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AspirinCode / awesome-BioMolFold

RNA, mRNA, DNA, Peptide, Protein, Antibody and Complex | Folding

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Folding Tool Collection

Menu Menu Menu Menu
AlphaFold AlphaFold 2 AlphaFold 3 AlphaFold-Multimer
Rosetta RoseTTAFold RoseTTAFold2 RoseTTAFold2NA
OpenFold Uni-Fold OmegaFold EquiFold
ESMFold

Based on the classification of biomolecules

Menu Menu Menu Menu
RNAFold mRNAFold DNAFold
peptideFold ProteinFold AntibodyFold
Protein-Protein ComplexFold Peptide-Protein ComplexFold RNA-Protein ComplexFold DNA-Protein ComplexFold
Antibody-Antigen ComplexFold

Recommendations and References

A collection of fold tools
https://github.com/biolists/folding_tools

Folding Tool Collection

AlphaFold

  • Improved protein structure prediction using potentials from deep learning [2020]
    Senior, A.W., Evans, R., Jumper, J. et al.
    Nature 577, 706–710 (2020) | code

AlphaFold 2

  • Highly accurate protein structure prediction with AlphaFold [2021]
    Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool et al.
    Nature 596, 583–589 (2021) | code

AlphaFold 3

  • Accurate structure prediction of biomolecular interactions with AlphaFold 3 [2024]
    Abramson, J., Adler, J., Dunger, J. et al.
    Nature (2024) | sever

AlphaFold-Multimer

  • Protein complex prediction with AlphaFold-Multimer [2021]
    JEvans, Richard, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žídek et al.
    biorxiv (2021) | code

  • Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search [2022]
    Bryant, P., Pozzati, G., Zhu, W. et al.
    Nat Commun 13, 6028 (2022) | code

Rosetta

  • Rosetta
    The Rosetta software suite includes algorithms for computational modeling and analysis of protein structures. It has enabled notable scientific advances in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes.
    Rosetta development began in the laboratory of Dr. David Baker at the University of Washington as a structure prediction tool but since then has been adapted to solve common computational macromolecular problems.

https://www.rosettacommons.org

RoseTTAFold

  • Accurate prediction of protein structures and interactions using a three-track neural network [2021]
    Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang et al.
    Science 373.6557 (2021) | code

RoseTTAFold2

  • Efficient and accurate prediction of protein structure using RoseTTAFold2 [2023]
    Baek, Minkyung, Ivan Anishchenko, Ian Humphreys, Qian Cong, David Baker, and Frank DiMaio.
    bioRxiv (2023)) | code

RoseTTAFold2NA

  • Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA [2024]
    Baek, Minkyung, Ryan McHugh, Ivan Anishchenko, Hanlun Jiang, David Baker, and Frank DiMaio.
    Nature Methods 21.1 (2024) | code

OpenFold

  • OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization [2022]
    Ahdritz, Gustaf, Nazim Bouatta, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell, Daniel Berenberg et al.
    Nat Methods (2024) | Biorxiv (2022) | code

Uni-Fold

  • Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold [2022]
    Li, Ziyao, Xuyang Liu, Weijie Chen, Fan Shen, Hangrui Bi, Guolin Ke, and Linfeng Zhang.
    bioRxiv (2022) | code

OmegaFold

  • High-resolution de novo structure prediction from primary sequence [2022]
    Wu, Ruidong, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su et al.
    bioRxiv (2022) | code

EquiFold

  • EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation [2022]
    Lee, Jae Hyeon, Payman Yadollahpour, Andrew Watkins, Nathan C. Frey, Andrew Leaver-Fay, Stephen Ra, Kyunghyun Cho, Vladimir Gligorijević, Aviv Regev, and Richard Bonneau.
    bioRxiv (2022) | code

ESMFold

  • Evolutionary-scale prediction of atomic-level protein structure with a language model [2023]
    Lin, Zeming, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin et al.
    Science 379.6637 (2023) | code

Based on the classification of biomolecules

RNAFold

  • RNA secondary structure prediction by learning unrolled algorithms [2020]
    Chen, Xinshi, Yu Li, Ramzan Umarov, Xin Gao, and Le Song.
    arXiv:2002.05810 (2020) | code

  • Geometric deep learning of RNA structure [2021]
    Townshend, Raphael JL, Stephan Eismann, Andrew M. Watkins, Ramya Rangan, Masha Karelina, Rhiju Das, and Ron O. Dror.
    Science 373.6558 (2021) | code

  • OpenComplex: RNA and protein-RNA complex models with high precision [2022]
    Jingcheng, Yu and Zhaoming, Chen and Zhaoqun, Li and Mingliang, Zeng and Wenjun, Lin and He, Huang and Qiwei, Ye.
    code

  • RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models [2024]
    Huang, Han, Ziqian Lin, Dongchen He, Liang Hong, and Yu Li.
    bioRxiv (2024) | code

  • RNAformer: A Simple Yet Effective Deep Learning Model for RNA Secondary Structure Prediction [2024]
    Franke, Joerg KH, Frederic Runge, Ryan Koeksal, Rolf Backofen, and Frank Hutter.
    bioRxiv (2024) | code

mRNAFold

DNAFold

  • Prediction of DNA origami shape using graph neural network [2024]
    Truong-Quoc, C., Lee, J.Y., Kim, K.S. et al.
    Nat. Mater. (2024) | code

peptideFold

  • PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture [2021]
    Xiao Cao, Wenjia He, Zitan Chen, Yifan Li, Kexin Wang, Hongbo Zhang, Lesong Wei, Lizhen Cui, Ran Su, Leyi Wei.
    Briefings in Bioinformatics (2021) | sever

