Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Baek, M. et al. Efficient and accurate prediction of protein structure using RoseTTAFold2. Preprint at bioRxiv https://doi.org/10.1101/2023.05.24.542179 (2023).Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017).
Google Scholar
Humphreys, I. R. et al. Computed structures of core eukaryotic protein complexes. Science 374, eabm4805 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun. 13, 1265 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).Article
CAS
PubMed
PubMed Central
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).Peng, Z., Wang, W., Han, R., Zhang, F. & Yang, J. Protein structure prediction in the deep learning era. Curr. Opin. Struct. Biol. 77, 102495 (2022).Article
CAS
PubMed
Google Scholar
Cheng, S. et al. FastFold: Optimizing AlphaFold training and inference on GPU clusters. In Proc. 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming 417–430 (ACM, 2024).Fang, X. et al. A method for multiple-sequence-alignment-free protein structure prediction using a protein language model. Nat. Mach. Intell. 5, 1087–1096 (2023).Article
Google Scholar
Ahdritz, G. et al. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat. Methods 21, 1514–1524 (2022).Article
Google Scholar
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).Liu, S. et al. PSP: million-level protein sequence dataset for protein structure prediction. Preprint at https://arxiv.org/abs/2206.12240 (2022).Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).Article
CAS
PubMed
Google Scholar
Lee, J.-W. et al. DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function. Bioinformatics 39, btad712 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).Article
CAS
PubMed
Google Scholar
Mirdita, M., Steinegger, M. & Söding, J. MMseqs2 desktop and local web server app for fast, interactive sequence searches. Bioinformatics 35, 2856–2858 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Lee, S. et al. Petabase-scale homology search for structure prediction. Cold Spring Harb. Perspect. Biol. 16, a041465 (2024).Article
PubMed
Google Scholar
Abakarova, M., Marquet, C., Rera, M., Rost, B. & Laine, E. Alignment-based protein mutational landscape prediction: doing more with less. Genome Biol. Evol. 15, evad201 (2023).Article
PubMed
PubMed Central
Google Scholar
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).Article
CAS
PubMed
Google Scholar
wwPDB consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2019).Article
Google Scholar
Liu, J. et al. Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15. Commun. Biol. 6, 1140 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Peng, Z., Wang, W., Wei, H., Li, X. & Yang, J. Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15. Proteins 91, 1704–1711 (2023).Article
CAS
PubMed
Google Scholar
Rego, N. & Koes, D. 3Dmol.js: molecular visualization with WebGL. Bioinformatics 31, 1322–1324 (2015).Article
PubMed
Google Scholar
Nomura, K. et al. Bacterial pathogens deliver water- and solute-permeable channels to plant cells. Nature 621, 586–591 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Mosalaganti, S. et al. AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science 376, eabm9506 (2022).Article
CAS
PubMed
Google Scholar
Zhang, H. et al. Structure of human glycosylphosphatidylinositol transamidase. Nat. Struct. Mol. Biol. 29, 203–209 (2022).Article
CAS
PubMed
Google Scholar
Del Alamo, D., Sala, D., Mchaourab, H. S. & Meiler, J. Sampling alternative conformational states of transporters and receptors with AlphaFold2. eLife 11, e75751 (2022).Article
PubMed
PubMed Central
Google Scholar
Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Proc. Mach. Learn. Res. 48, 1050–1059 (2016).
Google Scholar
Wallner, B. AFsample: improving multimer prediction with AlphaFold using massive sampling. Bioinformatics 39, btad573 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Wayment-Steele, H. K. et al. Predicting multiple conformations via sequence clustering and AlphaFold2. Nature 625, 832–839 (2024).Article
CAS
PubMed
Google Scholar
Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P. & Rubenstein, B. M. High-throughput prediction of protein conformational distributions with subsampled AlphaFold2. Nat. Commun. 15, 2464 (2024).Article
CAS
PubMed
PubMed Central
Google Scholar
Chakravarty, D. & Porter, L. L. AlphaFold2 fails to predict protein fold switching. Protein Sci. 31, e4353 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Saldaño, T. et al. Impact of protein conformational diversity on AlphaFold predictions. Bioinformatics 38, 2742–2748 (2022).Article
PubMed
Google Scholar
Garibsingh, R.-A. A. et al. Rational design of ASCT2 inhibitors using an integrated experimental-computational approach. Proc. Natl Acad. Sci. USA 118, e2104093118 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Garaeva, A. A., Guskov, A., Slotboom, D. J. & Paulino, C. A one-gate elevator mechanism for the human neutral amino acid transporter ASCT2. Nat. Commun. 10, 3427 (2019).Article
PubMed
PubMed Central
Google Scholar
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).Chowdhury, R. et al. Single-sequence protein structure prediction using a language model and deep learning. Nat. Biotechnol. 40, 1617–1623 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Wang, W., Peng, Z. & Yang, J. Single-sequence protein structure prediction using supervised transformer protein language models. Nat. Comput. Sci. 2, 804–814 (2022).Article
CAS
PubMed
Google Scholar
Bertoline, L. M. F., Lima, A. N., Krieger, J. E. & Teixeira, S. K. Before and after AlphaFold2: an overview of protein structure prediction. Front. Bioinform. 3, 1120370 (2023).Article
PubMed
PubMed Central
Google Scholar
Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res. 51, D418–D427 (2023).Article
CAS
PubMed
Google Scholar
Redl, I. et al. ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers. NAR Genom. Bioinform. 5, lqad041 (2023).Article
PubMed
PubMed Central
Google Scholar
Zhang, J., Schaeffer, R. D., Durham, J., Cong, Q. & Grishin, N. V. DPAM: a domain parser for AlphaFold models. Protein Sci. 32, e4548 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Howe, P. W. Principal components analysis of protein structure ensembles calculated using NMR data. J. Biomol. NMR 20, 61–70 (2001).Article
CAS
PubMed
Google Scholar
Roe, D. R. & Cheatham, T. E. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).Article
CAS
PubMed
Google Scholar
Zhang, H. et al. Structure of a human glycosylphosphatidylinositol (GPI) transamidase. Available at https://www.rcsb.org/structure/7W72 (2022).Garibsingh, R.-A. A. et al. ASCT2 in the presence of the inhibitor Lc-BPE (position “up”) in the outward-open conformation. Available at https://www.rcsb.org/structure/7BCQ (2021).Garaeva, A. A., Guskov, A., Slotboom, D. J. & Paulino, C. Inward-open structure of the ASCT2 (SLC1A5) mutant C467R in presence of TBOA. Available at https://www.rcsb.org/structure/6RVX (2019).Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).Article
CAS
PubMed
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).Article
CAS
PubMed
PubMed Central
Google Scholar
Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).Article
CAS
PubMed
PubMed Central
Google Scholar
O’Reilly, F. J. et al. Protein complexes in cells by AI-assisted structural proteomics. Mol. Syst. Biol. 19, e11544 (2023).Article
PubMed
PubMed Central
Google Scholar
Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinforma. 20, 473 (2019).Article
Google Scholar
Gabler, F. et al. Protein sequence analysis using the MPI bioinformatics toolkit. Curr. Protoc. Bioinforma. 72, e108 (2020).Article
CAS
Google Scholar