Sequence-based prediction of protein protein interaction using a deep-learning algorithm

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Abstract

Background

Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested.

Results

We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods.

Conclusions

To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1700-2) contains supplementary material, which is available to authorized users.

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Most cited references 28

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Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

Yuen Ho, Albrecht Gruhler, Adrian Heilbut … (2002)

The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.

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Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae.

Sean R Collins, Patrick Kemmeren, Xue-Chu Zhao … (2007)

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.

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Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

Yanzhi Guo, Lezheng Yu, Zhining Wen … (2008)

Compared to the available protein sequences of different organisms, the number of revealed protein–protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.

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Author and article information

Contributors

Jianfeng Pei:

ORCID: http://orcid.org/0000-0002-8482-1185

jfpei@pku.edu.cn

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 25 May 2017

Publication date PMC-release: 25 May 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 277

Affiliations

[1 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, , Peking University, ; Beijing, 100871 China

[2 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, Beijing National Laboratory for Molecular Science, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, , Peking University, ; Beijing, 100871 China

[3 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, Peking-Tsinghua Center for Life Sciences, , Peking University, ; Beijing, 100871 China

Author information

Jianfeng Pei http://orcid.org/0000-0002-8482-1185

Article

Publisher ID: 1700

DOI: 10.1186/s12859-017-1700-2

PMC ID: 5445391

PubMed ID: 28545462

SO-VID: 4dc177f5-dd6d-4e06-ac4e-072a893fdba7

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 6 March 2017

Date accepted : 18 May 2017

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002855, Ministry of Science and Technology of the People's Republic of China;

Award ID: 2016YFA0502303

Award Recipient : Luhua Lai

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 21673010

Award ID: 81273436

Award Recipient : Jianfeng Pei

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: deep learning,protein-protein interaction

Data availability:

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: deep learning, protein-protein interaction

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Sequence-based prediction of protein protein interaction using a deep-learning algorithm

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Abstract

Background

Results

Conclusions

Electronic supplementary material

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Computer Vision, Deep Learning, Deep Reinforcement Learning, IoT

Most cited references 28

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae.

Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

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Cited by 105

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