Inspired by Kevin Kaichuang Yang's Machine-learning-for-proteins. In terms of the fast changing of protein design in DL, I started making this dynamic repository as a record of papers in this field for newcomers like me. My notes of these papers are shared in a Zhihu Column(simplified Chinese).
- List of papers about Proteins Design using Deep Learning
FLIP: Benchmark tasks in fitness landscape inference for proteins
Christian Dallago, Jody Mou, Kadina E Johnston, Bruce Wittmann, Nick Bhattacharya, Samuel Goldman, Ali Madani, Kevin K Yang
NeurIPS 2021 Datasets and Benchmarks Track || website
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
Zhangyang Gao, Cheng Tan, Stan Z. Li
BioRxiv
Sequence Database
Structure Database
- PDB
- AlphaFoldDB
- PDBbind
- ...
In terms of datasets and benchmarks, protein design is far less mature than drug discovery (paperwithcode drug discovery benchmarks). (Maybe should add the evaluation of protein design for deep learning method (especially deep generative model))
Difficulties and opportunities always coexist. Happy to see the work of Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang and Zhangyang Gao, Cheng Tan, Stan Z. Li. How grateful.
Deep learning in protein structural modeling and design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray
Patterns 1.9 || 2020
Deep generative modeling for protein design
Strokach, Alexey, and Philip M. Kim.
Current Opinion in Structural Biology || 2022
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang
Current Opinion in Chemical Biology || note || 2021
Structure-based protein design with deep learning
Ovchinnikov, Sergey, and Po-Ssu Huang.
Current opinion in chemical biology || note || 2021
Hallucination is inverting prediction model for design.
Design of proteins presenting discontinuous functional sites using deep learning
Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, View ORCID ProfileIvan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker
bioRxiv (2020)
De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, Theresa A. Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren Carter, Cameron M. Chow, Gaetano T. Montelione & David Baker
Nature (2021) || code || trRosetta ||
Protein sequence design by conformational landscape optimization
Norn, Christoffer, et al.
Proceedings of the National Academy of Sciences 118.11 (2021) || code
Fast differentiable DNA and protein sequence optimization for molecular design
Linder, Johannes, and Georg Seelig.
arXiv preprint arXiv:2005.11275 (2020)
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander M, Koo, Peter K, Ovchinnikov, Sergey
bioRxiv (2021) || Notes of mine
Deep learning methods for designing proteins scaffolding functional sites
Wang J, Lisanza S, Juergens D, Tischer D, Anishchenko I, Baek M, Watson JL, Chun JH, Milles LF, Dauparas J, Expòsit M, Yang W, Saragovi A, Ovchinnikov S, Baker D
bioRxiv(2021)
AlphaDesign: A de novo protein design framework based on AlphaFold
Jendrusch, Michael, Jan O. Korbel, and S. Kashif Sadiq.
bioRxiv (2021) ||
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design
Moffat, Lewis, Joe G. Greener, and David T. Jones.
bioRxiv (2021)
These models design backbone/scaffold/template.
HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints
Xuezhi Xie, Philip M. Kim
Machine Learning for Structural Biology Workshop, NeurIPS 2021 || without code
Conditioning by adaptive sampling for robust design
Brookes, David, Hahnbeom Park, and Jennifer Listgarten.
International conference on machine learning. PMLR, 2019 || without code
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network
Sabban, Sari, and Mikhail Markovsky.
F1000Research 9 (2020) || code || pyRosetta || tensorflow || maximizaing the fluorescence of a protein
Fully differentiable full-atom protein backbone generation
Anand Namrata, Raphael Eguchi, and Po-Ssu Huang.