  • Benchmarking AlphaFold2 on peptide structure prediction [2023]
    McDonald, Eli Fritz, Taylor Jones, Lars Plate, Jens Meiler, and Alican Gulsevin.
    Structure 31.1 (2023)

ProteinFold

  • Improved protein structure prediction using potentials from deep learning [2020]
    Senior, A.W., Evans, R., Jumper, J. et al.
    Nature 577, 706–710 (2020) | code

  • Highly accurate protein structure prediction with AlphaFold [2021]
    Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool et al.
    Nature 596, 583–589 (2021) | code

  • Accurate prediction of protein structures and interactions using a three-track neural network [2021]
    Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang et al.
    Science 373.6557 (2021) | code

  • OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization [2022]
    Ahdritz, Gustaf, Nazim Bouatta, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell, Daniel Berenberg et al.
    Biorxiv (2022) | code

  • Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold [2022]
    Li, Ziyao, Xuyang Liu, Weijie Chen, Fan Shen, Hangrui Bi, Guolin Ke, and Linfeng Zhang.
    bioRxiv (2022) | code

  • High-resolution de novo structure prediction from primary sequence [2022]
    Wu, Ruidong, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su et al.
    bioRxiv (2022) | code

  • EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation [2022]
    Lee, Jae Hyeon, Payman Yadollahpour, Andrew Watkins, Nathan C. Frey, Andrew Leaver-Fay, Stephen Ra, Kyunghyun Cho, Vladimir Gligorijević, Aviv Regev, and Richard Bonneau.
    bioRxiv (2022) | code

  • Evolutionary-scale prediction of atomic-level protein structure with a language model [2023]
    Lin, Zeming, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin et al.
    Science 379.6637 (2023) | code

  • Accurate structure prediction of biomolecular interactions with AlphaFold 3 [2024]
    Abramson, J., Adler, J., Dunger, J. et al.
    Nature (2024) | sever

AntibodyFold

  • Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies [2023]
    Ruffolo, Jeffrey A., Lee-Shin Chu, Sai Pooja Mahajan, and Jeffrey J. Gray.
    [Nature communications 14.1 (2023)](Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies) | code

Protein-Protein ComplexFold

  • Protein complex prediction with AlphaFold-Multimer [2021]
    JEvans, Richard, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žídek et al.
    biorxiv (2021) | code

  • Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search [2022]
    Bryant, P., Pozzati, G., Zhu, W. et al.
    Nat Commun 13, 6028 (2022) | code

  • HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights [2024]
    Fang, Xiaomin, Jie Gao, Jing Hu, Lihang Liu, Yang Xue, Xiaonan Zhang, and Kunrui Zhu.
    arXiv:2404.10260 (2024) | code

  • Accurate structure prediction of biomolecular interactions with AlphaFold 3 [2024]
    Abramson, J., Adler, J., Dunger, J. et al.
    Nature (2024) | sever

Peptide-Protein ComplexFold

  • Protein complex prediction with AlphaFold-Multimer [2021]
    JEvans, Richard, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žídek et al.
    biorxiv (2021) | code

  • Accurate structure prediction of biomolecular interactions with AlphaFold 3 [2024]
    Abramson, J., Adler, J., Dunger, J. et al.
    Nature (2024) | sever

  • Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA [2024]
    Baek, Minkyung, Ryan McHugh, Ivan Anishchenko, Hanlun Jiang, David Baker, and Frank DiMaio.
    Nature Methods 21.1 (2024) | code

RNA-Protein ComplexFold

  • OpenComplex: RNA and protein-RNA complex models with high precision [2022]
    Jingcheng, Yu and Zhaoming, Chen and Zhaoqun, Li and Mingliang, Zeng and Wenjun, Lin and He, Huang and Qiwei, Ye.
    code

  • Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA [2024]
    Baek, Minkyung, Ryan McHugh, Ivan Anishchenko, Hanlun Jiang, David Baker, and Frank DiMaio.
    Nature Methods 21.1 (2024) | code

DNA-Protein ComplexFold

  • Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA [2024]
    Baek, Minkyung, Ryan McHugh, Ivan Anishchenko, Hanlun Jiang, David Baker, and Frank DiMaio.
    Nature Methods 21.1 (2024) | code

Antibody-Antigen ComplexFold

  • AntiFold: Improved antibody structure design using inverse folding [2023]
    Wu, Fandi, Yu Zhao, Jiaxiang Wu, Biaobin Jiang, Bing He, Longkai Huang, Chenchen Qin et al.
    NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. (2023) | code

  • Fast and accurate modeling and design of antibody-antigen complex using tFold [2024]
    Wu, Fandi, Yu Zhao, Jiaxiang Wu, Biaobin Jiang, Bing He, Longkai Huang, Chenchen Qin et al.
    bioRxiv (2024) | sever

  • Accurate structure prediction of biomolecular interactions with AlphaFold 3 [2024]
    Abramson, J., Adler, J., Dunger, J. et al.
    Nature (2024) | sever

About

RNA, mRNA, DNA, Peptide, Protein, Antibody and Complex | Folding

alphafoldalphafold-multimeresmfoldopenfoldrosettafoldequifoldomegafolduni-fold

License:GNU General Public License v3.0