A Combined Computer-Aided Approach to Drive the Identification of Potential Epitopes in Protein Therapeutics
Bibliographic record
Abstract
BACKGROUND: The identification of fragment sequences, or motifs, within a therapeutic protein that may elicit an immune response when processed by T-cells can be provided by computer-aided approaches. Immunogenicity is a significant problem associated with protein therapeutics and should be investigated in the early stage of protein-based drug development to avoid treatment resistance and potentially life-threatening immune responses. PURPOSE: To provide a combined computer-aided protocol for investigating the immunogenic profile of a recombinant Kunitz-type inhibitor, which has been reported as promising antitumor agent by our research group. METHODS: The combination of databases searching (IEDB and SYFPEITHI) and molecular docking simulations was exploited, herein. This combined protocol has allowed the identification of potential epitopes before in vitro/in vivo evaluation. Predictors of human proteasome cleavage transport and major histocompatibility complex (MHC) binding were considered as overall score assigning the corresponding intrinsic potential of being a T cell epitope to each fragment sequence. The peptides or motifs better classified in the two databases were docked into the three-dimensional (3D) structure of MHC (class I and II) complex to verify the calculated binding affinity. The binding interactions regarding the molecular recognition process by T-cells were also exploited through the MHC:ligand:T-cell complexes. RESULTS: Regarding the Kunitz-type sequence, four motifs were identified as potentially epitopes for MHC-I and three motifs were found for MHC-II. But, those motifs were classified as moderately immunogenic. Final remarks: The combined computer-aided protocol has significantly reduced the number of potential epitopes to be considered for further analysis and could be useful to identify immunogenic fragments (high, moderate and low) in protein pharmaceutics before in vitro/in vivo experimentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".