Structural Protein Function Prediction - A Comprehensive Review

Abstract

The large amounts of available protein structures emerges the need for computational methods for protein function prediction. Predicting protein function is mainly based on finding similarities between proteins with unknown function with already annotated proteins. This may be achieved using different protein characteristics: sequences, interactions, localization, structure and or psychochemical. A lot of review papers mainly focus on sequence and psychochemical featuresbased methods. This is because sequence and psychochemical data are easy to deal with and to interpret the results, and much available compared to protein structures. However, structure-based computational methods provide additional accuracy and reliability of protein function prediction. Therefore, unlike many review papers, this paper presents an up-to-date review on the structure-based protein function prediction. The aim was to provide a recent and comprehensive review of protein structure related topics: function aspects, structural classification, databases, tools and methods.

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

“…The crucial idea behind these methods is that proteins which share similar topological features in the PPI networks may share similar functions . Moreover, some protein function predictors utilize other types of data, such as genetic interactions, genomic context, protein structure, and gene expression . We focus on two classes of current predictors: sequence‐based methods that cover the use of domains, motifs and residue‐level information, and PPI‐based methods that rely on information extracted from these networks .…”