Preeti
Summarise this content to 300 words
Cao, L. et al. De novo design of picomolar SARS-CoV-2 miniprotein inhibitors. Science 370, 426–431 (2020).
Bryan, C. M. et al. Computational design of a synthetic PD-1 agonist. Proc. Natl Acad. Sci. USA 118, 2102164118 (2021).
Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).
Dou, J. et al. De novo design of a fluorescence-activating beta-barrel. Nature 561, 485–491 (2018).
Vorobieva, A. A. et al. De novo design of transmembrane beta barrels. Science 371, 8182 (2021).
Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature https://doi.org/10.1038/s41586-023-06415-8 (2023).
Yim, J. et al. SE(3) diffusion model with application to protein backbone generation. In Proc. of the 40th International Conference on Machine Learning (eds Krause, A. et al.) 40001–40039 (PMLR, 2023).
Ingraham, J. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).
Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
Hsu, C. et al. Learning inverse folding from millions of predicted structures. In Proc. of the 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 8946–8970 (PMLR, 2022).
Anand, N. et al. Protein sequence design with a learned potential. Nat. Commun. 13, 746 (2022).
Liu, Y. et al. Rotamer-free protein sequence design based on deep learning and self-consistency. Nat. Comput. Sci. 2, 451–462 (2022).
Huang, B. et al. Accurate and efficient protein sequence design through learning concise local environment of residues. Bioinformatics 39, 122 (2023).
Ingraham, J. et al. Generative models for graph-based protein design. In Proc. of Advances in Neural Information Processing Systems (eds Wallach, H. et al) 15820–15831 (NeurlPS, 2019).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Carreira, J. et al. Human pose estimation with iterative error feedback. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (eds Bajcsy, R. et al.) 4733–4742 (IEEE, 2016).
Tu, Z. & Bai, X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744–1757 (2010).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Robin, X. et al. Continuous Automated Model EvaluatiOn (CAMEO)—perspectives on the future of fully automated evaluation of structure prediction methods. Proteins 89, 1977–1986 (2021).
CASP15. Critical Assessment of Techniques for Protien Structure Prediction, 15th Round. Abstract Book (Protein Structure Prediction Center, 2022); https://predictioncenter.org/casp15/doc/CASP15_Abstracts.pdf
Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, 1988).
Wainwright, M. J. & Jordan, M. I. Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn. 1, 1–305 (2008).
Zhang, H. et al. Predicting protein inter-residue contacts using composite likelihood maximization and deep learning. BMC Bioinform. 20, 537 (2019).
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M. & Aurell, E. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys. Rev. E 87, 012707 (2013).
Morcos, F. et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl Acad. Sci. USA 108, 1293–1301 (2011).
Alford, R. F. et al. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13, 3031–3048 (2017).
Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA 89, 10915–10919 (1992).
Wang, W., Peng, Z. & Yang, J. Single-sequence protein structure prediction using supervised transformer protein language models. Nat. Comput. Sci. 2, 804–814 (2022).
Chowdhury, R. et al. Single-sequence protein structure prediction using a language model and deep learning. Nat. Biotechnol. 40, 1617–1623 (2022).
Sakuma, K., Koike, R. & Ota, M. Dual-wield NTPases: a novel protein family mined from AlphaFold DB. Protein Science. 33, e4934 (2024).
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, 439–444 (2022).
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).
Shin, J.-E. et al. Protein design and variant prediction using autoregressive generative models. Nat. Commun. 12, 2403 (2021).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Frazer, J. et al. Disease variant prediction with deep generative models of evolutionary data. Nature 599, 91–95 (2021).
Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. In Proc. of Advances in Neural Information Processing Systems (eds Ranzato, M. et al.) 29287–29303 (NeurlPS, 2021).
Notin, P. et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In Proc. of the 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 16990–17017 (PMLR, 2022).
Rao, R. M. et al. MSA transformer. In Proc. of the 38th International Conference on Machine Learning (eds Meila, M and Zhang, T.) 8844–8856 (PMLR, 2021).
Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018).
Kotler, E. et al. A systematic p53 mutation library links differential functional impact to cancer mutation pattern and evolutionary conservation. Mol. Cell 71, 178–1908 (2018).
Mighell, T. L., Evans-Dutson, S. & O’Roak, B. J. A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotype relationships. Am. J. Hum. Genet. 102, 943–955 (2018).
Jia, X. et al. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am. J. Hum. Genet. 108, 163–175 (2021).
Pan, X. et al. Structure of the human voltage-gated sodium channel Nav1.4 in complex with beta1. Science 362, 2486 (2018).
Hennig, M., Darimont, B., Sterner, R., Kirschner, K. & Jansonius, J. N. 2.0 Å structure of indole-3-glycerol phosphate synthase from the hyperthermophile Sulfolobus solfataricus: possible determinants of protein stability. Structure 3, 1295–1306 (1995).
Banerjee, S. et al. Protonation state of an important histidine from high resolution structures of lytic polysaccharide monooxygenases. Biomolecules https://doi.org/10.3390/biom12020194 (2022).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Leman, J. K. et al. Macromolecular modeling and design in rosetta: recent methods and frameworks. Nat. Methods 17, 665–680 (2020).
Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01618-2 (2023).
Hie, B. L. et al. Efficient evolution of human antibodies from general protein language models. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01763-2 (2023).
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, 570–578 (2020).
Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, 170–176 (2017).
Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9, 173–175 (2012).
Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinform. 11, 431 (2010).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. of the International Conference on Learning Representations (eds Bengio, Y. et al.) 210–219, (ICLR 2015).
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Proc. of Advances in Neural Information Processing Systems (eds Wallach, H. et al.) 8024–8035 (NeurlPS, 2019).
Ren, M., Yu, C., Bu, D. & Zhang, H. Accurate and robust protein sequence design with Carbondesign. Code Ocean https://doi.org/10.24433/CO.5915382.v2 (2024).
Source link
Source link: https://www.nature.com/articles/s42256-024-00838-2
GIPHY App Key not set. Please check settings