issue with installation due to modeller update solved

Author: AmirAsgary

.gitignore

@@ -43,4 +43,5 @@ initial_guess_benchmark/
 neoantigen/
 parsefold_affine/
 predictions/
-
+PMBind/
+data/example/test_5npz.tsv

PANDORA/PANDORA/PMHC/PMHC.py

@@ -316,7 +316,7 @@ def __init__(self, id, peptide, allele_type=[], MHC_class = 'I',
                 try:
                     print(top_ids[:5])
                     top_id = top_ids[self.top_rank_num][0]  # blast_results is a list of top ids as tuples
-                    print('###############################DDDDD', top_ids[self.top_rank_num][0])
+                    print( top_ids[self.top_rank_num][0])
                     self.allele_type = [top_id]
                     print('Predicting Anchors for:', self.allele_type, self.top_rank_num)
                     self.anchors = Modelling_functions.predict_anchors_netMHCpan(
@@ -334,7 +334,7 @@ def __init__(self, id, peptide, allele_type=[], MHC_class = 'I',
                           f"(top_rank_num={self.top_rank_num}) - {e}")
 
             elif MHC_class == 'II' and len(anchors) < 4:
-                print('WARNING: no anchor positions provided. Pandora will predict them using netMHCIIpan. anchors:', anchors)
+                print('WARNING: no anchor positions provided. Pandora will predict them using netMHCIIpan. anchorssssss:', anchors)
                 try:
                     print(top_ids[:5])
                     top_id = top_ids[0][1]

PMGen-cpuonly.yml

@@ -108,7 +108,7 @@ dependencies:
   - markupsafe=3.0.2=py39h9399b63_1
   - mkl=2022.2.1=h84fe81f_16997
   - ml-collections=0.1.1=pyhd8ed1ab_0
-  - modeller=10.6=py39h9bf148f_0
+  - modeller
   - mpc=1.3.1=h24ddda3_1
   - mpfr=4.2.1=h90cbb55_3
   - mpmath=1.3.0=pyhd8ed1ab_1

PMGen.yml

@@ -108,7 +108,7 @@ dependencies:
   - markupsafe=3.0.2=py39h9399b63_1
   - mkl=2022.2.1=h84fe81f_16997
   - ml-collections=0.1.1=pyhd8ed1ab_0
-  - modeller=10.6=py39h9bf148f_0
+  - modeller
   - mpc=1.3.1=h24ddda3_1
   - mpfr=4.2.1=h90cbb55_3
   - mpmath=1.3.0=pyhd8ed1ab_1

data/example/test_5npz.tsv

@@ -1,2 +1,3 @@
 peptide	mhc_seq	mhc_type	anchors	id
-EFEDLTFLA	MGPHSLSYFSTAVSRPDRGDSRFIAVGYVDDTQFVRFDSDAPNPRMEPRAPWIQQEGQEYWDRNTRNVMGSAQINRVNLKTLRGYYNQSEAGSHTLQWMYGCYLGPDGLLLRGYDQFAYDGADYLALNEDLRSWTAADMAAQISKRKWEAADAAEHWRSYLQGTCVESLRRYLQMGKDTLQRAEP	1		5NPZ
+RFYKTLRAEQASQ	EEHVIIQAEFYLNPDQSGEFMFDFDGDEIFHVDMAKKETVWRLEEFGRFASFEAQGALANIAVDKANLEIMTKRSNYTPITNVPPEVTVLTNSPV/DTRPRFLWQLKFECHFFNGTERVRLLERCIYNQEESVRFDSDVGEYRAVTELGRPDAEYWNSQKDLLEQRRAAVDTYCRHNYGVGESFTVQRRVE	2		6CQJ
+TPRVTGGGAM	GSHSMRYFYTSVSRPGRGEPRFISVGYVDDTQFVRFDSDAASPREEPRAPWIEQEGPEYWDRNTQIYKAQAQTDRESLRNLRGYYNQSEAGSHTLQSMYGCDVGPDGRLLRGHDQYAYDGKDYIALNEDLRSWTAADTAAQITQRKWEAAREAEQRRAYLEGECVEWLRRYLENGKDKLERADPP	1		5WMP

install.sh

@@ -80,7 +80,6 @@ else
     ACTIVATE_CMD="conda activate"
     echo "⚠ Mamba not found! Falling back to Conda."
 fi
-
 # Step 3: Ensure Conda is initialized
 if [ -z "$CONDA_PREFIX" ]; then
     source "$(conda info --base)/etc/profile.d/conda.sh"

run_PMGen.py

@@ -173,10 +173,14 @@ def main():
             AMINO_ACIDS = set('ARNDCEQGHILKMFPSTWYV/')
             try:
                 df = pd.read_csv(args.df, sep='\t')
+                print(df)
                 _ = df['mhc_seq']
             except:
                 df = pd.read_csv(args.df)
+                print(df)
                 _ = df['mhc_seq']
+
+
             df['mhc_seq'] = [''.join([aa.upper() for aa in seq if aa.upper() in AMINO_ACIDS]) for seq in df['mhc_seq'].tolist()]  # remove gaps from df:
             if args.multiple_anchors:
                 L1 = len(df)