We recently released a review about PSP models, named Protein Language Models and Structure Prediction: Connection and Progression, which aims to build the connections between pLMs and PSP, and recover the PSP methods: past, present, and future. However, the field changes fastly and there are so many new papers that needed to be recorded and categorized. Inspired by the previous work (Machine-learning-for-proteins), we build this repository to list related papers about pLMs and PSP methods.
- Structural features include 1D features (SS, SA, torsion angles, contact density, etc.) and 2D features (contact map and distance map)
- Others in this contents include work of protein structure generation, structure refinement, etc.
I-TASSER[server]
D-I-TASSER[server] rank first in CASP15
I-TASSER-MTD[server]
QUARK[server]
OPUS-X[code]
OPUSFold[code]
Openfold[code]
FUpred[server]
ThreaDom[server]
DEMO[server]
EquiFold[paper]
DeepPotential[server]
TripletRes[server]
ResPRE[server]
AlphaFold-Multimer[paper][code]
xTrimoMultimer[code]
xTrimoABFold[paper]
xTrimoDock[paper]
Controllable protein design with language models[paper]
Ferruz, N., Höcker, B
Nature Machine Intelligence (2022)
Advancing protein language models with linguistics: a roadmap for improved interpretability[paper]
Mai Ha Vu, Rahmad Akbar, Philippe A Robert, Bartlomiej Swiatczak, Geir Kjetil Sandve, Victor Greiff, Dag Trygve and Truslew Haug.
Preprint, July 2022
Different methods, techniques and their limitations in protein structure prediction: A review[paper]
Vrushali Bongirwar and A.S. Mokhade
Progress in Biophysics & Molecular Biology (2022)
The road to fully programmable protein catalysis. [paper]
Sarah L. Lovelock, Rebecca Crawshaw, Sophie Basler, Colin Levy, David Baker, Donald Hilvert, Anthony P. Green.
Nature, June 2022.
Applications of artificial intelligence to enzyme and pathway design for metabolic engineering. [paper]
Woo Dae Jang, Gi Bae Kim, Yeji Kim, Sang Yup Lee.
Current Opinion in Biotechnology, February 2022.
Adaptive machine learning for protein engineering.
Brian L. Hie, Kevin K. Yang.
Current Opinion in Structural Biology, February 2022.
[10.1016/j.sbi.2021.11.002]
The language of proteins: NLP, machine learning & protein sequences
.[Paper]
Dan Ofer, Nadav Brandes and Michal Linial
Computational and structural biotechnology journal (2021)
A Review of Protein Structure Prediction using Deep Learning.[Paper]
Meredita Susanty, Tati Erawati Rajab and Rukman Hertadi.
BIO Web of Conferences 41, 04003 (2021)
Machine learning in protein structure prediction[paper]
Author links open overlay panelMohammedAlQuraishi
Current Opinion in Chemical Biology (2021)
Pre-trained Language Models in Biomedical Domain: A Systematic Survey[paper]
Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li and Jie Fu
arXiv: Computation and Language, Oct 2021
Learning the protein language: Evolution, structure, and function[paper][code]
Tristan Bepler and Bonnie Berger
Cell systems (2021)
AI challenges for predicting the impact of mutations on protein stability. paper
Fabrizio Pucci, Martin Schwersensky, Marianne Rooman.
Preprint, Nov 2021.
Representation learning applications in biological sequence analysis.[paper]
Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada, Natsuki Iwano, Shunsuke Sumi, Shion Hosoda, Shitao Zhao, Tsukasa Fukunaga, Michiaki Hamada.
Computational and Structural Biotechnology Journal, May 2021.
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?. [paper]
Elodie Laine, Stephan Eismann, Arne Elofsson, Sergei Grudinin.
Preprint, May 2021.
Deep learning methods in protein structure prediction.[paper]
Mirko Torrisi, Gianluca Pollastri and Quan Le.
Computational and structural biotechnology journal (2020)
Learning from protein fitness landscapes: a review of mutability, epistasis, and evolution[Paper]
Emily C. Hartman and Danielle Tullman-Ercek.
Current Opinion in Systems Biology (2019)
Machine-learning-guided directed evolution for protein engineering[paper]
Kevin K. Yang, Zachary Wu & Frances H. Arnold
Nature Methods (2019)
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding[paper]
Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu and Jian Tang
arXiv Sep 2022
Learning functional properties of proteins with language models[paper][code]
Serbulent Unsal, Heval Atas, Muammer Albayrak, Kemal Turhan, Aybar C. Acar & Tunca Doğan
Nature Machine Intelligence (2022)
Evaluating Protein Transfer Learning with TAPE. [paper]
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song.
