"Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead." error code when ranking predicted structures of dimer

Expected Behavior

Attempting to predict the structure of a homodimer using colab with stock settings, plus amber relaxation of only the top-ranked final structure.

Current Behavior

After finishing the initial 5 predictions, it attempts to rerank models by 'multimer' metric but then stops and the titled error message appears.

Colabfold Output

2023-09-27 14:49:14,001 reranking models by 'multimer' metric
2023-09-27 14:49:16,964 Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.

Your Environment

Ran via hosted run-time (T4)

Executed code

#@title Input protein sequence(s), then hit Runtime -> Run all
from google.colab import files
import os
import re
import hashlib
import random

from sys import version_info
python_version = f"{version_info.major}.{version_info.minor}"

def add_hash(x,y):
return x+"_"+hashlib.sha1(y.encode()).hexdigest()[:5]

query_sequence = 'MSLMVSAGRGLGAVWSPTHVQVTVLQARGLRAKGPGGTSDAYAVIQVGKEKYATSVSERSLGAPVWREEA TFELPSLLSSGPAAAATLQLTVLHRALLGLDKFLGRAEVDLRDLHRDQGRRKTQWYKLKSKPGKKDKERG EIEVDIQFMRNNMTASMFDLSMKDKSRNPFGKLKDKIKGKNKDSGSDTASAIIPSTTPSVDSDDESVVKD KKKKSKIKTLLSKSNLQKTPLSQSMSVLPTSKPEKVLLRPGDFQSQWDEDDNEDESSSASDVMSHKRTAS TDLKQLNQVNFTLPKKEGLSFLGGLRSKNDVLSRSNVCINGNHVYLEQPEAKGEIKDSSPSSSPSPKGFR KKHLFSSTENLAAGSWKEPAEGGGLSSDRQLSESSTKDSLKSMTLPSYRPAPLVSGDLRENMAPANSEAT KEAKESKKPESRRSSLLSLMTGKKDVAKGSEGENPLTVPGREKEGMLMGVKPGEDASGPAEDLVRRSEKD TAAVVSRQGSSLNLFEDVQITEPEAEPESKSEPRPPISSPRAPQTRAVKPRLHPVKPMNAMATKVANCSL GTATIISENLNNEVMMKKYSPSDPAFAYAQLTHDELIQLVLKQKETISKKEFQVRELEDYIDNLLVRVME ETPNILRIPTQVGKKAGKM:MSLMVSAGRGLGAVWSPTHVQVTVLQARGLRAKGPGGTSDAYAVIQVGKEKYATSVSERSLGAPVWREEA TFELPSLLSSGPAAAATLQLTVLHRALLGLDKFLGRAEVDLRDLHRDQGRRKTQWYKLKSKPGKKDKERG EIEVDIQFMRNNMTASMFDLSMKDKSRNPFGKLKDKIKGKNKDSGSDTASAIIPSTTPSVDSDDESVVKD KKKKSKIKTLLSKSNLQKTPLSQSMSVLPTSKPEKVLLRPGDFQSQWDEDDNEDESSSASDVMSHKRTAS TDLKQLNQVNFTLPKKEGLSFLGGLRSKNDVLSRSNVCINGNHVYLEQPEAKGEIKDSSPSSSPSPKGFR KKHLFSSTENLAAGSWKEPAEGGGLSSDRQLSESSTKDSLKSMTLPSYRPAPLVSGDLRENMAPANSEAT KEAKESKKPESRRSSLLSLMTGKKDVAKGSEGENPLTVPGREKEGMLMGVKPGEDASGPAEDLVRRSEKD TAAVVSRQGSSLNLFEDVQITEPEAEPESKSEPRPPISSPRAPQTRAVKPRLHPVKPMNAMATKVANCSL GTATIISENLNNEVMMKKYSPSDPAFAYAQLTHDELIQLVLKQKETISKKEFQVRELEDYIDNLLVRVME ETPNILRIPTQVGKKAGKM' #@param {type:"string"}
#@markdown - Use : to specify inter-protein chainbreaks for modeling complexes (supports homo- and hetro-oligomers). For example PI...SK:PI...SK for a homodimer
jobname = 'RCP Homodimer Prediction Attempt 4' #@param {type:"string"}

number of models to use

num_relax = 1 #@param [0, 1, 5] {type:"raw"}
#@markdown - specify how many of the top ranked structures to relax using amber
template_mode = "none" #@param ["none", "pdb100","custom"]
#@markdown - none = no template information is used. pdb100 = detect templates in pdb100 (see notes). custom - upload and search own templates (PDB or mmCIF format, see notes)

use_amber = num_relax > 0

remove whitespaces

query_sequence = "".join(query_sequence.split())

basejobname = "".join(jobname.split())
basejobname = re.sub(r'\W+', '', basejobname)
jobname = add_hash(basejobname, query_sequence)

check if directory with jobname exists

def check(folder):
if os.path.exists(folder):
return False
else:
return True
if not check(jobname):
n = 0
while not check(f"{jobname}{n}"): n += 1
jobname = f"{jobname}{n}"

make directory to save results

os.makedirs(jobname, exist_ok=True)

save queries

queries_path = os.path.join(jobname, f"{jobname}.csv")
with open(queries_path, "w") as text_file:
text_file.write(f"id,sequence\n{jobname},{query_sequence}")

if template_mode == "pdb100":
use_templates = True
custom_template_path = None
elif template_mode == "custom":
custom_template_path = os.path.join(jobname,f"template")
os.makedirs(custom_template_path, exist_ok=True)
uploaded = files.upload()
use_templates = True
for fn in uploaded.keys():
os.rename(fn,os.path.join(custom_template_path,fn))
else:
custom_template_path = None
use_templates = False

print("jobname",jobname)
print("sequence",query_sequence)
print("length",len(query_sequence.replace(":","")))

Start with the supplied Colab notebook code and reproduce the hosted T4 run using the homodimer sequence and num_relax=1. Inspect the reranking step where the simtk.openmm warning appears; done means establishing whether this warning causes the run to stop and identifying the expected reranking outcome.