AFDP: An Automated Function Description Prediction Approach to Improve Accuracy of Protein Function Predictions

TL;DR

AFDP is a new integrated method that combines existing tools to improve the accuracy and readability of protein function predictions, effectively utilizing multiple homologs and curated descriptions.

Contribution

It introduces AFDP, which combines AHRD and eggNOG with the stCFExt algorithm to enhance protein function annotation accuracy and generate human-readable descriptions.

Findings

AFDP outperforms eggNOG and AHRD in accuracy on benchmark datasets.

The approach produces more precise and human-readable protein descriptions.

Integration of multiple tools improves generalization in protein function prediction.

Abstract

With the rapid growth in high-throughput biological sequencing technologies and subsequently the amount of produced omics data, it is essential to develop automated methods to annotate the functionality of unknown genes and proteins. There are developed tools such as AHRD applying known proteins characterization to annotate unknown ones. Some other algorithms such as eggNOG apply orthologous groups of proteins to detect the most probable function. However, while the available tools focus on the detection of the most similar characterization, they are not able to generalize and integrate information from multiple homologs while maintaining accuracy. Here, we devise AFDP, an integrated approach for protein function prediction which benefits from the combination of two available tools, AHRD and eggNOG, to predict the functionality of novel proteins and produce more precise human readable descriptions by applying our stCFExt algorithm. StCFExt creates function descriptions applying available manually curated descriptions in swiss-prot. Using a benchmark dataset we show that the annotations predicted by our approach are more accurate than eggNOG and AHRD annotations.