Add NetTCR-2.2 pMHC:TCR binding predictor (#215)

README.md

@@ -163,6 +163,7 @@ The canonical prediction kind strings are defined in `mhctools.pred.Kind`.
 | `pMHC_affinity` | Peptide-MHC binding affinity |
 | `pMHC_presentation` | Likelihood of surface presentation (EL/processing) |
 | `pMHC_stability` | Peptide-MHC complex stability |
+| `pMHC_TCR_binding` | TCR recognition of a peptide-MHC (pMHC:TCR binding) |
 | `immunogenicity` | T-cell immunogenicity |
 | `antigen_processing` | Combined processing score |
 | `proteasome_cleavage` | Proteasomal cleavage score |
@@ -203,6 +204,7 @@ Examples:
 | `MHCflurry` haplotype mode | `pMHC_presentation` | `haplotype` | `I` |
 | `MHCflurry` per-allele panel mode | `pMHC_presentation` | `single_allele` | `I` |
 | `Pepsickle` | `proteasome_cleavage` | `none` | `none` |
+| `NetTCR` | `pMHC_TCR_binding` | `none` | `I` |
 
 For MHCflurry presentation, `presentation_allele_mode="haplotype"` treats the
 requested alleles as one sample genotype and emits one `pMHC_presentation`
@@ -268,6 +270,35 @@ Processing predictors use configurable scoring to aggregate per-position
 cleavage probabilities into peptide-level scores. See `ProcessingPredictor`
 and `ProteasomePredictor` for details.
 
+### TCR specificity
+
+| Predictor | Kinds produced | Requires |
+|---|---|---|
+| `NetTCR` | pMHC:TCR binding | [NetTCR-2.2](https://github.com/mnielLab/NetTCR-2.2) clone (set `NETTCR_DIR`) + a TFLite runtime (`pip install mhctools[nettcr]`) |
+
+`NetTCR` predicts whether a paired αβ T-cell receptor recognises a
+(class-I) peptide. Unlike the MHC-ligand predictors, its input is a peptide
+plus a `TCR` (the six CDR loops), not an allele, and it emits the
+`pMHC_TCR_binding` kind. NetTCR ships its pretrained weights in its git
+repository as small TFLite models; this wrapper runs the pan cross-validation
+ensemble in-process and does not need NetTCR's conda environment.
+
+```python
+from mhctools import NetTCR, TCR
+
+predictor = NetTCR()   # resolves NETTCR_DIR / ~/NetTCR-2.2
+tcr = TCR(
+    cdr1a="NSASQS", cdr2a="VYSSG", cdr3a="VVEGDKVI",
+    cdr1b="MGHRA", cdr2b="YSYEKL", cdr3b="ASSHSGYEQF", name="clone1")
+
+# Score explicit (peptide, TCR) pairs...
+results = predictor.predict_pairs([("LLWNGPMAV", tcr)])
+results[0].tcr_binding.score        # ensemble-mean recognition probability
+
+# ...or every peptide x TCR combination.
+results = predictor.predict(["LLWNGPMAV", "GILGFVFTL"], [tcr])
+```
+
 ## Commandline examples
 
 ### Prediction for user-supplied peptide sequences

mhctools/__init__.py

@@ -14,6 +14,7 @@
     preds_from_rows,
 )
 from .sample import MultiSample
+from .tcr import TCR
 from .iedb import (
     IedbNetMHCcons,
     IedbNetMHCpan,
@@ -60,6 +61,7 @@
     "BigMHC_IM": (".bigmhc", "BigMHC_IM"),
     "MHCflurry": (".mhcflurry", "MHCflurry"),
     "MHCflurry_Affinity": (".mhcflurry", "MHCflurry_Affinity"),
+    "NetTCR": (".nettcr", "NetTCR"),
 }
 
 
@@ -75,7 +77,7 @@ def __getattr__(name):
     raise AttributeError(
         "module %r has no attribute %r" % (__name__, name))
 
-__version__ = "3.14.1"
+__version__ = "3.15.0"
 
 __all__ = [
     "Prediction",
@@ -90,6 +92,7 @@ def __getattr__(name):
     "best_direction",
     "preds_from_rows",
     "MultiSample",
+    "TCR",
     "BindingPrediction",
     "BindingPredictionCollection",
     "IedbNetMHCcons",
@@ -140,6 +143,7 @@ def __getattr__(name):
     "BigMHC",
     "BigMHC_EL",
     "BigMHC_IM",
+    "NetTCR",
     "RandomBindingPredictor",
     "UnsupportedAllele",
 ]