Evidence map›Paper›PMID 34236625›Full record

ArticleInterdisciplinary sciences, computational life sciences2021

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Xianfang Wang, Yifeng Liu, Zhiyong Du, Mingdong Zhu, Aman Chandra Kaushik, Xue Jiang, Dongqing Wei

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Article in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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7 authors.

Xianfang WangSchool of Computer Science and Technology, Henan Institute of Technology, Xinxiang, 453003, China. 2wangfang@163.com.ORCID http://orcid.org/0000-0001-8355-1470
Yifeng LiuSchool of Management, Henan Institute of Technology, Xinxiang, 453003, China.
Zhiyong DuSchool of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, 453003, China.
Mingdong ZhuSchool of Computer Science and Technology, Henan Institute of Technology, Xinxiang, 453003, China.
Aman Chandra KaushikWuxi School of Medicine, Jiangnan University, Wuxi, 214000, China.
Xue JiangSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Dongqing WeiSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. dqwei@sjtu.edu.cn.

Funding

the Key Research Area Grant of the Ministry of Science and Technology of China 2016YFA0501703the National Natural Science Foundation of China 32030063the National Natural Science Foundation of China 32070662the National Natural Science Foundation of China 61802116the National Natural Science Foundation of China 61832019the National Natural Science Foundation of China 62072157the Natural Science Foundation of Henan Province 202300410102The PhD Start-up Fund of Henan Institute of Technology KQ2002
6 · The paper itself

Abstract

backgroundPrediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models.

methodsThe redundancy of raw protein is reduced by CD-HIT. The four subsequences are built from protein sequence: one global and three locals. The global subsequence is the entire protein sequence, and these local subsequences are obtained by moving a sliding window with some rules. Using G-gap to extract the features of the above four subsequences, a mixed matrix is constructed as the input of one channel which is composed of three-layer convolutional operating. Additional features are extracted by SCRATCH tool as input of another channel, which is consist of a single convolution in order to find hidden relationships and improve the accuracy of predictor. The outputs of two parallel channels are concatenated as the input of the hidden layer. And the prediction of protein solubility is obtained in the output layer. The best protein solubility prediction model is obtained by doing some comparative experiments of different frameworks.

resultsThe performance indicators of DDcCNN model (our designed) are as follows: accuracy of 77.82%, Matthew's correlation coefficient of 0.57, sensitivity of 76.13% and specificity of 79.32%. The results of some comparative experiments show that the overall performance of DDcCNN model is better than existing models (GCNN, LCNN and PCNN). The related models and data are publicly deposited at http://www.ddccnn.wang .

conclusionThe satisfactory performance of DDcCNN model reveals that these features and flexible computational methodologies can reinforce the existing prediction models for better prediction of protein solubility could be applied in several applications, such as to preselect initial targets that are soluble or to alter solubility of target proteins, thus can help to reduce the production cost.

Indexed as

Neural Networks, ComputerProteinsAmino Acid SequenceProtein DomainsSolubilityProteinsDeep dual-channel convolutional neural networkDeep learningFeature fusionProtein solubility

Identifiers

PMID34236625

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