CoChat
Sign Up
Cited by 0Protein Structure and DynamicsChemical Synthesis and AnalysisMachine Learning in Bioinformatics

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

PolyProline Predictor: A web server for empirical sequence‐based prediction of polyproline II helices

Open access
López‐Sánchez, Rubén, Pantoja‐Uceda, David, Mompeán, Miguel, Laurents, Douglas V.·2026·Protein Science
DOI 10.1002/pro.70675PMC PMC13243192

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Polyproline II (PPII) helices are extended left‐handed secondary structures increasingly recognized for their roles in molecular recognition, signaling and within intrinsically disordered regions of proteins. Despite their functional importance, predicting regions with propensity to form PPII helices from sequence alone remains challenging due to subtle sequence determinants and their frequent misclassification as random coil. Here, we present PolyProline Predictor (PPP), a user‐friendly web server ( https://rmni.iqf.csic.es/software/polypropre/ ) for empirical, sequence‐based prediction of PPII helices. Unlike machine learning approaches, PPP aligns query sequences against a curated database of experimentally validated PPII helices, providing an interpretable, composition‐, and position‐sensitive similarity map. PPP successfully identified conserved PPII motifs in diverse proteins, and predicted the presence of similar motifs in regions lacking experimental structures but modeled by AlphaFold as extended PPII conformations, such as glycine‐rich plant proteins, mycobacterial PE_PGRS virulence factors, and the “disordered” C‐terminal tails of GroEL and its homologs, as well as the amyloid‐flanking region of the necroptosis effector RIPK3. Molecular dynamics simulations further supported persistent PPII helical bundles in three glycine‐rich mycobacterial proteins and more heterogeneous, transient PPII populations in plant proteins and RIPK3. Circular dichroism and nuclear magnetic resonance (NMR) spectroscopy validated these predictions for RIPK3, revealing partially populated PPII conformations flanking its amyloid core. Such motifs may regulate its amyloid assembly, offering structural insight into mechanisms of functional amyloid formation. By combining experimental evidence with interpretable prediction, PPP fills a critical gap in bioinformatics tools and enables systematic exploration of regions with propensity to form PPII helices across proteomes, redefining the structural landscape of low‐complexity regions.

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 · 10.1002/pro.70675

Cites

2 of 2 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

  • 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 · cited 10×

    “Similarly, the PPP server also identifies large glycine-rich segments in M. tuberculosis PE_PGRS47 (UniProt ID Q79FB3), implicated in host – pathogen interactions and intracellular persistence (Saini et al., 2016), and the corresponding AF PSD models further support the presence of a glycine-rich PPII helical bundle d…”
    Influential

  • Accurate structure prediction of biomolecular interactions with AlphaFold 3

    Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · cited 2×

    “Remarkably, the AlphaFold Server (Abramson et al., 2024) predicts with moderate confidence that a hexaglycine peptide would bind and associate with the former.”

