Protein Shape, Not Just Sequence, Guides Where the Proteasome Cuts

Deep inside nearly every cell, a barrel-shaped molecular machine called the proteasome shreds worn-out or damaged proteins into short peptides. That routine act of cellular housekeeping has a hidden consequence: some of the fragments it produces are displayed on the cell surface, where they tell patrolling immune cells whether the cell is healthy or infected. Now, a team of researchers from Brazil and the United States reports that the proteasome’s cutting decisions are influenced not only by the sequence of amino acids in a protein, but also by the local three-dimensional shape of the protein backbone at the moment of cleavage. The finding, published in Frontiers in Bioinformatics, could sharpen the computational tools used to design vaccines and T-cell-based immunotherapies.

The proteasome is a massive 2.5-megadalton complex whose 20S catalytic core is capped by 19S regulatory particles that unfold ubiquitinated substrates and thread them into the destruction chamber. A narrow channel of roughly 15 angstroms in the alpha ring forces every substrate to enter in an extended, unfolded state. Structural studies of proteases bound to their substrates have long suggested that the polypeptide chain must flatten into an extended beta-strand conformation inside the catalytic groove, and modern inhibitors such as carfilzomib and bortezomib mimic exactly that geometry. What remained unclear was whether the conformation a substrate region adopts before it reaches the active site leaves any detectable signature on where the cuts actually fall.

Previous work on proteasomal specificity has focused almost entirely on sequence: the beta5 catalytic subunit favors hydrophobic residues, beta2 prefers basic ones, and the immunoproteasome’s swapped subunits further tune those preferences. But sequence motifs alone cannot explain why cleavage appears biased toward particular structural contexts. The researchers asked a deceptively simple question: how much cleavage signal survives when amino-acid identity is stripped away entirely, leaving only information about whether each residue sits in a helix, a beta-strand, or a coil?

To find out, the team first mined a corpus of 1,505 non-redundant human and viral protein structures from the Protein Data Bank, all resolved at 2.5 angstroms or better. They scored every residue with established sequence-based cleavage predictors, including NetChop 20S, NetChop C-term, ProteaSMM-C, and ProteaSMM-I, then compared the secondary-structure composition of the top 10 percent of predicted cleavage sites against the background composition of the whole corpus. The result was striking: high-confidence predicted cleavage sites were consistently enriched in beta-strand, or extended, conformations and depleted in coil-like regions. The beta-strand signal peaked near the cleavage site itself, forming a bell-shaped profile that faded with distance, and it was even stronger for predictors trained on immunoproteasome and MHC class I ligand data.

The reciprocal pattern appeared at unfavored sites. There, coils were enriched while helices and beta-strands were underrepresented relative to expectation. Crucially, the same tendencies held when the researchers analyzed true experimental cleavage sites from a 20-protein digestion dataset combining published in vitro data, and they held whether secondary structure was predicted from sequence by NetSurfP-3.0 or assigned directly from crystallographic coordinates by STRIDE. A protein-level analysis reinforced the point: mean predicted coil content correlated inversely with mean predicted cleavage probability across the 1,505-protein corpus and across 22 experimental digestion maps, suggesting the association is not an artifact of residue-level statistical non-independence.

The team then went a step further and built a machine learning model that saw no amino acids at all. Their proof-of-concept classifier, dubbed SSO-8, represented each candidate cleavage site as an eight-residue window spanning positions P4 to P4′ on either side of the scissile bond, encoded solely as 64 eight-state secondary-structure probabilities predicted by NetSurfP-3.0. Trained on the 20-protein digestion dataset, which contains 1,215 experimentally determined cleavage sites, the final SSO-8 model was a linear support-vector machine selected by protein-grouped cross-validation. On held-out validation benchmarks derived from 16 HIV-derived peptides, SSO-8 achieved an area under the ROC curve of 0.649 on the constitutive proteasome dataset and 0.737 on the immunoproteasome dataset.

Those numbers become remarkable in context. A matched sequence-only control, trained with the identical protocol but using full one-hot amino-acid encodings, reached AUC values of 0.674 and 0.736 on the same benchmarks, and paired statistical tests found no significant difference between the structure-only and sequence-only models. In other words, secondary structure alone captured nearly the same cleavage signal as complete amino-acid identity, while using 2.5-fold less input information. On the immunoproteasome benchmark, SSO-8 even edged out the widely used NetChop 20S predictor, which reached an AUC of 0.711, and matched NetChop C-term at 0.731.

To test whether structural information adds anything beyond sequence, the researchers constructed hybrid position-specific scoring matrices, or PSSMs, that aggregated predictions from multiple methods. Adding SSO-8 to a sequence-only ensemble nudged the immunoproteasome benchmark AUC from 0.781 to 0.797, and the best five-method ensemble reached 0.806. The improvement was directionally consistent but not statistically significant, with a paired permutation p-value of 0.30, so the authors are careful to present it as suggestive rather than conclusive. Still, the pattern supports the idea that local backbone conformation carries cleavage information partly orthogonal to sequence motifs.

The team also ran a rigorous audit for data leakage. Pairwise Needleman-Wunsch alignments confirmed that no protein pair between the two training subsets, or between the training set and the validation peptides, exceeded the standard 30 percent identity threshold for functional relatedness. And because the PSSM octamer labels were derived purely from predicted scores rather than experimental annotations, experimental data from training proteins could not circularly inflate the corpus-based results. A post hoc analysis added another intriguing layer: beta-strand frequency at true immunoproteasome cleavage sites was 32.5 percent, significantly higher than the 18.7 percent observed at constitutive cleavage sites, indicating that the two proteasome types carry distinguishable structural signatures.

Mechanistically, the authors propose that strand-like geometry may simply reflect compatibility with the extended conformation the proteasome demands of its substrates, rather than selection for intact native beta-strands before engagement. The signal was consistently stronger for the immunoproteasome, the isoform induced by interferon-gamma during inflammation that specializes in producing ligands for MHC class I presentation. Because proteasomal cleavage generates the C-terminal anchor residue of presented peptides, the researchers hypothesize that this beta-strand bias may steer the immunoproteasome’s output toward structurally constrained regions of proteins, where escape mutations are less tolerated by the virus or tumor. If confirmed, incorporating structural features into next-generation cleavage predictors could improve epitope and neoantigen discovery for vaccine development and cancer immunotherapy, though the authors caution that their model was trained on limited in vitro data lacking cellular context, and that protein language models may offer an even richer structural representation for future work.

Subject of Research: Association between substrate local secondary structure and proteasomal cleavage propensity

Article Title: Substrate secondary structure is associated with proteasomal cleavage propensity

Article References: Gutierres, M. D. B. B., Beruldsen, F., Antunes, D. A., & Vieira, G. F. (2026). Substrate secondary structure is associated with proteasomal cleavage propensity. Frontiers in Bioinformatics, 6, Article 1730535. https://doi.org/10.3389/fbinf.2026.1730535

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1730535

Keywords: proteasome, immunoproteasome, secondary structure, beta-strand, machine learning, MHC class I, T-cell epitopes, cleavage prediction, immunoinformatics, neoantigen, Protein Data Bank, NetSurfP