wangbing587 / deep_learning_in_proteomics

Deep learning in proteomics

This is a list of applications of deep learning methods in proteomics.

Table of contents:

• Peptide MS/MS spectrum prediction
• Peptide retention time prediction
• Protein post-translational modification site prediction
• MHC-peptide binding prediction

Peptide MS/MS spectrum prediction

1. pDeep

   • Source code: https://github.com/pFindStudio/pDeep
   • Pre-trained models: https://github.com/pFindStudio/pDeep
   • Reference:
     • Zeng, Wen-Feng, et al. "MS/MS spectrum prediction for modified peptides using pDeep2 trained by transfer learning." Analytical chemistry 91.15 (2019): 9724-9731.
     • Zhou, Xie-Xuan, et al. "pDeep: predicting MS/MS spectra of peptides with deep learning." Analytical chemistry 89.23 (2017): 12690-12697.

2. Prosit

   • Source code: https://github.com/kusterlab/prosit
   • Pre-trained models: https://www.proteomicsdb.org/prosit/
   • Prediction: https://www.proteomicsdb.org/prosit/, web server.
   • Reference:
     • Gessulat, Siegfried, et al. "Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning." Nature methods 16.6 (2019): 509-518.

3. DeepMass

   • Web: https://github.com/verilylifesciences/deepmass
   • Pre-trained models: DeepMass::Prism is provided as a service using Google Cloud Machine Learning Engine.
   • Reference:
     • Tiwary, Shivani, et al. "High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis." Nature methods 16.6 (2019): 519-525.

4. Predfull

   • Source code: https://github.com/lkytal/PredFull
   • Pre-trained models: https://github.com/lkytal/PredFull and http://predfull.com/
   • Reference:
     • Liu, Kaiyuan, et al. "Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network." Analytical Chemistry 92.6 (2020): 4275-4283.

5. Guan et al.

   • Source code: https://zenodo.org/record/2652602#.X16LZZNKhTZ
   • Pre-trained models: https://zenodo.org/record/2652602#.X16LZZNKhTZ
   • Reference:
     • Guan, Shenheng, Michael F. Moran, and Bin Ma. "Prediction of LC-MS/MS properties of peptides from sequence by deep learning." Molecular & Cellular Proteomics 18.10 (2019): 2099-2107.

6. MS²CNN

   • Source code: https://github.com/changlabtw/MS2CNN
   • Pre-trained models: https://github.com/changlabtw/MS2CNN
   • Reference:
     • Lin, Yang-Ming, Ching-Tai Chen, and Jia-Ming Chang. "MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks." BMC genomics 20.9 (2019): 1-10.

7. DeepDIA:

   • Source code: https://github.com/lmsac/DeepDIA/
   • Pre-trained models: https://github.com/lmsac/DeepDIA/
   • Reference:
     • Yang, Yi, et al. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics." Nature communications 11.1 (2020): 1-11.

Peptide retention time prediction

1. Prosit

   • Source code: https://github.com/kusterlab/prosit
   • Pre-trained models: https://www.proteomicsdb.org/prosit/
   • Prediction: https://www.proteomicsdb.org/prosit/, web server.
   • Reference:
     • Gessulat, Siegfried, et al. "Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning." Nature methods 16.6 (2019): 509-518.

2. DeepMass

   • Web: https://github.com/verilylifesciences/deepmass
   • Pre-trained models: DeepMass::Prism is provided as a service using Google Cloud Machine Learning Engine.
   • Reference:
     • Tiwary, Shivani, et al. "High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis." Nature methods 16.6 (2019): 519-525.

3. Guan et al.

   • Source code: https://zenodo.org/record/2652602#.X16LZZNKhTZ
   • Pre-trained models: https://zenodo.org/record/2652602#.X16LZZNKhTZ
   • Reference:
     • Guan, Shenheng, Michael F. Moran, and Bin Ma. "Prediction of LC-MS/MS properties of peptides from sequence by deep learning." Molecular & Cellular Proteomics 18.10 (2019): 2099-2107.

4. AutoRT

   • Source code: https://github.com/bzhanglab/AutoRT
   • Pre-trained models: https://github.com/bzhanglab/AutoRT
   • Reference:
     • Wen, Bo, et al. "Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis." Nature communications 11.1 (2020): 1-14.

5. DeepDIA:

   • Source code: https://github.com/lmsac/DeepDIA/
   • Pre-trained models: https://github.com/lmsac/DeepDIA/
   • Reference:
     • Yang, Yi, et al. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics." Nature communications 11.1 (2020): 1-11.

6. DeepRT:

   • Source code: https://github.com/horsepurve/DeepRTplus
   • Pre-trained models: https://github.com/horsepurve/DeepRTplus
   • Reference:
     • Ma, Chunwei, et al. "Improved peptide retention time prediction in liquid chromatography through deep learning." Analytical chemistry 90.18 (2018): 10881-10888.