Preprint, June 2019.
SPOT-1D-LM: Reaching Alignment-profile-based Accuracy in Predicting Protein Secondary and Tertiary Structural Properties without Alignment[paper]
Jaspreet Singh, Kuldip K. Paliwal, Jaswinder Singh and Yaoqi Zhou.
scientific reports (2022)
Single Layers of Attention Suffice to Predict Protein Contacts[paper]
Nicholas Bhattacharya, Neil Thomas, Roshan Rao, Justas Daupras, Peter K. Koo, David Baker, Yun S. Song and Sergey Ovchinnikov.
bioRxiv (2021)
SPOT-Contact-Single: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model[paper][code]
Jaspreet Singh, Thomas Litfin, Jaswinder Singh, Kuldip K. Paliwal and Yaoqi Zhou.
bioRxiv (2021)
Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.[paper][server]
Yumeng Yan and Sheng-You Huang.
Briefings in Bioinformatics (2021)
Study of real-valued distance prediction for protein structure prediction with deep learning[paper]
Jin Li and Jinbo Xu.
Bioinformatics (2021)
Multi-task deep learning for concurrent prediction of protein structural properties[paper][server]
Buzhong Zhang, Jinyan Li, Lijun Quan and Qiang Lyu.
bioRxiv (2021)
SPOT-1D-Single: Improving the Single-Sequence-Based Prediction of Protein Secondary Structure, Backbone Angles, Solvent Accessibility and Half-Sphere Exposures using a Large Training Set and Ensembled Deep Learning.[paper][server]
Jaspreet Singh, Thomas Litfin, Kuldip K. Paliwal, Jaswinder Singh, Anil Kumar Hanumanthappa and Yaoqi Zhou.
Bioinformatics (2021)
OPUS-TASS: A Protein Backbone Torsion Angles and Secondary Structure Predictor Based on Ensemble Neural Networks[paper][github]
Gang Xu, Qinghua Wang and Jianpeng Ma.
Bioinformatics(2020)
Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments[paper]
Shaun M. Kandathil, Joe G Greener, Andy M. Lau and David T. Jones.
bioRxiv (2020)
Template-based prediction of protein structure with deep learning[paper][code]
Haicang Zhang and Yufeng Shen.
BMC Genomics (2020)
Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction[paper]
Chen Chen, Tianqi Wu, Zhiye Guo and Jianlin Cheng.
Proteins (2020)
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks[paper][server]
Yang Li, Chengxin Zhang, Eric W. Bell, Wei Zheng, Xiao-Gen Zhou, Dong-Jun Yu and Yang Zhang.
PLOS Computational Biology (2020)
REALDIST: Real-valued protein distance prediction[paper][code]
Badri Adhikari.
bioRxiv (2020)
A fully open-source framework for deep learning protein real-valued distances[paper][code]
Badri Adhikari.
Scientific Reports (2020)
DeepDist: real-value inter-residue distance prediction with deep residual convolutional network[paper][code]
Tianqi Wu, Zhiye Guo, Jie Hou and Jianlin Cheng.
BMC Bioinformatics (2020)
A multi-task deep-learning system for predicting membrane associations and secondary structures of proteins[paper]
Bian Li, Jeffrey L. Mendenhall, John A. Capra and Jens Meiler.
Journal of Proteome Research (2020)
PconsC4: fast, accurate and hassle-free contact predictions.[paper][code]
Mirco Michel, David Menéndez Hurtado and Arne Elofsson.
Bioinformatics (2019)
DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences[paper][code]
Nina Pörner, Ehsaneddin Asgari, Alice C. McHardy and Mohammad R. K. Mofrad.
bioRxiv (2019)
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints[paper][code]
Joe G Greener, Shaun M. Kandathil and David T. Jones.
Nature Communications (2018)
Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks[paper][server]
Jack Hanson, Kuldip K. Paliwal, Thomas Litfin, Yuedong Yang and Yaoqi Zhou.
Bioinformatics (2018)
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.[paper][code]
David T. Jones and Shaun M. Kandathil.
Bioinformatics (2018)
DeepCDpred: Inter-residue Distance and Contact Prediction for Improved Prediction of Protein Structure.[paper]
Shuangxi Ji, Tuğçe Oruç, Liam Mead, Muhammad Fayyaz ur Rehman, Christopher M. Thomas, Sam Butterworth and Peter J. Winn.
PLOS ONE (2018)
Improved protein contact predictions with the MetaPSICOV2 server in CASP12[paper][server]
Daniel W. A. Buchan and David T. Jones.
