Complex Networks Govern Coiled-Coil Oligomerization – Predicting and Profiling by Means of a Machine Learning Approach
Open accessAbstract
Understanding the relationship between protein sequence and structure is one of the great challenges in biology. In the case of the ubiquitous coiled-coil motif, structure and occurrence have been described in extensive detail, but there is a lack of insight into the rules that govern oligomerization, i.e. how many α-helices form a given coiled coil. To shed new light on the formation of two- and three-stranded coiled coils, we developed a machine learning approach to identify rules in the form of weighted amino acid patterns. These rules form the basis of our classification tool, PrOCoil, which also visualizes the contribution of each individual amino acid to the overall oligomeric tendency of a given coiled-coil sequence. We discovered that sequence positions previously thought irrelevant to direct coiled-coil interaction have an undeniable impact on stoichiometry. Our rules also demystify the oligomerization behavior of the yeast transcription factor GCN4, which can now be described as a hybrid--part dimer and part trimer--with both theoretical and experimental justification.
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Initial attempts on predicting oligomerization states in coiled coils focused on distinguishing dimers from trimers.
“Initial attempts on predicting oligomerization states in coiled coils focused on distinguishing dimers from trimers.34−39”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [36]
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BackgroundInfluentialBañares-Hidalgo, Á., Pérez‐Gil, J., & Estrada, P. (2014) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2014.03.016 · Cited by 15 · see citing works
“It is also possible to predict whether the coiled coil in protein sequences with propensity to form superhelix is prone to form a dimeric or a trimeric assembly using the PROCOIL algorithm, which plots the contribution of each residue of the chosen sequence to the discriminant function.”
BackgroundMethodInfluentialGenome-Wide Chromatin Remodeling Identified at GC-Rich Long Nucleosome-Free Regions
Schwarzbauer, K., Bodenhofer, U., & Hochreiter, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047924 · Cited by 14 · see citing works
“These pattern weights facilitate the computation of prediction profiles [67] from which we can extract regions of interest, i.e. sub-sequences the SVM considers particularly typical of remodeled LNFRs (Text S1, Section 5.3).”
InfluentialSelf-citationStackSSSPred: A Stacking-Based Prediction of Supersecondary Structure from Sequence
Flot, M., Mishra, A., Kuchi, A., & Hoque, M. T. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9161-7_5 · Cited by 12 · see citing works
Sequence- and structure-based prediction of amyloidogenic regions in proteins
Bouziane, H., & Chouarfia, A. (2019) · Soft Computing · doi:10.1007/s00500-019-04087-z · Cited by 11 · see citing works
“Thus, the area above the horizontal line at − bl (baseline) corresponds to the positive class and the area below, the negative class (Mahrenholz et al. 2011; Palme et al. 2015).”
BackgroundACCORD: an assessment tool to determine the orientation of homodimeric coiled-coils
Kim, B.-W., Jung, Y. O., Kim, M. K., Kwon, D. H., Park, S. H., Kim, J., Kuk, Y.-B., Oh, S.-J., Kim, L., Kim, B. H., et al. (2017) · Scientific Reports · doi:10.1038/srep43318 · Cited by 10 · see citing works
AAFreqCoil: a new classifier to distinguish parallel dimeric and trimeric coiled coils
Wang, X., Zhou, Y., & Yan, R. (2015) · Molecular BioSystems · doi:10.1039/c5mb00119f · Cited by 9 · see citing works
Drobnak, I., Ljubetič, A., Gradišar, H., Pisanski, T., & Jerala, R. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-39196-0_2 · Cited by 9 · see citing works
“0 [79] and ProCoil [80], that can classify a sequence with assigned heptad registers as either parallel dimers or trimers.”
BackgroundKodama, T. S., Furuita, K., & Kojima, C. (2025) · Molecules · doi:10.3390/molecules30061220 · Cited by 5 · see citing works
“…web-based tool for the comparative visualization of coiled-coil predictions, including four coiled-coil tools, Marcoil [81], Multicoil2 [82], Ncoils [83], and Paircoil2 [84], and three oligomerization evaluation methods (Scorer 2.0 [85], PrOCoil [86], and LOGICOIL [87]) with a window length of 21.”
InfluentialAlvarez-Cabrera, A. L., Delgado, S., Gil‐Carton, D., Mortuza, G. B., Montoya, G., Sorzano, C. Ó. S., Tang, T. K., & Carazo, J. M. (2017) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2017.00017 · Cited by 5 · see citing works
“For HsCPAP897−1338, the secondary structure was predicted with Jpred4 (Drozdetskiy et al., 2015); for the prediction of CC regions the web server COILS (Lupas et al., 1991) was used and, finally, the web servers PrOCoils (Mahrenholz et al., 2011) and LOGICOIL (Vincent et al., 2013) were employed to predict the oligome…”
MethodNotari, E., Wood, C. W., & Michel, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01695 · Cited by 4 · see citing works
Basic Methods of Data Analysis
Kepler, J. (2015) · Cited by 2 · see citing works
Shah, S., Rahman, A. U., Jabeen, S., Khan, A., Khan, F. G., & ELAffendi, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3174260 · Cited by 2 · see citing works
“In our proposed technique, we have accommodated sequence order information very effectively, with the help of gappy pair kernel [61], [62].”
MethodSharing of Very Short IBD Segments between Humans, Neandertals, and DenisovansPreprint
Povysil, G., & Hochreiter, S. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/003988 · Cited by 3 · see citing works
Self-citationAraghi, R. R., Mahrenholz, C. C., Volkmer, R., & Koksch, B. (2012) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.8.71 · Cited by 1 · see citing works
Self-citationDecoding Sequence Classification Models for Acquiring New Biological InsightsPreprint
Bodenhofer, U., Kothmeier, A., Abfalter, I. G., Mahrenholz, C. C., & Hochreiter, S. (2010) · Nature Precedings · doi:10.1038/npre.2010.4708.1
Self-citationNotari, E., Wood, C. W., & Michel, J. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-t02df
Zambidis, A. E., Siddaramaiah, L. K., Konecny, A., Gray, M. D., & Prlic, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.13.704926
CaptureBody enables accurate unmixing for spectral flow cytometry
Zambidis, A. E., Siddaramaiah, L. K., Gray, M., Konecny, A., & Prlic, M. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101571
- Authors
- Mahrenholz, C. C., Abfalter, I. G., Bodenhofer, U., Volkmer, R., & Hochreiter, S.
- Affiliations
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Johannes Kepler University of Linz (AT)
- Charité – Universitätsmedizin Berlin (DE)
- Corresponding author
- Hochreiter, Sepp
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 10
- Issue
- 5
- Pages
- M110.004994
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC3098589
- OpenAlex
- W2119336981
- Cited by
- 47
- References
- 46
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 2 · 2019: 2 · 2018: 1 · 2017: 8 · 2016: 2 · 2015: 5 · 2014: 5 · 2013: 5 · 2012: 5
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- GlaxoSmithKline
- Deutsche Forschungsgemeinschaft
- GlaxoSmithKline foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
- Keywords
- amino acid patterns, sequence-structure relationship, GCN4, machine learning prediction
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Models, Molecular, ROC Curve, Area Under Curve, Amino Acid Motifs, Saccharomyces cerevisiae Proteins, Mutant Proteins, Basic-Leucine Zipper Transcription Factors, Protein Multimerization, Molecular Sequence Annotation
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