<scp>MHC2AffyPred</scp> : A machine‐learning approach to estimate affinity of <scp>MHC</scp> class <scp>II</scp> peptides based on structural interaction fingerprints
Bibliographic record
Abstract
Abstract Understanding how MHC class II (MHC‐II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC‐II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC‐II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC‐II structures and the binding affinity (BA) (IC 50 ). We present here a machine‐learning approach to calculate the BA of the peptides within the MHC‐II pocket for HLA‐DRA1, HLA‐DRB1, HLA‐DP, and HLA‐DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC‐II complexes as the templates, followed by site‐directed peptide docking. The structural interaction fingerprints generated from such docked pMHC‐II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC‐II peptide lengths (9–19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC‐II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred . The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612–.898) than MHCII3D (.03–.594) and NetMHCIIpan‐3.2 (.289–.692) programs in the HLA‐DRA1, HLA‐DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA‐DP and HLA‐DQ peptides (13‐mer to 17‐mer). Further, a case study on MHC‐II binding 15‐mer peptides of severe acute respiratory syndrome coronavirus‐2 showed very close competency in computing the IC 50 values compared to the sequence‐based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".