OpenReview ICLR 2019 workshop DeepGenStruct || without code
IG-VAE: generative modeling of immunoglobulin proteins by direct 3D coordinate generation
Raphael R. Eguchi, Christian A. Choe, Po-Ssu Huang
Biorxiv (2020) || without code ||
Function-guided protein design by deep manifold sampling
Vladimir Gligorijevic, Stephen Ra, Daniel Berenberg, Richard Bonneau, Kyunghyun Cho
NeurIPS 2021 || without code
A backbone-centred energy function of neural networks for protein design
Huang, B., Xu, Y., Hu, X. et al
Nature (2022)
Identify amino sequence from given backbone/scaffold/template.
3D representations of amino acids—applications to protein sequence comparison and classification
Li, Jie, and Patrice Koehl.
Computational and structural biotechnology journal 11.18 (2014) || 2014
SPIN2: Predicting sequence profiles from protein structures using deep neural networks
O'Connell, James, et al.
Proteins: Structure, Function, and Bioinformatics 86.6 (2018)
Computational protein design with deep learning neural networks
Wang, Jingxue, et al.
Scientific reports 8.1 (2018)
Design of metalloproteins and novel protein folds using variational autoencoders
Greener, Joe G., Lewis Moffat, and David T. Jones.
Scientific reports 8.1 (2018)
To improve protein sequence profile prediction through image captioning on pairwise residue distance map
Chen, Sheng, et al.
Journal of chemical information and modeling 60.1 (2019)
ProDCoNN: Protein design using a convolutional neural network
Zhang, Yuan, et al.
Proteins: Structure, Function, and Bioinformatics 88.7 (2020)
A structure-based deep learning framework for protein engineering
Shroff, Raghav, et al.
bioRxiv (2019)
Protein sequence design with a learned potential
Anand-Achim, Namrata, et al.
Biorxiv (2021)
Fast and flexible protein design using deep graph neural networks.
Strokach, Alexey, et al.
Cell Systems 11.4 (2020)
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs
Li, Alex J., et al.
NeurIPS 2021
Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, Wengong, et al.
arXiv preprint arXiv:2110.04624 (2021)
De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks
Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen
Journal of chemical information and modeling 60.12 (2020) || code || gcWGAN
Generative models for graph-based protein design
John Ingraham, Vikas K Garg, Dr.Regina Barzilay, Tommi Jaakkola
NeurIPS 2019 || code
Rotamer-Free Protein Sequence Design Based on Deep Learning and Self-Consistency
Liu, Yufeng, et al.
Nature portfolio (2022)
DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet
Qi, Yifei, and John ZH Zhang.
Journal of chemical information and modeling 60.3 (2020)
These models generate sequences from expected function.
AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design
Shuhao Zhang, Youjun Xu, Jianfeng Pei, Luhua Lai
NeurIPS 2021
Variational auto-encoding of protein sequences
Sinai, Sam, et al.
arXiv preprint arXiv:1712.03346 (2017)
Deep generative models for T cell receptor protein sequences
Davidsen, Kristian, et al.
Elife 8 (2019)
How to hallucinate functional proteins
Costello, Zak, and Hector Garcia Martin.
arXiv preprint arXiv:1903.00458 (2019)
Conditioning by adaptive sampling for robust design
Brookes, David, Hahnbeom Park, and Jennifer Listgarten.
International conference on machine learning. PMLR, 2019
Generating functional protein variants with variational autoencoders
Hawkins-Hooker, Alex, et al.
PLoS computational biology 17.2 (2021)
Therapeutic enzyme engineering using a generative neural network
Giessel, Andrew, et al.
Scientific Reports 12.1 (2022)
Function-guided protein design by deep manifold sampling
Gligorijevic, Vladimir, et al.
bioRxiv (2021)
Deep generative models create new and diverse protein structures
Zeming, Tom, Yann and Alexander.
NeurIPS 2021
Generative modeling for protein structures
Anand, Namrata, and Possu Huang.
NeurIPS 2018
Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks
Chhibbar, Prabal, and Arpit Joshi.
arXiv preprint arXiv:1904.13240 (2019)
ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework
Han, Xi, et al.
