ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space

TL;DR

ProtNN is a fast, accurate, and scalable method for protein function prediction that uses graph embeddings and nearest neighbor classification, significantly outperforming existing approaches in speed and efficiency.

Contribution

This paper introduces ProtNN, a novel graph embedding-based approach for protein function prediction that achieves unprecedented speed and scalability compared to prior methods.

Findings

ProtNN accurately classifies protein functions across multiple datasets.

ProtNN runs thousands of times faster than state-of-the-art methods.

ProtNN scales efficiently to large datasets like the entire PDB.

Abstract

Studying the function of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the determination of the function of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the determination of protein functions in living cells. In this paper, we propose ProtNN, a novel approach for protein function prediction. Given an unannotated protein structure and a set of annotated proteins, ProtNN finds the nearest neighbor annotated structures based on protein-graph pairwise similarities. Given a query protein, ProtNN finds the nearest neighbor reference proteins based on a graph representation model and a pairwise similarity between vector embedding of both query and reference protein-graphs in structural and topological spaces. ProtNN assigns to the query protein the function with the highest number of votes across the set of k nearest neighbor reference proteins, where k is a user-defined parameter. Experimental evaluation demonstrates that ProtNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that ProtNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude of runtime compared to state-of-the-art approaches.