ASAP: a machine learning framework for local protein properties | JoVE Visualize

Area of Science:

Computational Biology
Bioinformatics
Machine Learning in Proteomics

Background:

Accurate prediction of residue-level protein properties, including post-translational modifications (PTMs), is crucial for understanding protein function.
Experimental methods for PTM identification are resource-intensive, and traditional computational approaches struggle with low sequence similarity.
Machine Learning (ML) offers a powerful alternative for annotating protein properties and predicting modifications.

Purpose of the Study:

To introduce ASAP (Amino-acid Sequence Annotation Prediction), a universal ML framework for predicting residue-level protein properties.
To demonstrate the framework's versatility by developing CleavePred, a model for predicting protein precursor cleavage sites.
To provide a scalable and efficient tool for proteomic analysis and protein design.

Main Methods:

ASAP extracts diverse features from amino acid sequences, including optional integration of external data like secondary structure and PSSM profiles.
ML classifiers are trained using these extracted features to predict various residue-level properties.
CleavePred, an ASAP-based model, was specifically developed and evaluated for predicting protein precursor cleavage sites.

Main Results:

ASAP enables rapid creation of new ML classifiers for diverse prediction tasks within minutes.
The CleavePred model achieved state-of-the-art performance in predicting protein precursor cleavage sites, outperforming existing rule-based methods.
The high accuracy of CleavePred addresses the limitations of current methods, which suffer from high false positive rates.

Conclusions:

ASAP provides a robust and adaptable baseline for residue-level protein sequence prediction.
CleavePred demonstrates the efficacy of the ASAP framework for specific PTM prediction tasks, with significant implications for genomic-scale proteome analysis.
Both ASAP and CleavePred are accessible as open-source tools with a Python API and a web-based application, facilitating broader research and application.