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SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network

Ming Chen,Y. Li,Yiheng Zhu,Fang Ge,Dong-Jun Yu

Published 2020 in Journal of Chemical Information and Modeling

ABSTRACT

There has been a significant improvement in protein residue contact prediction in recent years. Nevertheless, state-of-the-art methods still show deficiencies in the contact prediction of proteins with low-homology information. These top methods depend largely on statistical features that derived from homologous sequences, but previous studies, along with our analyses, show that they are insufficient for inferencing accurate contact-map for non-homology protein targets. To compensate, we proposed a brand-new Single-Sequence-based Contact predictor (SSCpred) that performs prediction through the Deep Fully Convolutional Network (Deep FCN) with only the target sequence itself, i.e., without additional homology information. The proposed pipeline makes good use of the target sequence by utilizing the pair-wise encoding technique and Deep FCN. Experimental results demonstrated that SSCpred can produce accurate predictions based on the proposed efficient pipeline. Compared with several most recent methods, SSCpred achieves completive performance on non-homology targets. Overall, we explored the possibilities of single-sequence-based contact prediction and designed a novel pipeline without using a complex and redundant feature set. The proposed SSCpred can compensate for current methods' disadvantages and achieve better performance on the non-homology targets. The web server of SSCpred is freely available at http://csbio.njust.edu.cn/bioinf/sscpred/.

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