Configure ImmuneBuilder pipeline for WES execution

- Update container image to harbor.cluster.omic.ai/omic/immunebuilder:latest
- Update input/output paths to S3 (s3://omic/eureka/immunebuilder/)
- Remove local mount containerOptions (not needed in k8s)
- Update homepage to Gitea repo URL
- Clean history to remove large model weight blobs

ImmuneBuilder/util.py

@@ -0,0 +1,136 @@
+from ImmuneBuilder.constants import res_to_num, atom_types, residue_atoms, restype_1to3, restypes
+
+import numpy as np
+import torch
+import requests
+import os
+
+
+def download_file(url, filename):
+    with requests.get(url, stream=True) as r:
+        r.raise_for_status()
+        with open(filename, 'wb+') as f:
+            for chunk in r.iter_content(chunk_size=8192): 
+                f.write(chunk)
+    return filename
+
+
+def get_one_hot(targets, nb_classes=21):
+    res = np.eye(nb_classes)[np.array(targets).reshape(-1)]
+    return res.reshape(list(targets.shape) + [nb_classes])
+
+
+def get_encoding(sequence_dict, chain_ids="HL"):
+    
+    encodings = []
+
+    for j,chain in enumerate(chain_ids):
+        seq = sequence_dict[chain]
+        one_hot_amino = get_one_hot(np.array([res_to_num(x) for x in seq]))
+        one_hot_region = get_one_hot(j * np.ones(len(seq), dtype=int), 2)
+        encoding = np.concatenate([one_hot_amino, one_hot_region], axis=-1)
+        encodings.append(encoding)
+
+    return np.concatenate(encodings, axis = 0)
+
+
+def find_alignment_transform(traces):
+    centers = traces.mean(-2, keepdim=True)
+    traces = traces - centers
+
+    p1, p2 = traces[0], traces[1:]
+
+    C = torch.einsum("i j k, j l -> i k l", p2, p1)
+    V, _, W = torch.linalg.svd(C)
+    U = torch.matmul(V, W)
+    U = torch.matmul(torch.stack([torch.ones(len(p2), device=U.device),torch.ones(len(p2), device=U.device),torch.linalg.det(U)], dim=1)[:,:,None] * V,  W)
+
+    return torch.cat([torch.eye(3, device=U.device)[None], U]), centers
+    
+
+def to_pdb(numbered_sequences, all_atoms, chain_ids = "HL"):
+    atom_index = 0
+    pdb_lines = []
+    record_type = "ATOM"
+    seq = numbered_sequences[chain_ids[0]] + numbered_sequences[chain_ids[1]]
+    chain_index = [0]*len(numbered_sequences[chain_ids[0]]) + [1]*len(numbered_sequences[chain_ids[1]])
+    chain_id = chain_ids[0]
+
+    for i, amino in enumerate(seq):
+        for atom in atom_types:
+            if atom in residue_atoms[amino[1]]:
+                j = residue_atoms[amino[1]].index(atom)
+                pos = all_atoms[i, j]
+                if pos.mean() != pos.mean():
+                    continue
+                name = f' {atom}'
+                alt_loc = ''
+                res_name_3 = restype_1to3[amino[1]]
+                if chain_id != chain_ids[chain_index[i]]:
+                    chain_id = chain_ids[chain_index[i]]
+                occupancy = 1.00
+                b_factor = 0.00
+                element = atom[0]
+                charge = ''
+                # PDB is a columnar format, every space matters here!
+                atom_line = (f'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
+                             f'{res_name_3:>3} {chain_id:>1}'
+                             f'{(amino[0][0]):>4}{amino[0][1]:>1}   '
+                             f'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
+                             f'{occupancy:>6.2f}{b_factor:>6.2f}          '
+                             f'{element:>2}{charge:>2}')
+                pdb_lines.append(atom_line)
+                atom_index += 1
+
+    return "\n".join(pdb_lines)
+
+
+def sequence_dict_from_fasta(fasta_file):
+    out = {}
+
+    with open(fasta_file) as file:
+        txt = file.read().split()
+
+    for i in range(len(txt)-1):
+        if ">" in txt[i]:
+            chain_id = txt[i].split(">")[1]
+        else:
+            continue
+
+        if all(c in restypes for c in txt[i+1]):
+            out[chain_id] = txt[i+1]
+    
+    return out
+
+
+def add_errors_as_bfactors(filename, errors, header=[]):
+
+    with open(filename) as file:
+        txt = file.readlines()
+
+    new_txt = [x for x in header]
+    residue_index = -1
+    position = "  "
+
+    for line in txt:
+        if line[:4] == "ATOM":
+            current_res = line[22:27]
+            if current_res != position:
+                position = current_res
+                residue_index += 1
+            line = line.replace("  0.00  ",f"{errors[residue_index]:>6.2f}  ")
+        elif "REMARK   1 CREATED WITH OPENMM" in line:
+            line = line.replace(" 1 CREATED WITH OPENMM", "STRUCTURE REFINED USING OPENMM")
+            line = line[:-1] + (81-len(line))*" " + "\n"
+        new_txt.append(line)
+
+    with open(filename, "w+") as file:
+        file.writelines(new_txt)
+
+
+def are_weights_ready(weights_path):
+    if not os.path.exists(weights_path) or os.path.getsize(weights_path) == 0:
+        return False
+    with open(weights_path, "rb") as f:
+        filestart = str(f.readline())
+    return filestart != "b'EMPTY'"