Cross-Species Protein Comparison - Compare proteins across species: get orthologs from NCBI, compute properties for each, and compare similarity. Use this skill for comparative biology tasks involving get gene orthologs calculate protein sequence properties calculate smiles similarity get homology id. Combines 4 tools from 3 SCP server(s).
Discipline: Comparative Biology | Tools Used: 4 | Servers: 3
Compare proteins across species: get orthologs from NCBI, compute properties for each, and compare similarity.
get_gene_orthologs from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBIcalculate_protein_sequence_properties from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Toolcalculate_smiles_similarity from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Toolget_homology_id from (streamable-http) - ensembl-serverhttps://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl{
"gene_id": 7157
}
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 = {
"ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI",
"server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool",
"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["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http")
sessions["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")
sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")
# Execute workflow steps
# Step 1: Get orthologs from NCBI
result_1 = await sessions["ncbi-server"].call_tool("get_gene_orthologs", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Calculate properties for human protein
result_2 = await sessions["server-2"].call_tool("calculate_protein_sequence_properties", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Calculate properties for mouse ortholog
result_3 = await sessions["server-2"].call_tool("calculate_smiles_similarity", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get Ensembl homology data
result_4 = await sessions["ensembl-server"].call_tool("get_homology_id", 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())