PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction.

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Associating Gene Ontology (GO) terms with proteins defines their functional characteristics and biological context.
• Complex protein function prediction often requires advanced computational expertise and deep learning knowledge.

Purpose of the Study:

• To introduce PRONTO-TK, a Python-based software toolkit.
• To democratize access to neural network-based protein function prediction workflows.
• To empower researchers with varying programming experience to utilize advanced deep learning for GO term annotation.

Main Methods:

• Development of PRONTO-TK, a Python toolkit with a graphical user interface (GUI).
• Integration of state-of-the-art Deep Learning architectures for protein function prediction.
• Demonstration of the toolkit's effectiveness through a running example and intuitive configuration.

Main Results:

• PRONTO-TK provides accessible protein function annotation using GO terms.
• The toolkit simplifies the generation of complex analyses via an intuitive GUI.
• Researchers can leverage deep learning for protein function prediction without building pipelines from scratch.

Conclusions:

• PRONTO-TK effectively democratizes access to advanced protein function prediction methods.
• The toolkit lowers the barrier to entry for utilizing deep learning in bioinformatics.
• It enables researchers to easily perform complex protein function annotation using GO terms.