7. DeepLC:

   • Source code: https://github.com/compomics/DeepLC
   • Pre-trained models: https://github.com/compomics/DeepLC
   • Reference:
     • Bouwmeester, Robbin, et al. "DeepLC can predict retention times for peptides that carry as-yet unseen modifications." BioRxiv (2020).

Peptide de novo sequencing

1. DeepNovo

   • Source code: https://github.com/nh2tran/DeepNovo
   • Pre-trained models: https://github.com/nh2tran/DeepNovo
   • Reference:
     • Tran, Ngoc Hieu, et al. "De novo peptide sequencing by deep learning." Proceedings of the National Academy of Sciences 114.31 (2017): 8247-8252.

2. DeepNovo-DIA

   • Source code: https://github.com/nh2tran/DeepNovo-DIA
   • Pre-trained models: https://github.com/nh2tran/DeepNovo-DIA
   • Reference:
     • Tran, Ngoc Hieu, et al. "Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry." Nature methods 16.1 (2019): 63-66.

3. SMSNet

   • Source code: https://github.com/cmb-chula/SMSNet
   • Pre-trained models: https://github.com/cmb-chula/SMSNet
   • Reference:
     • Karunratanakul, Korrawe, et al. "Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework." Molecular & Cellular Proteomics 18.12 (2019): 2478-2491.

Protein post-translational modification site prediction

1. DeepACE

   • Source code: https://github.com/jiagenlee/DeepAce
   • Reference:
     • Zhao, Xiaowei, et al. "General and species-specific lysine acetylation site prediction using a bi-modal deep architecture." IEEE Access 6 (2018): 63560-63569.

2. Deep-PLA

   • Web: http://deeppla.cancerbio.info/
   • Prediction: http://deeppla.cancerbio.info/
   • Reference:
     • Yu, Kai, et al. "Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation." Briefings in Bioinformatics (2019).

3. DeepAcet

   • Source code: https://github.com/Sunmile/DeepAcet
   • Reference:
     • Wu, Meiqi, et al. "A deep learning method to more accurately recall known lysine acetylation sites." BMC bioinformatics 20.1 (2019): 49.

4. DNNAce

   • Source code: https://github.com/QUST-AIBBDRC/DNNAce/
   • Reference:
     • Yu, Bin, et al. "DNNAce: Prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion." Chemometrics and Intelligent Laboratory Systems (2020): 103999.

5. pKcr

   • Web: http://www.bioinfogo.org/pkcr/
   • Prediction: http://www.bioinfogo.org/pkcr/
   • Reference:
     • Zhao, Yiming, Ningning He, Zhen Chen, and Lei Li. "Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks." IEEE Access 8 (2020): 14244-14252.

6. DeepGly

   • Reference:
     • Chen, Jingui, et al. "DeepGly: A Deep Learning Framework With Recurrent and Convolutional Neural Networks to Identify Protein Glycation Sites From Imbalanced Data." IEEE Access 7 (2019): 142368-142378.

7. Longetal2018

   • Reference:
     • Long, Haixia, et al. "A hybrid deep learning model for predicting protein hydroxylation sites." International Journal of Molecular Sciences 19.9 (2018): 2817.

8. MUscADEL

   • Web: http://muscadel.erc.monash.edu/
   • Prediction: http://muscadel.erc.monash.edu/
   • Reference:
     • Chen, Zhen, et al. "Large-scale comparative assessment of computational predictors for lysine post-translational modification sites." Briefings in bioinformatics 20.6 (2019): 2267-2290.

9. LEMP

   • Web: http://www.bioinfogo.org/lemp
   • Prediction: http://www.bioinfogo.org/lemp
   • Reference:
     • Chen, Zhen, et al. "Integration of a deep learning classifier with a random forest approach for predicting malonylation sites." Genomics, proteomics & bioinformatics 16.6 (2018): 451-459.

10. DeepNitro

    • Web: http://deepnitro.renlab.org/
    • Prediction: http://deepnitro.renlab.org/, both web server and standalone.
    • Reference:
      • Xie, Yubin, et al. "DeepNitro: prediction of protein nitration and nitrosylation sites by deep learning." Genomics, proteomics & bioinformatics 16.4 (2018): 294-306.

11. MusiteDeep

    • Source code: https://github.com/duolinwang/MusiteDeep
    • Pre-trained models: https://github.com/duolinwang/MusiteDeep
    • Reference:
      • Wang, Duolin, et al. "MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction." Bioinformatics 33.24 (2017): 3909-3916.