Flagged first, then by citations

Research works printed but not linked: 79

  1. 10.1016/j.softx.2015.06.001 · DOI · Paper page
  2. 10.1016/j.jmb.2013.03.018 · DOI
  3. 10.1006/jmbi.1996.0673 · DOI
  4. 10.1063/1.448118 · DOI
  5. 10.1371/journal.ppat.1010760 · DOI
  6. 10.1110/ps.32602 · DOI
  7. 10.1016/j.jmb.2005.10.053 · DOI
  8. 10.1016/j.jbc.2023.105122 · DOI
  9. 10.1093/database/bau102 · DOI
  10. 10.1038/24206 · DOI
  11. 10.1038/176780a0 · DOI
  12. 10.1063/1.464397 · DOI
  13. 10.1038/s41586-021-03959-5 · DOI
  14. DeLano WL, 2002, The PyMOL molecular graphics system
  15. 10.1016/j.str.2018.07.017 · DOI
  16. 10.1038/sj.emboj.7600425 · DOI
  17. 10.1155/2022/9015123 · DOI
  18. 10.1016/j.cell.2025.03.027 · DOI
  19. 10.1073/pnas.1609579114 · DOI
  20. 10.1038/s41467-020-15698-8 · DOI
  21. 10.1021/ja9058052 · DOI
  22. 10.1142/S0219720023500221 · DOI
  23. 10.1002/pro.70208 · DOI · Paper page
  24. 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H · DOI
  25. 10.1038/s41467-022-31333-0 · DOI
  26. 10.1093/nar/gkac439 · DOI
  27. 10.1515/BMC.2010.022 · DOI
  28. 10.1146/annurev-biophys-082521-113418 · DOI
  29. 10.1038/nmeth.4067 · DOI
  30. 10.1038/nature11283 · DOI · Paper page
  31. 10.1093/nar/gkab425 · DOI
  32. 10.1021/acs.biochem.1c00501 · DOI
  33. 10.1096/fasebj.14.2.231 · DOI
  34. 10.1007/s10858-011-9472-x · DOI
  35. 10.1002/pro.435 · DOI
  36. 10.1074/jbc.M115.665844 · DOI
  37. 10.1093/bioinformatics/btu350 · DOI
  38. 10.1073/pnas.0912293107 · DOI
  39. 10.1093/bioinformatics/btu830 · DOI
  40. 10.1016/j.cell.2012.06.019 · DOI · Paper page
  41. 10.1038/nature10879 · DOI
  42. 10.1038/s42004-024-01268-2 · DOI
  43. 10.1038/s42004-025-01576-1 · DOI
  44. 10.1016/S0014-5793(01)03290-2 · DOI
  45. 10.4161/psb.5.2.10336 · DOI
  46. 10.1093/bioinformatics/btaa186 · DOI
  47. 10.1016/j.cell.2018.03.032 · DOI
  48. 10.1016/j.abb.2021.108867 · DOI
  49. 10.1002/2211-5463.13163 · DOI
  50. 10.1371/journal.pone.0053785 · DOI
  51. 10.1098/rsos.191239 · DOI
  52. 10.1002/pmic.201800081 · DOI
  53. 10.1371/journal.pone.0150800 · DOI
  54. 10.26434/chemrxiv-2025-kmpwh · DOI
  55. 10.1002/pro.5560060809 · DOI
  56. 10.1063/1.328693 · DOI
  57. 10.1021/ja8013538 · DOI
  58. 10.1016/j.jbc.2023.104568 · DOI
  59. 10.1038/s41586-021-04140-8 · DOI
  60. 10.1038/176915a0 · DOI
  61. 10.1016/j.abb.2024.109981 · DOI · Paper page
  62. 10.1002/pro.70354 · DOI
  63. 10.1038/nmicrobiol.2016.133 · DOI
  64. 10.1371/journal.pone.0200430 · DOI
  65. 10.1007/BF00042036 · DOI
  66. 10.1039/D5TB01841B · DOI
  67. 10.1038/s41598-022-11684-w · DOI
  68. 10.1021/jacs.3c10941 · DOI
  69. 10.3390/vaccines3040850 · DOI
  70. 10.1002/bip.1968.360061212 · DOI
  71. 10.1002/bip.1968.360060911 · DOI
  72. 10.1021/jacs.8b05261 · DOI
  73. 10.1093/nar/gkab1061 · DOI · Paper page
  74. 10.1074/jbc.M114.577205 · DOI
  75. 10.1042/bj2970249 · DOI
  76. 10.1073/pnas.2022933118 · DOI
  77. 10.1073/pnas.2213727120 · DOI
  78. 10.3389/fmolb.2017.00098 · DOI
  79. 10.1111/tpj.14273 · DOI

Metadata

Authors
López‐Sánchez, R., Pantoja‐Uceda, D., Mompeán, M., & Laurents, D. V.
Affiliations
Consejo Superior de Investigaciones Científicas (ES)
Corresponding author
Laurents, Douglas V.
ORCID
Venue
Protein Science
Publisher
Wiley
Published
2026-06-07
Volume
35
Issue
7
Pages
e70675-e70675
ISSN
  • 0961-8368 (ISSN-L)
  • 1469-896X
DOI
10.1002/pro.70675
PMC
PMC13243192
OpenAlex
W7163811542
Cited by
0
References
2
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Protein Science (publishedVersion, cc-by)
  • ChemRxiv (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, other-oa)
Funders
  • Agencia Estatal de Investigación (PID2019‐109306RB‐I00, PID2022‐137806OB‐I00)
  • European Research Council (101042403)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
sequence-based structure prediction, sequence alignment, intrinsically disordered regions, low complexity regions, RIPK3, amyloid assembly, glycine-rich proteins, molecular dynamics simulation, circular dichroism, NMR spectroscopy
MeSH terms
Protein Conformation, alpha-Helical, Prediction Algorithms, Peptides, Software, Protein Structure, Secondary, Internet

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