr spectroscopy, J Biol Chem, 265, 22059, 10.1016/S0021-9258(18)45665-7 · DOI
  16. Yip, 2020, Atomic-resolution protein structure determination by cryo-em, Nature, 587, 157, 10.1038/s41586-020-2833-4 · DOI
  17. Varadi, 2022, Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, Nucleic Acids Res, 50, D439, 10.1093/nar/gkab1061 · DOI · Paper page
  18. Shihab, 2013, Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden markov models, Hum Mutat, 34, 57, 10.1002/humu.22225 · DOI
  19. Malhis, 2019, Improved measures for evolutionary conservation that exploit taxonomy distances, Nat Commun, 10, 1556, 10.1038/s41467-019-09583-2 · DOI
  20. Malhis, 2020, List-s2: taxonomy based sorting of deleterious missense mutations across species, Nucleic Acids Res, 48, W154, 10.1093/nar/gkaa288 · DOI
  21. López-Ferrando, 2017, Pmut: a web-based tool for the annotation of pathological variants on proteins, 2017 update, Nucleic Acids Res, 45, W222, 10.1093/nar/gkx313 · DOI
  22. Adzhubei, 2010, A method and server for predicting damaging missense mutations, Nat Methods, 7, 248, 10.1038/nmeth0410-248 · DOI · Paper page
  23. Choi, 2015, Provean web server: a tool to predict the functional effect of amino acid substitutions and indels, Bioinformatics, 31, 2745, 10.1093/bioinformatics/btv195 · DOI
  24. Vaser, 2016, Sift missense predictions for genomes, Nat Protoc, 11, 1, 10.1038/nprot.2015.123 · DOI · Paper page
  25. Wang, 2019, Structure-based method for predicting deleterious missense snps, 1
  26. Ponzoni, 2018, Structural dynamics is a determinant of the functional significance of missense variants, Proc Natl Acad Sci, 115, 4164, 10.1073/pnas.1715896115 · DOI
  27. Ponzoni, 2020, Rhapsody: predicting the pathogenicity of human missense variants, Bioinformatics, 36, 3084, 10.1093/bioinformatics/btaa127 · DOI
  28. Bakan, 2011, Prody: protein dynamics inferred from theory and experiments, Bioinformatics, 27, 1575, 10.1093/bioinformatics/btr168 · DOI
  29. Quan, 2019, Dampred: recognizing disease-associated nssnps through bayes-guided neural-network model built on low-resolution structure prediction of proteins and protein–protein interactions, J Mol Biol, 431, 2449, 10.1016/j.jmb.2019.02.017 · DOI
  30. Sedova, 2019, Cancer3d 2.0: interactive analysis of 3d patterns of cancer mutations in cancer subsets, Nucleic Acids Res, 47, D895, 10.1093/nar/gky1098 · DOI
  31. Meyer, 2016, mutation3d: cancer gene prediction through atomic clustering of coding variants in the structural proteome, Hum Mutat, 37, 447, 10.1002/humu.22963 · DOI
  32. Gao, 2017, 3d clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets, Genome Med, 9, 1, 10.1186/s13073-016-0393-x · DOI
  33. Chen, 2020, Hotspot3d web server: an integrated resource for mutation analysis in protein 3d structures, Bioinformatics, 36, 3944, 10.1093/bioinformatics/btaa258 · DOI
  34. Stitziel, 2004, Toposnp: a topographic database of non-synonymous single nucleotide polymorphisms with and without known disease association, Nucleic Acids Res, 32, D520, 10.1093/nar/gkh104 · DOI
  35. Obiajulu, 2022, Alphacluster: Coevolutionary driven residue-residue interaction models enable quantifiable clustering analysis of de novo variants to enhance predictions of pathogenicity, 10.21203/rs.3.rs-1910518/v2 · DOI
  36. English, 2001, Experimental and computational mapping of the binding surface of a crystalline protein, Protein Eng, 14, 47, 10.1093/protein/14.1.47 · DOI
  37. Liang, 1998, Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design, Protein Sci, 7, 1884, 10.1002/pro.5560070905 · DOI · Paper page
