--- name: protein-property-comparison-4f785617bdef description: "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)." --- # Cross-Species Protein Comparison **Discipline**: Comparative Biology | **Tools Used**: 4 | **Servers**: 3 ## Description Compare proteins across species: get orthologs from NCBI, compute properties for each, and compare similarity. ## Tools Used - **`get_gene_orthologs`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` - **`calculate_protein_sequence_properties`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_smiles_similarity`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`get_homology_id`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` ## Workflow 1. Get orthologs from NCBI 2. Calculate properties for human protein 3. Calculate properties for mouse ortholog 4. Get Ensembl homology data ## Test Case ### Input ```json { "gene_id": 7157 } ``` ### Expected Steps 1. Get orthologs from NCBI 2. Calculate properties for human protein 3. Calculate properties for mouse ortholog 4. Get Ensembl homology data ## Usage Example > **Note:** Replace `` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python 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": ""}) 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()) ``` --- Source attribution: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/protein_property_comparison/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2024 Anthropic, PBC Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/protein_property_comparison/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research Roles: Data Scientist, Researcher Specialties: API Tools: Drugsda, Ensembl, Ncbi Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: ab40b91b2a076c4ac80de0f21a4e7386de3c1df02d76fbc91a9a38f30b8b9e84