12. NetPhosPan

    • Web: https://services.healthtech.dtu.dk/service.php?NetPhospan-1.0
    • Prediction: https://services.healthtech.dtu.dk/service.php?NetPhospan-1.0, both web server and standalone.
    • Reference:
      • Fenoy, Emilio, et al. "A generic deep convolutional neural network framework for prediction of receptor–ligand interactions—NetPhosPan: application to kinase phosphorylation prediction." Bioinformatics 35.7 (2019): 1098-1107.

13. DeepPhos

    • Source code: https://github.com/USTC-HIlab/DeepPhos
    • Pre-trained models: https://github.com/USTC-HIlab/DeepPhos
    • Reference:
      • Luo, Fenglin, et al. "DeepPhos: prediction of protein phosphorylation sites with deep learning." Bioinformatics 35.16 (2019): 2766-2773.

14. EMBER

    • Source code: https://github.com/gomezlab/EMBER
    • Reference:
      • Kirchoff, Kathryn E., and Shawn M. Gomez. "EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning." BioRxiv (2020).

15. DeepKinZero

    • Source code: https://github.com/tastanlab/DeepKinZero
    • Pre-trained models: https://github.com/tastanlab/DeepKinZero
    • Reference:
      • Deznabi, Iman, et al. "DeepKinZero: zero-shot learning for predicting kinase–phosphosite associations involving understudied kinases." Bioinformatics 36.12 (2020): 3652-3661.

16. CapsNet_PTM

    • Source code: https://github.com/duolinwang/CapsNet_PTM
    • Pre-trained models: https://github.com/duolinwang/CapsNet_PTM
    • Reference:
      • Wang, Duolin, Yanchun Liang, and Dong Xu. "Capsule network for protein post-translational modification site prediction." Bioinformatics 35.14 (2019): 2386-2394.

17. GPS-Palm

    • Web: http://gpspalm.biocuckoo.cn
    • Prediction: http://gpspalm.biocuckoo.cn, standalone version.
    • Reference:
      • Ning, Wanshan, et al. "GPS-Palm: a deep learning-based graphic presentation system for the prediction of S-palmitoylation sites in proteins." Briefings in Bioinformatics (2020).

18. CNN-SuccSite

    • Web: http://csb.cse.yzu.edu.tw/CNN-SuccSite/
    • Prediction: http://csb.cse.yzu.edu.tw/CNN-SuccSite/, web server.
    • Reference:
      • Huang, Kai-Yao, Justin Bo-Kai Hsu, and Tzong-Yi Lee. "Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method." Scientific reports 9.1 (2019): 1-15.

19. DeepUbiquitylation

    • Source code: https://github.com/jiagenlee/deepUbiquitylation
    • Pre-trained models: https://github.com/jiagenlee/deepUbiquitylation
    • Reference:
      • He, Fei, et al. "Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture." BMC systems biology 12.6 (2018): 109.

20. DeepUbi

    • Source code: https://github.com/Sunmile/DeepUbi
    • Reference:
      • Fu, Hongli, et al. "DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins." BMC bioinformatics 20.1 (2019): 1-10.

MHC-peptide binding prediction

1. ConvMHC

   • Web: http://jumong.kaist.ac.kr:8080/convmhc
   • Prediction: http://jumong.kaist.ac.kr:8080/convmhc, web server.
   • Reference:
     • Han, Youngmahn, and Dongsup Kim. "Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction." BMC bioinformatics 18.1 (2017): 585.

2. HLA-CNN

   • Source code: https://github.com/uci-cbcl/HLA-bind
   • Pre-trained models: https://github.com/uci-cbcl/HLA-bind
   • Reference:
     • Vang, Yeeleng S., and Xiaohui Xie. "HLA class I binding prediction via convolutional neural networks." Bioinformatics 33.17 (2017): 2658-2665.

3. DeepMHC

   • Web: http://mleg.cse.sc.edu/deepMHC/
   • Prediction: http://mleg.cse.sc.edu/deepMHC/, web server.
   • Reference:
     • Hu, Jianjun, and Zhonghao Liu. "DeepMHC: Deep convolutional neural networks for high-performance peptide-MHC binding affinity prediction." bioRxiv (2017): 239236.

4. DeepSeqPan

   • Source code: https://github.com/pcpLiu/DeepSeqPan
   • Pre-trained models: https://github.com/pcpLiu/DeepSeqPan
   • Reference:
     • Liu, Zhonghao, et al. "DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction." Scientific reports 9.1 (2019): 1-10.

5. AI-MHC

   • Web: https://baras.pathology.jhu.edu/AI-MHC/index.html
   • Prediction: https://baras.pathology.jhu.edu/AI-MHC/index.html, web server.
   • Reference:
     • Sidhom, John-William, Drew Pardoll, and Alexander Baras. "AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions." bioRxiv (2018): 318881.

6. DeepSeqPanII

   • Source code: https://github.com/pcpLiu/DeepSeqPanII
   • Pre-trained models: https://github.com/pcpLiu/DeepSeqPanII
   • Reference:
     • Liu, Zhonghao, et al. "DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction." bioRxiv (2019): 817502.

