Mutation Impact Analysis - Analyze mutation impact: predict structure, predict mutations from sequence and structure, and check variant effects with Ensembl VEP. Use this skill for molecular biology tasks involving pred protein structure esmfold zero shot sequence prediction predict zero shot structure get vep hgvs. Combines 4 tools from 3 SCP server(s).
Discipline: Molecular Biology | Tools Used: 4 | Servers: 3
Analyze mutation impact: predict structure, predict mutations from sequence and structure, and check variant effects with Ensembl VEP.
pred_protein_structure_esmfold from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Modelzero_shot_sequence_prediction from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactorypredict_zero_shot_structure from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactoryget_vep_hgvs from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl{
"sequence": "MKTIIALSYIFCLVFA",
"hgvs": "ENSP00000269305.4:p.Val600Glu"
}
Note: Replace
<YOUR_SCP_HUB_API_KEY>with your own SCP Hub API Key. You can obtain one from the SCP Platform.
import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
"server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory",
"ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl"
}
async def connect(url, transport_type):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
read, write, _ = await transport.__aenter__()
ctx = ClientSession(read, write)
session = await ctx.__aenter__()
await session.initialize()
return session, ctx, transport
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
# Connect to required servers
sessions = {}
sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http")
sessions["server-1"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "sse")
sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")
# Execute workflow steps
# Step 1: Predict protein structure
result_1 = await sessions["server-3"].call_tool("pred_protein_structure_esmfold", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Predict mutations from sequence
result_2 = await sessions["server-1"].call_tool("zero_shot_sequence_prediction", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Predict mutations from structure
result_3 = await sessions["server-1"].call_tool("predict_zero_shot_structure", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Check variant effects with VEP
result_4 = await sessions["ensembl-server"].call_tool("get_vep_hgvs", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())