Protein Folding Resource Collection

In this repository you will find papers, tutorials and open-resource projects related to protein folding. The goal is to collect learning resources. This repository is contribution friendly, so if you want to add something, please feel free to submit a PR.

General Protein Folding

• Peng, Zhenling, Wenkai Wang, Hong Wei, Xiaoge Li, and Jianyi Yang. Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15. Proteins: Structure, Function, and Bioinformatics. 2023.

• Baek, Minkyung, Ivan Anishchenko, Ian Humphreys, Qian Cong, David Baker, and Frank DiMaio. Efficient and accurate prediction of protein structure using RoseTTAFold2. bioRxiv (2023): 2023-05. RoseTTAFold2

• Terwilliger, Thomas C., Billy K. Poon, Pavel V. Afonine, Christopher J. Schlicksup, Tristan I. Croll, Claudia Millán, Jane S. Richardson, Randy J. Read, and Paul D. Adams. Improved AlphaFold modeling with implicit experimental information. Nature methods 19, no. 11 (2022): 1376-1382.

• Xiaomin Fang, Fan Wang, Lihang Liu, Jingzhou He, Dayong Lin, Yingfei Xiang, Xiaonan Zhang, Hua Wu, Hui Li, Le Song. HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative. arXiv. 2022. HelixFold-Single

• Lin, Zeming, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa et al. Evolutionary-scale prediction of atomic level protein structure with a language model. bioRxiv (2022). ESMFold

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

• Robin Pearce,Yang Li,Gilbert S. Omenn,Yang Zhang. Fast and accurate Ab Initio Protein structure prediction using deep learning potentials PLOS Computational Biology. 2022. DeepFold

• Barrett, Thomas D., Amelia Villegas-Morcillo, Louis Robinson, Benoit Gaujac, David Admete, Elia Saquand, Karim Beguir, and Arthur Flajolet. So ManyFolds, So Little Time: Efficient Protein Structure Prediction With pLMs and MSAs. bioRxiv (2022): 2022-10. MonoFold/PolyFold

• Chowdhury, Ratul, Nazim Bouatta, Surojit Biswas, Charlotte Rochereau, George M. Church, Peter K. Sorger, and Mohammed AlQuraishi. Single-sequence protein structure prediction using language models from deep learning. bioRxiv (2021). RGN2

• Evans, Richard, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žídek et al. Protein complex prediction with AlphaFold-Multimer. BioRxiv (2022): 2021-10. AlphaFold-Multimer

• Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, no. 7873 (2021): 583-589. AlphaFold2

• Yang, Jianyi, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, and David Baker. Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences (PNAS) 117, no. 3 (2020): 1496-1503. trRosetta

• AlQuraishi, Mohammed. End-to-end differentiable learning of protein structure. Cell systems 8, no. 4 (2019): 292-301.

• Xu, Jinbo. Distance-based protein folding powered by deep learning. Proceedings of the National Academy of Sciences 116, no. 34 (2019): 16856-16865.

• Adhikari, Badri, and Jianlin Cheng. CONFOLD2: improved contact-driven ab initio protein structure modeling. BMC bioinformatics 19 (2018): 1-5.

• Adhikari, Badri, Debswapna Bhattacharya, Renzhi Cao, and Jianlin Cheng. CONFOLD: residue‐residue contact‐guided ab initio protein folding Proteins: Structure, Function, and Bioinformatics 83, no. 8 (2015): 1436-1449. CONFOLD

• Brunger, Axel T. Version 1.2 of the Crystallography and NMR system. Nature protocols 2, no. 11 (2007): 2728-2733. CNS (a useful tool to determine structure from distance map.)

Antibody/Nanobody/Loop/Mini-Protein Folding

• Burbach, Sarah M., and Bryan Briney. Improving antibody language models with native pairing. arXiv preprint arXiv:2308.14300 (2023).

• Fernández-Quintero, Monica L., Janik Kokot, Franz Waibl, Anna-Lena M. Fischer, Patrick K. Quoika, Charlotte M. Deane, and Klaus R. Liedl. Challenges in antibody structure prediction. In MAbs, vol. 15, no. 1, p. 2175319. Taylor & Francis, 2023.

• Ruffolo, Jeffrey A., and Jeffrey J. Gray. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nature communications 14, no. 1 (2023): 2389. IgFold

• Yining Wang, Xumeng Gong, Shaochuan Li, Bing Yang, Yiwu Sun, Yujie Luo, Hui Li, Le Song Fast de novo antibody structure prediction with atomic accuracy. The American Association for Cancer Research. 4296-4296. 2023. xTrimoABFold++

• Peng, Chao, Zelong Wang, Peize Zhao, Weifeng Ge, and Charles Huang. AbFold--an AlphaFold Based Transfer Learning Model for Accurate Antibody Structure Prediction. bioRxiv (2023): 2023-04. AbFold

• Kończak, Jarosław, Bartosz Janusz, Jakub Młokosiewicz, Tadeusz Satława, Sonia Wróbel, Paweł Dudzic, and Konrad Krawczyk. Structural pre-training improves physical accuracy of antibody structure prediction using deep learning. ImmunoInformatics (2023): 100028.