Proteins (2018)
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning[paper][server]
Michael Schantz Klausen, Martin Closter Jespersen, Henrik Nielsen, Kamilla Kjærgaard Jensen, Vanessa Isabell Jurtz, Casper Kaae Sønderby, Morten Otto Alexander Sommer, Ole Winther, Morten Nielsen, Bent O. Petersen and Paolo Marcatili.
Proteins (2018)
Porter 5: state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes[paper][server]
Mirko Torrisi, Manaz Kaleel and Gianluca Pollastri.
bioRxiv (2018)
Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning[paper][server]
Rhys Heffernan, Kuldip K. Paliwal, James Lyons, Jaswinder Singh, Yuedong Yang and Yaoqi Zhou.
Journal of Computational Chemistry (2018)
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks[paper][code]
Yang Liu, Perry Palmedo, Qing Ye, Bonnie Berger and Jian Peng.
Cell systems (2017)
DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks[paper][code]
Badri Adhikari, Jie Hou and Jianlin Cheng.
Bioinformatics (2017)
Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.[paper][server]
Rhys Heffernan, Yuedong Yang, Kuldip K. Paliwal and Yaoqi Zhou.
Bioinformatics (2017)
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model[paper]
Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang and Jinbo Xu.
PLOS Computational Biology (2016)
A hybrid method for prediction of protein secondary structure based on multiple artificial neural networks[paper]
Haris Hasic, Emir Buza and Amila Akagic.
international convention on information and communication technology electronics and microelectronics (2017)
RaptorX-Property: a web server for protein structure property prediction[paper][server]
Li Wei, Sheng Wang, Shiwang Liu and Jinbo Xu.
Nucleic Acids Research (2016)
MUST-CNN: a multilayer shift-and-stitch deep convolutional architecture for sequence-based protein structure prediction[paper]
Zeming Lin, Jack Lanchantin and Yanjun Qi.
national conference on artificial intelligence (2016)
Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning[paper]
Akosua Busia, Jasmine Collins and Navdeep Jaitly.
arXiv: Learning (2016)
A deep learning network approach to ab initio protein secondary structure prediction[paper]
Matthew Spencer, Jesse Eickholt and Jianlin Cheng.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2015)
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.[paper][server]
Rhys Heffernan, Kuldip K. Paliwal, James Lyons, Abdollah Dehzangi, Alok Sharma, Jihua Wang, Abdul Sattar, Yuedong Yang and Yaoqi Zhou.
Scientific Reports (2015)
Protein Secondary Structure Prediction with Long Short Term Memory Networks[paper]
Søren Kaae Sønderby and Ole Winther.
arXiv: Quantitative Methods (2014)
SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity[paper][server]
Christophe Magnan and Pierre Baldi.
Bioinformatics (2014)
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction[paper]
Jian Zhou and Olga G. Troyanskaya.
international conference on machine learning (2014)
A dynamic Bayesian network approach to protein secondary structure prediction[paper]
Xin-Qiu Yao, Huaiqiu Zhu and Zhen-Su She.
BMC Bioinformatics (2008)
Protein secondary structure prediction with a neural network.[paper]
L H Holley and Martin Karplus.
Proceedings of the National Academy of Sciences of the United States of America (1989)
Improved prediction of protein secondary structure by use of sequence profiles and neural networks[paper]
Burkhard Rost and Chris Sander
Proceedings of the National Academy of Sciences of the United States of America (1993)
Two-Stage Distance Feature-based Optimization Algorithm for De novo Protein Structure Prediction[paper]
Gui-Jun Zhang, Wang Xiaoqi, Ma Laifa, Wang Liujing, Jun Hu and Xiao-Gen Zhou.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020)
Secondary Structure and Contact Guided Differential Evolution for Protein Structure Prediction[paper]
Gui-Jun Zhang, Ma Laifa, Wang Xiaoqi and Xiao-Gen Zhou.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020)
Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction.[paper]
David Simoncini, Thomas Schiex and Kam Y. J. Zhang.
Proteins (2017)
Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta[paper]
Y C Liu, Sergey Ovchinnikov, David E. Kim, Ray Yu-Ruei Wang, Frank DiMaio and David Baker.
Proteins (2016)
UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling.[paper]
Debswapna Bhattacharya, Renzhi Cao and Jianlin Cheng.
Bioinformatics (2016)
RBO Aleph: leveraging novel information sources for protein structure prediction[paper]
Mahmoud Mabrouk, Ines Putz, Tim Werner, Michael Schneider, Moritz Neeb, Philipp Bartels and Oliver Brock.
