diff --git a/utils/inference_utils.py b/utils/inference_utils.py
index dc5d09e71..ac11ec857 100644
--- a/utils/inference_utils.py
+++ b/utils/inference_utils.py
@@ -1,48 +1,39 @@
-import copy
 import os
-import pickle
-
-import torch
-from Bio.PDB import PDBParser
 from esm import FastaBatchedDataset, pretrained
 from rdkit.Chem import AddHs, MolFromSmiles
 from torch_geometric.data import Dataset, HeteroData
+import numpy as np
+import torch
+import prody as pr
 import esm
 
-from datasets.constants import three_to_one
 from datasets.process_mols import generate_conformer, read_molecule, get_lig_graph_with_matching, moad_extract_receptor_structure
+from datasets.parse_chi import aa_idx2aa_short, get_onehot_sequence
 
 
 def get_sequences_from_pdbfile(file_path):
-    biopython_parser = PDBParser()
-    structure = biopython_parser.get_structure('random_id', file_path)
-    structure = structure[0]
     sequence = None
-    for i, chain in enumerate(structure):
-        seq = ''
-        for res_idx, residue in enumerate(chain):
-            if residue.get_resname() == 'HOH':
-                continue
-            residue_coords = []
-            c_alpha, n, c = None, None, None
-            for atom in residue:
-                if atom.name == 'CA':
-                    c_alpha = list(atom.get_vector())
-                if atom.name == 'N':
-                    n = list(atom.get_vector())
-                if atom.name == 'C':
-                    c = list(atom.get_vector())
-            if c_alpha != None and n != None and c != None:  # only append residue if it is an amino acid
-                try:
-                    seq += three_to_one[residue.get_resname()]
-                except Exception as e:
-                    seq += '-'
-                    print("encountered unknown AA: ", residue.get_resname(), ' in the complex. Replacing it with a dash - .')
+
+    pdb = pr.parsePDB(file_path)
+    seq = pdb.ca.getSequence()
+    one_hot = get_onehot_sequence(seq)
+
+    chain_ids = np.zeros(len(one_hot))
+    res_chain_ids = pdb.ca.getChids()
+    res_seg_ids = pdb.ca.getSegnames()
+    res_chain_ids = np.asarray([s + c for s, c in zip(res_seg_ids, res_chain_ids)])
+    ids = np.unique(res_chain_ids)
+
+    for i, id in enumerate(ids):
+        chain_ids[res_chain_ids == id] = i
+
+        s_temp = np.argmax(one_hot[res_chain_ids == id], axis=1)
+        s = ''.join([aa_idx2aa_short[aa_idx] for aa_idx in s_temp])
 
         if sequence is None:
-            sequence = seq
+            sequence = s
         else:
-            sequence += (":" + seq)
+            sequence += (":" + s)
 
     return sequence