Anfinsen, C. B. Principles that govern the folding of protein chains. Science 181, 223–230 (1973).
Google Scholar
Dill, K. A., Ozkan, S. B., Shell, M. S. & Weikl, T. R. The protein folding problem. Annu. Rev. Biophys. 37, 289–316 (2008).
Google Scholar
Jones, D. T., Singh, T., Kosciolek, T. & Tetchner, S. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics 31, 999–1006 (2015).
Google Scholar
Golkov, V. et al. Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images. In Advances in Neural Information Processing Systems (eds Lee, D. et al.) (Curran Associates, 2016).
Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput. Biol. 13, e1005324 (2017).
Google Scholar
Liu, Y., Palmedo, P., Ye, Q., Berger, B. & Peng, J. Enhancing evolutionary couplings with deep convolutional neural networks. Cell Syst. 6, 65–74 (2018).
Google Scholar
Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
Google Scholar
Xu, J., McPartlon, M. & Li, J. Improved protein structure prediction by deep learning irrespective of co-evolution information. Nat. Mach. Intell. 3, 601–609 (2021).
Google Scholar
Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
Google Scholar
Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc. 5, 725–738 (2010).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 577, 583–589 (2021).
Google Scholar
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
Google Scholar
Baek, M. Adding a big enough number for ‘residue_index’ feature is enough to model hetero-complex using AlphaFold (green&cyan: crystal structure / magenta: predicted model w/ residue_index modification). Twitter twitter.com/minkbaek/status/1417538291709071362?lang=en (2021).
Tsaban, T. et al. Harnessing protein folding neural networks for peptide–protein docking. Nat. Commun. 13, 176 (2022).
Google Scholar
Roney, J. P. & Ovchinnikov, S. State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett. 129, 238101 (2022).
Google Scholar
Baltzis, A. et al. Highly significant improvement of protein sequence alignments with AlphaFold2. Bioinformatics 38, 5007–5011 (2022).
Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein–protein interactions using AlphaFold2. Nat. Commun. 13, 1265 (2022).
Google Scholar
Wayment-Steele, H. K., Ovchinnikov, S., Colwell, L. & Kern, D. Prediction of multiple conformational states by combining sequence clustering with AlphaFold2. Nature 625, 832–839 (2024).
Google Scholar
Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).
Google Scholar
Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2021).
Google Scholar
Callaway, E. ‘The entire protein universe’: AI predicts shape of nearly every known protein. Nature 608, 15–16 (2022).
Google Scholar
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2021).
Ahdritz, G. et al. OpenProteinSet: training data for structural biology at scale. In Advances in Neural Information Processing Systems (eds Oh, A. et al.) 4597-4609 (Curran Associates, 2023).
Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems (eds Wallach, H. et al.) 8026–8037 (Curran Associates, 2019).
Bradbury, J. et al. JAX: composable transformations of Python+NumPy programs. GitHub github.com/google/jax (2018).
Rasley, J., Rajbhandari, S., Ruwase, O. & He, Y. DeepSpeed: system optimizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20 3505–3506 (Association for Computing Machinery, 2020).
Charlier, B., Feydy, J., Glaunès, J., Collin, F.-D. & Durif, G. Kernel operations on the GPU, with autodiff, without memory overflows. J. Mach. Learn. Res. 22, 1–6 (2021).
Falcon, W. & the PyTorch Lightning team. PyTorch Lightning (PyTorch Lightning, 2019).
Dao, T., Fu, D. Y., Ermon, S., Rudra, A. & Ré, C. FlashAttention: fast and memory-efficient exact attention with IO-awareness. In Advances in Neural Information Processing Systems (eds Koyejo, S. et al.) 16344–16359 (Curran Associates, 2022).
Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).
Google Scholar
wwPDB Consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2018).
Google Scholar
Haas, J. ürgen et al. Continuous automated model evaluation (CAMEO) complementing the critical assessment of structure prediction in CASP12. Proteins 86, 387–398 (2018).
Google Scholar
Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013).
Google Scholar
Orengo, C. A. et al. CATH—a hierarchic classification of protein domain structures. Structure 5, 1093–1108 (1997).
Google Scholar
Sillitoe, I. et al. CATH: increased structural coverage of functional space. Nucleic Acids Res. 49, D266–D273 (2021).
Google Scholar
Andreeva, A., Kulesha, E., Gough, J. & Murzin, A. G. The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures. Nucleic Acids Res. 48, D376–D382 (2020).
Google Scholar
Saitoh, Y. et al. Structural basis for high selectivity of a rice silicon channel Lsi1. Nat. Commun. 12, 6236 (2021).