  38. Tseng, 2009, Predicting protein function and binding profile via matching of local evolutionary and geometric surface patterns, J Mol Biol, 387, 451, 10.1016/j.jmb.2008.12.072 · DOI
  39. Tseng, 2006, Estimation of amino acid residue substitution rates at local spatial regions and application in protein function inference: a bayesian Monte Carlo approach, Mol Biol Evol, 23, 421, 10.1093/molbev/msj048 · DOI
  40. Dundas, 2011, Structural signatures of enzyme binding pockets from order-independent surface alignment: a study of metalloendopeptidase and nad binding proteins, J Mol Biol, 406, 713, 10.1016/j.jmb.2010.12.005 · DOI
  41. Edelsbrunner, 1994, Three-dimensional alpha shapes, ACM Trans Graphics (TOG), 13, 43, 10.1145/174462.156635 · DOI · Paper page
  42. Liang, 1998, Analytical shape computation of macromolecules: I. molecular area and volume through alpha shape, Proteins: Struct Funct Bioinformatics, 33, 1, 10.1002/(SICI)1097-0134(19981001)33:1<1::AID-PROT1>3.0.CO;2-O · DOI
  43. Breiman, 2001, Random forests, Mach Learn, 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
  44. Karczewski, 2017, The exac browser: displaying reference data information from over 60 000 exomes, Nucleic Acids Res, 45, D840, 10.1093/nar/gkw971 · DOI
  45. Nair, 2013, V ari b ench: a benchmark database for variations, Hum Mutat, 34, 42, 10.1002/humu.22204 · DOI
  46. Bendl, 2014, Predictsnp: robust and accurate consensus classifier for prediction of disease-related mutations, PLoS Comput Biol, 10, e1003440, 10.1371/journal.pcbi.1003440 · DOI
  47. Billas, 2003, Structural adaptability in the ligand-binding pocket of the ecdysone hormone receptor, Nature, 426, 91, 10.1038/nature02112 · DOI
  48. Michael Gromiha, 1999, Relationship between amino acid properties and protein stability: buried mutations, J Protein Chem, 18, 565, 10.1023/A:1020603401001 · DOI
  49. Tian, 2018, Castp 3.0: computed atlas of surface topography of proteins, Nucleic Acids Res, 46, W363, 10.1093/nar/gky473 · DOI · Paper page
  50. Pires, 2014, Mcsm: predicting the effects of mutations in proteins using graph-based signatures, Bioinformatics, 30, 335, 10.1093/bioinformatics/btt691 · DOI · Paper page
  51. Li, 2003, Simplicial edge representation of protein structures and alpha contact potential with confidence measure, Proteins: Struct Funct Bioinformatics, 53, 792, 10.1002/prot.10442 · DOI
  52. Li, 2005, Geometric cooperativity and anticooperativity of three-body interactions in native proteins, Proteins: Struct Funct and Bioinformatics, 60, 46, 10.1002/prot.20438 · DOI
  53. Liang, 1998, Analytical shape computation of macromolecules: I. molecular area and volume through alpha shape, Proteins: Struct Funct Bioinformatics, 33, 1, 10.1002/(SICI)1097-0134(19981001)33:1<1::AID-PROT1>3.0.CO;2-O · DOI
  54. Liang, 1998, Analytical shape computation of macromolecules: ii. Inaccessible cavities in proteins, Proteins: Struct Funct Bioinformatics, 33, 18, 10.1002/(SICI)1097-0134(19981001)33:1<18::AID-PROT2>3.0.CO;2-H · DOI
  55. Nakamura, 1996, Roles of electrostatic interaction in proteins, Q Rev Biophys, 29, 1, 10.1017/S0033583500005746 · DOI
  56. Shoulders, 2006, Reciprocity of steric and stereoelectronic effects in the collagen triple helix, J Am Chem Soc, 128, 8112, 10.1021/ja061793d · DOI
  57. Baldridge, 2011, Steric and electronic effects in capsule-confined green fluorescent protein chromophores, J Am Chem Soc, 133, 712, 10.1021/ja1094606 · DOI
  58. Bundy, 1984, Breadth-first search, Catalogue Artif Intell Tools, 13
  59. Kumar, 1999, Salt bridge stability in monomeric proteins, J Mol Biol, 293, 1241, 10.1006/jmbi.1999.3218 · DOI