Nucleic Acids Research (2015)
CONFOLD: Residue-residue contact-guided ab initio protein folding[paper]
Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao and Jianlin Cheng.
Proteins (2015)
Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.[paper]
Tatjana Braun, Julia Koehler Leman and Oliver F. Lange.
PLOS Computational Biology (2015)
De novo structure prediction of globular proteins aided by sequence variation-derived contacts.[paper]
Tomasz Kosciolek and David T. Jones.
PLOS ONE (2014)
Genomics-aided structure prediction[paper]
Joanna I. Sulkowska, Faruck Morcos, Martin Weigt, Terence Hwa and José N. Onuchic.
Proceedings of the National Academy of Sciences of the United States of America (2012)
A probabilistic fragment-based protein structure prediction algorithm[paper]
David Simoncini, Francois Berenger, Rojan Shrestha and Kam Y. J. Zhang.
PLOS ONE (2012)
Protein Structure Determination from Pseudocontact Shifts Using ROSETTA[paper]
Christophe Schmitz, Robert B. Vernon, Gottfried Otting, David Baker and Thomas Huber.
Journal of Molecular Biology (2012)
Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field[paper]
Dong Xu and Yang Zhang.
Proteins (2012)
Protein structure determination from NMR chemical shifts.[paper]
Andrea Cavalli, Xavier Salvatella, Christopher M. Dobson and Michele Vendruscolo.
Proceedings of the National Academy of Sciences of the United States of America (2007)
Protein Structure Prediction Using Rosetta[paper]
Carol A. Rohl, Charlie E. M. Strauss, Kira M.S. Misura and David Baker.
Methods in Enzymology (2004)
TOUCHSTONE II: a new approach to ab initio protein structure prediction.[paper]
Yang Zhang, Andrzej Kolinski and Jeffrey Skolnick.
Biophysical Journal (2003)
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions[paper]
Kim T. Simons, Charles Kooperberg, Enoch S. Huang and David Baker.
Journal of Molecular Biology (1997)
HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle[paper][code]
Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, Dianhai Yu, Fan Wang and Yanjun Ma.
arxiv (2022).
Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold[paper][code]
Ziyao Li, Xuyang Liu, Weijie Chen, Fan Shen, Hangrui Bi, Guolin Ke and Linfeng Zhang.
bioRxiv (2022).
PSP: Million-level Protein Sequence Dataset for Protein Structure Predictio[paper][code]
Sirui Liu, Jun Zhang, Haotian Chu, Min Wang, Boxin Xue, Ningxi Ni, Jialiang Yu, Yuhao Xie, Zhenyu Chen, Mengyun Chen, Yuan Liu, Piya Patra, Fan Xu, Jie Chen, Zidong Wang, Lijiang Yang, Fan Yu, Lei Chen and Yi Qin Gao.
arxiv (2022).
ColabFold - Making protein folding accessible to all[paper][code]
Milot Mirdita, Sergey Ovchinnikov and Martin Steinegger.
Nature Methods (2022)
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours[paper][code]
Shenggan Cheng, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng and Yang You.
arxiv (2022).
AlphaFold: Improved protein structure prediction using potentials from deep learning[paper][code]
Andrew Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David Jones, David Silver, Koray Kavukcuoglu and Demis Hassabis.
Nature (2022).
Few-Shot Learning of Accurate Folding Landscape for Protein Structure Prediction[paper]
Jun Zhang, Sirui Liu, Mengyun Chen, Haotian Chu, Min Wang, Zidong Wang, Jialiang Yu, Ningxi Ni, Fan Yu, Diqing Chen, Yi Isaac Yang, Boxin Xue, Lijiang Yang, Yuan Liu and Yi Qin Gao.
arxiv (2022).
OPUS-Rota4: A Gradient-Based Protein Side-Chain Modeling Framework Assisted by Deep Learning-Based Predictors[paper][github]
Gang Xu, Qinghua Wang and Jianpeng Ma.
bioRxiv(2021)
Highly accurate protein structure prediction with AlphaFold[paper][code]
Andrew M. Cowie, John M. Jumper, Richard O. Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russell Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, R. D. Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David L. Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli and Demis Hassabis.
Nature (2021)
Accurate prediction of protein structures and interactions using a three-track neural network[paper][code]
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose Henrique Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read and David Baker.
Science (2021)
Distillation of MSA Embeddings to Folded Protein Structures with Graph Transformers[paper]
Albert Costa, Manvitha Ponnapati, Joseph M. Jacobson and Pranam Chatterjee.
bioRxiv (2021)
Study of real-valued distance prediction for protein structure prediction with deep learning[paper]
Jin Li and Jinbo Xu.
