Network-based protein structural classification

by   Arash Rahnama, et al.

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct ("raw") 3-dimensional (3D) structure-based protein features. Instead, we first model 3D structures as protein structure networks (PSNs). Then, we use ("processed") network-based features for PSC. We are the first ones to do so. We propose the use of graphlets, state-of-the-art features in many domains of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from the weighted PSNs. When evaluated on a large set of 9,509 CATH and 11,451 SCOP protein domains, our proposed approaches are superior to existing PSC approaches in terms of both accuracy and running time.


Weighted graphlets and deep neural networks for protein structure classification

As proteins with similar structures often have similar functions, analys...

A novel and effective scoring scheme for structure classification and pairwise similarity measurement

Protein tertiary structure defines its functions, classification and bin...

The divergence time of protein structures modelled by Markov matrices and its relation to the divergence of sequences

A complete time-parameterized statistical model quantifying the divergen...

CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval

Due to the advancements in technology number of entries in the structura...

DeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures

Motivation: Thanks to the recent advances in structural biology, nowaday...

Machine Learning for Classification of Protein Helix Capping Motifs

The biological function of a protein stems from its 3-dimensional struct...

Exact p-values for global network alignments via combinatorial analysis of shared GO terms

Network alignment aims to uncover topologically similar regions in the p...

Please sign up or login with your details

Forgot password? Click here to reset