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ProteinStructureWithDL

ProteinStructureWithDL aims to take an overview of the application of deep learning on protein structure prediction and design. We will gather recent published papers on this topic and relevant dataset here.

Papers and codes

Featured papers

Protein structure prediction

Protein structure design

Protein representation

  1. Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier, Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom and Rives, Alexander. "MSA Transformer". **, . (2021):
  2. Vig, Jesse and Madani, Ali and Varshney, Lav R. and Xiong, Caiming and Socher, Richard and Rajani, Nazneen Fatema. "BERTology Meets Biology: Interpreting Attention in Protein Language Models". **, . (2020):

Recent papers

Protein structure prediction

Protein structure design

Protein representation

Old papers

Protein structure prediction

  1. Yang, Jianyi and Anishchenko, Ivan and Park, Hahnbeom and Peng, Zhenling and Ovchinnikov, Sergey and Baker, David. "Improved protein structure prediction using predicted interresidue orientations". Proceedings of the National Academy of Sciences of the United States of America, 117.3 (2020): 1496--1503
  2. Gainza, P and Sverrisson, F and Monti, F and Rodol{`{a}}, E and Boscaini, D and Bronstein, M M and Correia, B E. "Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning". Nature Methods, 17.2 (2020): 184--192
  3. Adhikari, Badri. "A fully open-source framework for deep learning protein real-valued distances". Scientific Reports, 10.1 (2020):
  4. Stokes, Jonathan M. and Yang, Kevin and Swanson, Kyle and Jin, Wengong and Cubillos-Ruiz, Andres and Donghia, Nina M. and MacNair, Craig R. and French, Shawn and Carfrae, Lindsey A. and Bloom-Ackerman, Zohar and Tran, Victoria M. and Chiappino-Pepe, Anush and Badran, Ahmed H. and Andrews, Ian W. and Chory, Emma J. and Church, George M. and Brown, Eric D. and Jaakkola, Tommi S. and Barzilay, Regina and Collins, James J.. "A Deep Learning Approach to Antibiotic Discovery". Cell, 180.4 (2020): 688--702.e13
  5. Bornschein, J{"{o}}rg and Visin, Francesco and Osindero, Simon. "Small Data, Big Decisions: Model Selection in the Small-Data Regime". **, . (2020):
  6. Senior, Andrew W. and Evans, Richard and Jumper, John and Kirkpatrick, James and Sifre, Laurent and Green, Tim and Qin, Chongli and {\v{Z}}{'{i}}dek, Augustin and Nelson, Alexander W.R. and Bridgland, Alex and Penedones, Hugo and Petersen, Stig and Simonyan, Karen and Crossan, Steve and Kohli, Pushmeet and Jones, David T. and Silver, David and Kavukcuoglu, Koray and Hassabis, Demis. "Improved protein structure prediction using potentials from deep learning". Nature, 577.7792 (2020): 706--710
  7. Anishchenko, Ivan and Chidyausiku, Tamuka and Ovchinnikov, Sergey and Pellock, Samuel and Baker, David. "De novo protein design by deep network hallucination". bioRxiv, . (2020): 2020.07.22.211482
  8. Senior, Andrew W. and Evans, Richard and Jumper, John and Kirkpatrick, James and Sifre, Laurent and Green, Tim and Qin, Chongli and {\v{Z}}{'{i}}dek, Augustin and Nelson, Alexander W.R. and Bridgland, Alex and Penedones, Hugo and Petersen, Stig and Simonyan, Karen and Crossan, Steve and Kohli, Pushmeet and Jones, David T. and Silver, David and Kavukcuoglu, Koray and Hassabis, Demis. "Improved protein structure prediction using potentials from deep learning". Nature, 577.7792 (2020): 706--710
  9. Kandathil, Shaun M and Greener, Joe G and Jones, David T. "Recent developments in deep learning applied to protein structure prediction". **, 87.12 (2019): 1179--1189
  10. Kandathil, Shaun M and Greener, Joe G and Jones, David T. "Prediction of interresidue contacts with DeepMetaPSICOV in CASP13". Proteins: Structure, Function and Bioinformatics, 87.12 (2019): 1092--1099
  11. Xu, Jinbo and Wang, Sheng. "Analysis of distance‐based protein structure prediction by deep learning in CASP13". Proteins: Structure, Function, and Bioinformatics, 87.12 (2019): 1069--1081
  12. Kuhlman, B. and Bradley, P.. "Advances in protein structure prediction and design". Nature Reviews Molecular Cell Biology, 20.11 (2019):
  13. Li, Yang and Zhang, Chengxin and Bell, Eric W. and Yu, Dong‐Jun and Zhang, Yang. "Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13". Proteins: Structure, Function, and Bioinformatics, 87.12 (2019): 1082--1091
  14. Senior, Andrew W. and Evans, Richard and Jumper, John and Kirkpatrick, James and Sifre, Laurent and Green, Tim and Qin, Chongli and {\v{Z}}{'{i}}dek, Augustin and Nelson, Alexander W. R. and Bridgland, Alex and Penedones, Hugo and Petersen, Stig and Simonyan, Karen and Crossan, Steve and Kohli, Pushmeet and Jones, David T. and Silver, David and Kavukcuoglu, Koray and Hassabis, Demis. "Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)". Proteins: Structure, Function, and Bioinformatics, 87.12 (2019): 1141--1148
  15. Schaarschmidt, Joerg and Monastyrskyy, Bohdan and Kryshtafovych, Andriy and Bonvin, Alexandre M.J.J.. "Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age". Proteins: Structure, Function, and Bioinformatics, 86. (2018): 51--66
  16. Jones, David T. and Kandathil, Shaun M.. "High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features". Bioinformatics, 34.19 (2018): 3308--3315
  17. Anishchenko, Ivan and Ovchinnikov, Sergey and Kamisetty, Hetunandan and Baker, David. "Origins of coevolution between residues distant in protein 3D structures". Proceedings of the National Academy of Sciences of the United States of America, 114.34 (2017): 9122--9127
  18. MacKenzie, Craig O. and Zhou, Jianfu and Grigoryan, Gevorg. "Tertiary alphabet for the observable protein structural universe". Proceedings of the National Academy of Sciences of the United States of America, 113.47 (2016): E7438--E7447
  19. Feng, Xiang and Barth, Patrick. "A topological and conformational stability alphabet for multipass membrane proteins". Nature Chemical Biology, 12.3 (2016): 167--173
  20. Joosten, Robbie P. and {Te Beek}, Tim A.H. and Krieger, Elmar and Hekkelman, Maarten L. and Hooft, Rob W.W. and Schneider, Reinhard and Sander, Chris and Vriend, Gert. "A series of PDB related databases for everyday needs". Nucleic Acids Research, 39.SUPPL. 1 (2011): D411
  21. Kabsch, Wolfgang and Sander, Christian. "Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features". Biopolymers, 22.12 (1983): 2577--2637

