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

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Abstract

Annotation of protein functions plays an important role in understanding life at the molecular level. High throughput sequencing produces massive numbers of raw proteins sequences and only about 1% of them have been manually annotated with functions. Experimental annotations of functions are expensive, time-consuming and do not keep up with the rapid growth of the sequence numbers. This motivates development of computational approaches that predict protein functions. We propose a novel deep learning framework, DeepFunc, which accurately predicts protein functions from protein sequence- and network-derived information. More precisely, DeepFunc uses a long and sparse binary vector to encode information concerning domains, families and motifs collected from the InterPro tool that are associated with the input protein sequence. We process this vector with two neural layers to obtain a low-dimensional and dense vector which is combined with topological information extracted from publically available protein-protein interactions (PPIs) and functional linkages. The combined information is processed by a deep neural network that predicts protein functions. We empirically and comparatively test DeepFunc on a benchmark testing dataset and the CAFA3 dataset. The experimental results demonstrate that DeepFunc outperforms current methods on the testing dataset and that it secures the highest Fmax = 0.54 and AUC = 0.94 on the CAFA3 dataset. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.