• Wang, Junlin, Wenbo Wang, and Yi Shang. Protein Loop Modeling Using AlphaFold2. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023). IAFLoop

• Wu, Jiaxiang, Fandi Wu, Biaobin Jiang, Wei Liu, and Peilin Zhao. tFold-ab: fast and accurate antibody structure prediction without sequence homologs. bioRxiv (2022): 2022-11. tFold-ab

• Tripathi, Asmita, Rajkrishna Mondal, Tapobrata Lahiri, Deepak Chaurasiya, and Manoj Kumar Pal. TemPred: A novel protein template search engine to improve protein structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics 01 (2023): 1-12. TemPred

• Giovanoudi, Eleni, and Dimitrios Rafailidis. Multi-Task Learning with Loop Specific Attention for CDR Structure Prediction. arXiv preprint arXiv:2306.13045 (2023).

• Wang, Tianyue, Xujun Zhang, Odin Zhang, Peichen Pan, Guangyong Chen, Yu Kang, Chang-Yu Hsieh, and Tingjun Hou. Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop. arXiv preprint arXiv:2306.12754 (2023). KarmaLoop

• Ruffolo, Jeffrey A., Jeremias Sulam, and Jeffrey J. Gray. Antibody structure prediction using interpretable deep learning. Patterns 3, no. 2 (2022).

• Abanades, Brennan, Guy Georges, Alexander Bujotzek, and Charlotte M. Deane. ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics 38, no. 7 (2022): 1877-1880. ABlooper

• Abanades, Brennan, Wing Ki Wong, Fergus Boyles, Guy Georges, Alexander Bujotzek, and Charlotte Mary Deane. ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins. bioRxiv (2022). ImmuneBuilder

• Cohen, Tomer, Matan Halfon, and Dina Schneidman-Duhovny. NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning. Frontiers in immunology 13 (2022): 958584. NanoNet

• Wang, Yining, Xumeng Gong, Shaochuan Li, Bing Yang, YiWu Sun, Chuan Shi, Hui Li, Yangang Wang, Cheng Yang, and Le Song. xtrimoABFold: Improving antibody structure prediction without multiple sequence alignments. arXiv preprint arXiv:2212.00735 (2022). xTrimoABFold

• Lee, Jae Hyeon, Payman Yadollahpour, Andrew Watkins, Nathan C. Frey, Andrew Leaver-Fay, Stephen Ra, Kyunghyun Cho, Vladimir Gligorijevic, Aviv Regev, and Richard Bonneau. EquiFold: protein structure prediction with a novel coarse-grained structure representation. bioRxiv (2022): 2022-10. EquiFold

• Ruffolo, Jeffrey A., Jeremias Sulam, and Jeffrey J. Gray. Antibody structure prediction using interpretable deep learning. Patterns 3, no. 2 (2022): 100406.

Protein Conformation/Distribution

• Jing, Bowen, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, and Tommi Jaakkola. EigenFold: Generative Protein Structure Prediction with Diffusion Models. arXiv preprint arXiv:2304.02198 (2023).

• Wu, Jiaxiang, Tao Shen, Haidong Lan, Yatao Bian, and Junzhou Huang. SE (3)-equivariant energy-based models for end-to-end protein folding. bioRxiv (2021): 2021-06. SE(3)-Fold

Engineering Tech. on Folding

• Lee, Jaemyung, Jaehoon Kim, Hasun Yu, and Youhan Lee. Solvent: A Framework for Protein Folding. arXiv preprint arXiv:2307.04603 (2023).

• Mirdita, Milot, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger. ColabFold: making protein folding accessible to all. Nature Methods (2022): 1-4. ColabFold

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

• Cheng, Shenggan, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng, and Yang You. FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours. arXiv preprint arXiv:2203.00854 (2022). FastFold

• Ahdritz, Gustaf, Nazim Bouatta, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell, Daniel Berenberg et al. OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv (2022): 2022-11.

• Wang, Guoxia, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, Dianhai Yu, Fan Wang, and Yanjun Ma. Helixfold: An efficient implementation of alphafold2 using paddlepaddle. arXiv preprint arXiv:2207.05477 (2022).

Tools

• Gutierrez, Santiago, Wojciech G. Tyczynski, Wouter Boomsma, Felix Teufel, and Ole Winther. MembraneFold: Visualising transmembrane protein structure and topology. bioRxiv (2022): 2022-12.

Related Works

• Amini, Ava P., and Kevin K. Yang. From noise to protein with image models. Nature Computational Science (2023): 1-2.

• Kandathil, Shaun M., Andy M. Lau, and David T. Jones. Machine learning methods for predicting protein structure from single sequences. Current Opinion in Structural Biology 81 (2023): 102627.