  60. Steven Henikoff and Jorja G Henikoff, 1992, Amino acid substitution matrices from protein blocks, Proc Natl Acad Sci, 89, 10915, 10.1073/pnas.89.22.10915 · DOI · Paper page
  61. Fabian, 2014, Clustal omega, Curr Protoc Bioinformatics, 48, 3
  62. Liaw, 2002, Classification and regression by randomforest, R News, 2, 18
  63. Ingle, 2019, Genetic mutations in the s-loop of human glutathione synthetase: links between substrate binding, active site structure and allostery. Computational and structural, Biotechnol J, 17, 31
  64. Dinescu, 2004, Function of conserved residues of human glutathione synthetase: implications for the atp-grasp enzymes, J Biol Chem, 279, 22412, 10.1074/jbc.M401334200 · DOI
  65. Ribeiro, 2018, Mechanism and catalytic site atlas (m-csa): a database of enzyme reaction mechanisms and active sites, Nucleic Acids Res, 46, D618, 10.1093/nar/gkx1012 · DOI
  66. Zeng, 2000, Distribution-balanced stratified cross-validation for accuracy estimation, J Exp Theor Artif Intell, 12, 1, 10.1080/095281300146272 · DOI
  67. Chicco, 2020, The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation, BMC Genomics, 21, 1, 10.1186/s12864-019-6413-7 · DOI · Paper page
  68. Greenman, 2007, Patterns of somatic mutation in human cancer genomes, Nature, 446, 153, 10.1038/nature05610 · DOI
  69. Ramroop, 2019, Germline variants impact somatic events during tumorigenesis, Trends Genet, 35, 515, 10.1016/j.tig.2019.04.005 · DOI
  70. Wang, 2017, Role of tumor microenvironment in tumorigenesis, J Cancer, 8, 761, 10.7150/jca.17648 · DOI · Paper page
  71. Dundas, 2011, Structural signatures of enzyme binding pockets from order-independent surface alignment: a study of metalloendopeptidase and nad binding proteins, J Mol Biol, 406, 713, 10.1016/j.jmb.2010.12.005 · DOI
  72. Liu, 2021, Enhancing cancer driver gene prediction by protein–protein interaction network, IEEE/ACM Trans Comput Biol Bioinform, 19, 2231, 10.1109/TCBB.2021.3063532 · DOI
  73. Kundrotas, 2020, Dockground tool for development and benchmarking of protein docking procedures, Protein Struct Predict, 289, 10.1007/978-1-0716-0708-4_17 · DOI
  74. Evans, 2021, Protein complex prediction with alphafold-multimer, BioRxiv, 2021
  75. Pierce, 2014, Zdock server: interactive docking prediction of protein–protein complexes and symmetric multimers, Bioinformatics, 30, 1771, 10.1093/bioinformatics/btu097 · DOI
  76. Sprinzak, 2003, How reliable are experimental protein–protein interaction data?, J Mol Biol, 327, 919, 10.1016/S0022-2836(03)00239-0 · DOI

Metadata

Authors
Wang, B., Lei, X., Tian, W., Perez-Rathke, A., Tseng, Y.-Y., & Liang, J.
Affiliations
  • University of Illinois Chicago (US)
  • Wayne State University (US)
Corresponding author
  • Tseng, Yan-Yuan
  • Liang, Jie
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2023-06-17
Volume
24
Issue
4
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbad206
PMC
PMC10359089
OpenAlex
W4381124915
Cited by
11
References
4
Citations by year
2026: 2 · 2025: 6 · 2024: 3
FWCI
1.83
Type
journal article
Language
en
Open access
Licence
academic.oup.com/pages/standard-publication-reuse-rights (vor)
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institutes of Health (R35 GM 127084)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
deleterious mutation prediction, protein structure, cancer driver genes, driver mutations, monogenic disorders, germline mutations, somatic mutations, AlphaFold2, saturation mutagenesis, human proteome
MeSH terms
Humans, Mutation, Neoplasms, Virulence, Computational Biology, Mutation, Missense

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