Bioinformatics (2021)
Improved protein structure prediction using predicted inter-residue orientations[paper][code]
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov and David Baker.
Proceedings of the National Academy of Sciences of the United States of America (2019)
DESTINI: A deep-learning approach to contact-driven protein structure prediction.[paper][code]
Mu Gao, Hongyi Zhou and Jeffrey Skolnick.
Scientific Reports (2019)
End-to-End Differentiable Learning of Protein Structure[paper][code]
Mohammed AlQuraishi.
Cell systems (2018)
Language models of protein sequences at the scale of evolution enable accurate structure prediction[paper][code]
Allan dos Santos Costa, Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido and Alexander Rives.
biorxiv (2022).
High-resolution de novo structure prediction from primary sequence[paper][code]
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, Jianzhu Ma and Jian Peng.
biorxiv (2022).
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative[paper][code]
Xiaomin Fang, Fan Wang, Lihang Liu, Jingzhou He, Dayong Lin, Yingfei Xiang, Xiaonan Zhang, Hua Wu, Hui Li and Le Song.
arxiv (2022).
Single-sequence protein structure prediction using supervised transformer protein language models[paper]
Wenkai Wang, Zhenling Peng and Jianyi Yang.
bioxiv (2022).
EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation[paper]
Jae Hyeon Lee, Payman Yadollahpour, Andrew Watkins, Nathan C Frey, Andrew Leaver-Fay, Stephen Ra, Kyunghyun Cho, Vladimir Gligorijevic, Aviv Regev, Richard Bonneau, Prescient Design and Genentech.
bioxiv (2022).
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies[paper][code]
Jeffrey A. Ruffolo and Jeffrey J. Gray.
Biophysical Journal (2022)
Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA[paper][code]
Minkyung Baek, Ryan Mchugh, Ivan Anishchenko, David Baker and Frank Dimaio.
bioRxiv (2022).
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search.[paper]
Arne Elofsson, Patrick Bryant, Gabriele Pozzati, Wensi Zhu, Aditi Shenoy and Petras Kundrotas.
Nature Communications (2022)
Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments[paper]
Patrick Bryant, Gabriele Pozzati and Arne Elofsson.
*Nature Communications (2022) *
Improve the Protein Complex Prediction with Protein Language Models[paper]
Bo Chen, Ziwei Xie, Jinbo Xu, Jiezhong Qiu, Zhaofeng Ye and Jie Tang.
bioRxiv (2022).
Protein complex prediction with AlphaFold-Multimer[paper][code]
Andrew M. Cowie, Richard Evans, Michael J. O'Neill, Alexander Pritzel, Natasha Antropova, Andrew W. Senior, Tim Green, Augustin Žídek, Russell Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian Bodenstein, Michal Zielinski, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, R. D. Jain, Ellen Clancy, Pushmeet Kohli, John M. Jumper and Demis Hassabis.
bioRxiv (2021)
SE(3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation[paper]
Deqin Liu, Sheng Chen, Shuangjia Zheng, Sen Zhang and Yuedong Yang.
bioRxiv (2022).
SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2[paper][code]
Richard A. Stein and Hassane S. Mchaourab.
PLOS Computational Biology (2022)
Energy-based models for atomic-resolution protein conformation[paper][code]
Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus and Alexander Rives.
International Conference on Learning Representations (2020)
E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction[paper][code]
Tao Shen, Zhihang Hu, Zhangzhi Peng, Jiayang Chen, Peng Xiong, Liang Hong, Liangzhen Zheng, Yixuan Wang, Irwin King, Sheng Wang, Siqi Sun and Yu Li.
arxiv (2022).
After AlphaFold: protein-folding contest seeks next big breakthrough[paper]
Ewen Callaway
nature news article 2022
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models[paper]
Joseph L
bioRxiv, 2022.
Protein structure generation via folding diffusion[paper]
Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu and Ava P. Amini.
arxiv (2022).
Generative Modeling for Protein Structures[paper]
Namrata Anand and Po-Ssu Huang.
NIPS (2018)
Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph neural networks[paper]
Tianqi Wu and Chen Chen.
bioRxiv (2022).
Fast and effective protein model refinement using deep graph neural networks[paper][code]
Xiaoyang Jing and Jinbo Xu.
Nature Computational Science (2021)
Improved protein structure refinement guided by deep learning based accuracy estimation[paper][code]
Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, Ivan Anishchanka, Justas Dauparas and David Baker.
Nature Communications (2020)