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Download fileSSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network
journal contribution
posted on 2020-05-15, 18:34 authored by Ming-Cai Chen, Yang Li, Yi-Heng Zhu, Fang Ge, Dong-Jun YuThere
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
an accurate contact map for nonhomology 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 efficient pipeline. Compared with
several most recent methods, SSCpred achieves completive performance
on nonhomology 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 achieves
better performance on the nonhomology targets. The web server of SSCpred
is freely available at http://csbio.njust.edu.cn/bioinf/sscpred/.