7. MHCSeqNet

   • Source code: https://github.com/cmb-chula/MHCSeqNet
   • Pre-trained models: https://github.com/cmb-chula/MHCSeqNet
   • Reference:
     • Phloyphisut, Poomarin, et al. "MHCSeqNet: a deep neural network model for universal MHC binding prediction." BMC bioinformatics 20.1 (2019): 270.

8. MARIA

   • Web: https://maria.stanford.edu/
   • Prediction: https://maria.stanford.edu/, web server.
   • Reference:
     • Chen, Binbin, et al. "Predicting HLA class II antigen presentation through integrated deep learning." Nature biotechnology 37.11 (2019): 1332-1343.

9. MHCflurry

   • Source code: https://github.com/openvax/mhcflurry
   • Pre-trained models: https://github.com/openvax/mhcflurry
   • Reference:
     • O'Donnell, Timothy J., et al. "MHCflurry: open-source class I MHC binding affinity prediction." Cell systems 7.1 (2018): 129-132.

10. DeepHLApan

    • Source code: https://github.com/jiujiezz/deephlapan
    • Pre-trained models: https://github.com/jiujiezz/deephlapan
    • Prediction: http://biopharm.zju.edu.cn/deephlapan/, web server.
    • Reference:
      • Wu, Jingcheng, et al. "DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity." Frontiers in Immunology 10 (2019): 2559.

11. ACME

    • Source code: https://github.com/HYsxe/ACME
    • Pre-trained models: https://github.com/HYsxe/ACME
    • Reference:
      • Hu, Yan, et al. "ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks." Bioinformatics 35.23 (2019): 4946-4954.

12. EDGE

    • Source code: Supplementary data
    • Reference:
      • Bulik-Sullivan, Brendan, et al. "Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification." Nature biotechnology 37.1 (2019): 55-63.

13. CNN-NF

    • Source code: https://github.com/zty2009/MHC-I
    • Reference:
      • Zhao, Tianyi, et al. "Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature." Frontiers in Genetics 10 (2019).

14. MHCnuggets

    • Web: https://karchinlab.org/apps/appMHCnuggets.html
    • Source code: https://github.com/KarchinLab/mhcnuggets
    • Pre-trained models: https://github.com/KarchinLab/mhcnuggets
    • Reference:
      • Shao, Xiaoshan M., et al. "High-throughput prediction of MHC class i and ii neoantigens with MHCnuggets." Cancer Immunology Research 8.3 (2020): 396-408.

15. DeepNeo

    • Web: https://omics.kaist.ac.kr/resources
    • Reference:
      • Kim, Kwoneel, et al. "Predicting clinical benefit of immunotherapy by antigenic or functional mutations affecting tumour immunogenicity." Nature communications 11.1 (2020): 1-11.

16. DeepLigand

    • Source code: https://github.com/gifford-lab/DeepLigand
    • Pre-trained models: https://github.com/gifford-lab/DeepLigand
    • Reference:
      • Zeng, Haoyang, and David K. Gifford. "DeepLigand: accurate prediction of MHC class I ligands using peptide embedding." Bioinformatics 35.14 (2019): i278-i283.

17. PUFFIN

    • Source code: http://github.com/gifford-lab/PUFFIN
    • Pre-trained models: https://github.com/gifford-lab/PUFFIN
    • Reference:
      • Zeng, Haoyang, and David K. Gifford. "Quantification of uncertainty in peptide-MHC binding prediction improves high-affinity peptide Selection for therapeutic design." Cell systems 9.2 (2019): 159-166.

18. NeonMHC2

    • Web: https://neonmhc2.org/
    • Source code: https://bitbucket.org/dharjanto-neon/neonmhc2
    • Prediction: https://neonmhc2.org/, web server and standalone version.
    • Reference:
      • Abelin, Jennifer G., et al. "Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction." Immunity 51.4 (2019): 766-779.

19. USMPep

    • Source code: https://github.com/nstrodt/USMPep
    • Reference:
      • Vielhaben, Johanna, et al. "USMPep: universal sequence models for major histocompatibility complex binding affinity prediction." BMC bioinformatics 21.1 (2020): 1-16.

20. MHCherryPan

    • Reference:
      • Xie, Xuezhi, Yuanyuan Han, and Kaizhong Zhang. "MHCherryPan. a novel model to predict the binding affinity of pan-specific class I HLA-peptide." 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.

21. DeepAttentionPan

    • Source code: https://github.com/jjin49/DeepAttentionPan
    • Pre-trained models: https://github.com/jjin49/DeepAttentionPan
    • Reference:
      • Jin, Jing, et al. "Attention mechanism-based deep learning pan-specific model for interpretable MHC-I peptide binding prediction." bioRxiv (2019): 830737.