Protein structure design

Protein representation

  1. Alley, Ethan C. and Khimulya, Grigory and Biswas, Surojit and AlQuraishi, Mohammed and Church, George M.. "Unified rational protein engineering with sequence-based deep representation learning". Nature Methods, 16.12 (2019): 1315--1322
  2. Bhattacharya, Nicholas and Thomas, Neil and Rao, Roshan and Dauparas, Justas and Koo, Peter K. and Baker, David and Song, Yun S. and Ovchinnikov, Sergey. "Single Layers of Attention Suffice to Predict Protein Contacts". **, . (2020):
  3. Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rihawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and Bhowmik, Debsindhu and Rost, Burkhard. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing". **, . ():
  4. Lu, Amy X. and Zhang, Haoran and Ghassemi, Marzyeh and Moses, Alan. "Self-Supervised Contrastive Learning of Protein Representations By Mutual Information Maximization". bioRxiv, . (2020): 2020.09.04.283929
  5. Rao, Roshan and Bhattacharya, Nicholas and Thomas, Neil and Duan, Yan and Chen, Xi and Canny, John and Abbeel, Pieter and Song, Yun S.. "Evaluating Protein Transfer Learning with TAPE". **, . ():
  6. Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier, Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom and Rives, Alexander. "MSA Transformer". **, . (2021):
  7. Rao, Roshan M. and Meier, Joshua and Sercu, Tom and Ovchinnikov, Sergey and Rives, Alexander. "Transformer protein language models are unsupervised structure learners". **, . (2020):
  8. Strodthoff, Nils and Wagner, Patrick and Wenzel, Markus and Samek, Wojciech. "UDSMProt: universal deep sequence models for protein classification". Bioinformatics (Oxford, England), 36.8 (2020): 2401--2409
  9. Vig, Jesse and Madani, Ali and Varshney, Lav R. and Xiong, Caiming and Socher, Richard and Rajani, Nazneen Fatema. "BERTology Meets Biology: Interpreting Attention in Protein Language Models". **, . (2020):
  10. Yang, Kevin K. and Wu, Zachary and Bedbrook, Claire N. and Arnold, Frances H.. "Learned protein embeddings for machine learning". Bioinformatics (Oxford, England), 34.15 (2018): 2642--2648

Datasets

Protein Data Bank (PDB) [website] DSSP [website] Membrane Protein of Known 3D Structure (mpstruc) [website]