Google Scholar
Mota, DaniellyC. A. M. et al. Structural and thermodynamic analyses of human TMED1 (p241) Golgi dynamics. Biochimie 192, 72–82 (2022).
Google Scholar
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems (eds Guyon, I. et al.) (Curran Associates, 2017).
Rabe, M. N. & Staats, C. Self-attention does not need O(n2) memory. Preprint at https://doi.org/10.48550/arXiv.2112.05682 (2021).
Cheng, S. et al. FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters. In Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming 417–430 (Association for Computing Machinery, 2024).
Li, Z. et al. Uni-Fold: an open-source platform for developing protein folding models beyond AlphaFold. Preprint at bioRxiv https://doi.org/10.1101/2022.08.04.502811 (2022).
Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Science 22, 2577–2637 (1983).
Google Scholar
Zemla, A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 31, 3370–3374 (2003).
Google Scholar
Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).
Google Scholar
Sułkowska, J. I., Morcos, F., Weigt, M., Hwa, T. & Onuchic, José Genomics-aided structure prediction. Proc. Natl Acad. Sci. USA 109, 10340–10345 (2012).
Google Scholar
Kaplan, J. et al. Scaling laws for neural language models. Preprint at https://doi.org/10.48550/arXiv.2001.08361 (2020).
Hoffmann, J. et al. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems (eds Oh, A. H. et al.) 30016–30030 (NeurIPS, 2022).
Tay, Y. et al. Scaling laws vs model architectures: how does inductive bias influence scaling? In Findings of the Association for Computational Linguistics: EMNLP 2023 (eds Bouamor, H. et al.) 12342–12364 (Association for Computational Linguistics, 2023).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Google Scholar
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M. & Church, G. M. Unified rational protein engineering with sequence-based deep representation learning. Nat. Methods 16, 1315–1322 (2019).
Google Scholar
Chowdhury, R. et al. Single-sequence protein structure prediction using a language model and deep learning. Nat. Biotechnol. 40, 1617–1623 (2022).
Wu, R. et al. High-resolution de novo structure prediction from primary sequence. Preprint at bioRxiv https://doi.org/10.1101/2022.07.21.500999 (2022).
Singh, J., Paliwal, K., Litfin, T., Singh, J. & Zhou, Y. Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling. Bioinformatics 38, 3900–3910 (2022).
Google Scholar
Baek, M., McHugh, R., Anishchenko, I., Baker, D. & DiMaio, F. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).
Google Scholar
Pearce, R., Omenn, G. S. & Zhang, Y. De novo RNA tertiary structure prediction at atomic resolution using geometric potentials from deep learning. Preprint at bioRxiv https://doi.org/10.1101/2022.05.15.491755 (2022).
McPartlon, M., Lai, B. & Xu, J. A deep SE(3)-equivariant model for learning inverse protein folding. Preprint at bioRxiv https://doi.org/10.1101/2022.04.15.488492 (2022).
McPartlon, M. & Xu, J. An end-to-end deep learning method for protein side-chain packing and inverse folding. In Proceedings of the National Academy of Sciences e2216438120 (PNAS, 2023).
Knox, H. L., Sinner, E. K., Townsend, C. A., Boal, A. K. & Booker, S. J. Structure of a B12-dependent radical SAM enzyme in carbapenem biosynthesis. Nature 602, 343–348 (2022).
Google Scholar
Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
Google Scholar
Rajbhandari, S., Rasley, J., Ruwase, O. & He, Y. Zero: memory optimizations toward training trillion parameter models. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (IEEE Press, 2020).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In 3rd International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) (ICLR, 2015).
Wang, G. et al. HelixFold: an efficient implementation of AlphaFold2 using PaddlePaddle. Preprint at https://doi.org/10.48550/arXiv.2207.05477 (2022).
Yuan, J. et al. OneFlow: redesign the distributed deep learning framework from scratch. Preprint at https://doi.org/10.48550/arXiv.2110.15032 (2021).
Ovchinnikov, S. Weekend project! nerd-face So now that OpenFold weights are available. I was curious how different they are from AlphaFold weights and if they can be used for AfDesign evaluation. More specifically, if you design a protein with AlphaFold, can OpenFold predict it (and vice-versa)? (1/5). Twitter twitter.com/sokrypton/status/1551242121528520704?lang=en (2022).
Wei, X. et al. The α-helical cap domain of a novel esterase from gut Alistipes shahii shaping the substrate-binding pocket. J. Agric. Food Chem. 69, 6064–6072 (2021).
Google Scholar
Carroll, B. L. et al. Caught in motion: human NTHL1 undergoes interdomain rearrangement necessary for catalysis. Nucleic Acids Res. 49, 13165–13178 (2021).
Google Scholar
