Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE.

Area of Science:

• Computational Biology
• Machine Learning
• Bioinformatics

Background:

• Machine learning for protein property prediction is advancing rapidly.
• Current methods are often disparate, limiting broad application and reproducibility.
• Predicting properties for protein variants and homologs requires flexible tools.

Purpose of the Study:

• To introduce Artificial Intelligence Driven protein Estimation (AIDE), a unified software package.
• To enable standardized instantiation, optimization, and testing of diverse protein property prediction models.
• To facilitate reproducible machine learning workflows for protein variants and homologs.

Main Methods:

• AIDE is a Python package compatible with scikit-learn API.
• It supports both zero-shot and supervised learning approaches.
• The software is designed for modularity and ease of integration into existing pipelines.

Main Results:

• AIDE provides a standardized API for various property prediction methods.
• It allows testing models on variable-length homologs and variants.
• The package is installable on major operating systems (Windows, Mac, Linux).

Conclusions:

• AIDE enhances the power and accessibility of machine learning in protein property prediction.
• Its modular design promotes reproducibility and simplifies the testing of diverse models.
• The software is available with comprehensive documentation and user guides.