🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk due to its direct use of os.system for shell execution, which can lead to arbitrary command execution. However, there is no evidence of malicious intent or obfuscation.

High shell risk due to os.system usage
Low maintenance and quality metadata

Per-check LLM notes

Network: Network calls to UniProt API are likely legitimate for fetching protein data.
Shell: Direct use of os.system suggests potential for executing arbitrary commands, indicating high risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintenance and potentially low-quality metadata, but there are no clear indicators of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

: urllib.request.urlretrieve("https://rest.uniprot.org/uniprotkb/"+protein+".
protein) urllib.request.urlretrieve("https://rest.uniprot.org/uniprotkb/"+protein+".

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

" os.system(cmd) os.system(cmd2)
) os.system(cmd2) os.system(cmd3)
) os.system(cmd3) need_msa.append(protein)
due files os.system(cmd) int_score[protein] = dict() #remove
in}*" os.system(cmd) need_msa.append(protein)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: univ-rennes.fr

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Quentin Rouger" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with HInt-ppi

Create a mini-application named 'HomologyInteractionExplorer' that leverages the HInt-ppi package to facilitate the exploration of homologous protein-protein interactions (PPIs) for researchers and bioinformaticians. This application will serve as a user-friendly interface to input protein sequences, identify potential homologous PPIs, and accelerate the process of structural modeling using AlphaFold.

Step 1: Design the User Interface
- Develop a simple yet intuitive web-based interface using Flask, a lightweight Python web framework. The UI should allow users to upload one or more FASTA files containing protein sequences.
- Include a section where users can select specific parameters for the HInt-ppi analysis, such as the desired homology threshold and the type of interaction predictions required.

Step 2: Implement the Core Functionality
- Utilize the HInt-ppi package to analyze the uploaded protein sequences. The application should automatically detect homologous proteins based on sequence similarity and predict their potential interactions.
- Integrate AlphaFold-based structural modeling to provide 3D models of the predicted interactions. Ensure that the application optimizes the use of HInt-ppi to speed up the structural modeling process.

Step 3: Enhance User Experience
- Provide visual representations of the predicted interactions within the application. Use libraries like PyMOL or NGL Viewer to render the 3D models directly in the browser.
- Implement a feature that allows users to save and share their results via downloadable reports or direct links.

Step 4: Add Advanced Features
- Incorporate a database of known PPIs to compare against the predictions made by the application. This can help in validating the accuracy of the homology-based predictions.
- Offer an option for users to submit their data for cloud-based processing if their local resources are insufficient, thereby expanding the computational capacity available to them.

Step 5: Testing and Deployment
- Thoroughly test the application with various sets of protein sequences to ensure reliability and accuracy of the predictions.
- Deploy the application on a cloud platform like AWS or Google Cloud, ensuring it is accessible to a wide audience and scalable to handle multiple concurrent users.