Computers & Chemical Engineering 131 (2019)
Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions
Kucera, Tim, Matteo Togninalli, and Laetitia Meng-Papaxanthos
bioRxiv (2021)
Expanding functional protein sequence spaces using generative adversarial networks
Repecka, Donatas, et al.
Nature Machine Intelligence 3.4 (2021)
HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints
Xie, Xuezhi, and Philip M. Kim.
NeurIPS 2021
Recurrent neural network model for constructive peptide design
Müller, Alex T., Jan A. Hiss, and Gisbert Schneider.
Journal of chemical information and modeling 58.2 (2018)
Pepcvae: Semi-supervised targeted design of antimicrobial peptide sequences
Das, Payel, et al.
arXiv preprint arXiv:1810.07743 (2018)
Deep learning to design nuclear-targeting abiotic miniproteins
Schissel, Carly K., et al.
Nature Chemistry 13.10 (2021)
Protein design and variant prediction using autoregressive generative models
Shin, Jung-Eun, et al.
Nature communications 12.1 (2021) || code::SeqDesign || mutation effect prediction || sequence generation || April 2021
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Luo, Yunan, et al.
Nature communications 12.1 (2021)
Guided Generative Protein Design using Regularized Transformers
Castro, Egbert, et al.
arXiv preprint arXiv:2201.09948 (2022)
Generative Language Modeling for Antibody Design
Shuai, Richard W., Jeffrey A. Ruffolo, and Jeffrey J. Gray.
bioRxiv (2021)
Deep neural language modeling enables functional protein generation across families
Madani, Ali, et al.
bioRxiv (2021)
BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning
Prihoda, David, et al.
mAbs. Vol. 14. No. 1. Taylor & Francis, 2022
Accelerating protein design using autoregressive generative models
Riesselman, Adam, et al.
BioRxiv (2019)
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Khan, Asif, et al.
arXiv preprint(2022)
Progen: Language modeling for protein generation
Madani, Ali, et al.
arXiv preprint arXiv:2004.03497 (2020)
Model-based reinforcement learning for biological sequence design
Angermueller, Christof, et al.
International conference on learning representations. 2019
In consideration of learning more various of generative models for design, these recommended models from Molecular Design might be helpful.
Inverse design of 3d molecular structures with conditional generative neural networks
Gebauer, Niklas WA, et al.
arXiv preprint arXiv:2109.04824 (2021) || code || Sept 21
Differentiable scaffolding tree for molecular optimization
Fu, T., Gao, W., Xiao, C., Yasonik, J., Coley, C. W., & Sun, J.
arXiv preprint arXiv:2109.10469 || code || Sept 21
Structure-based de novo drug design using 3D deep generative models
Li, Yibo, Jianfeng Pei, and Luhua Lai.
Chemical science 12.41 (2021)
A 3D Generative Model for Structure-Based Drug Design
Luo, Shitong, et al.
Advances in Neural Information Processing Systems 34 (2021)
CELLS: Cost-Effective Evolution in Latent Space for Goal-Directed Molecular Generation
Chen, Zhiyuan, et al.
arXiv preprint arXiv:2112.00905 (2021)
Deciphering protein evolution and fitness landscapes with latent space models
Xinqiang Ding, Zhengting Zou & Charles L. Brooks III
Nature Communications || code || Dec 2019
The generative capacity of probabilistic protein sequence models
Francisco McGee, Sandro Hauri, Quentin Novinger, Slobodan Vucetic, Ronald M. Levy, Vincenzo Carnevale & Allan Haldane
Nature Communications || code::generation_capacity_metrics || code::sVAE || Nov 2021
Deep generative models of genetic variation capture the effects of mutations
Adam J. Riesselman, John B. Ingraham & Debora S. Marks
Nature Methods || code || Oct 2018
Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions
Amirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, David H. Brookes, Yijie Huang, O. Ozan Koyluoglu, Jennifer Listgarten & Kannan Ramchandran
Nature Communications || code || Sep 2021