Highly accurate protein structure prediction with AlphaFold.

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   Highly accurate protein structure prediction for the human proteome

   Kathryn Tunyasuvunakool, J. Adler, Zachary Wu et al. · 2021 · Nature · cited 2,684 times · Not summarized yet

   Abstract

   Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introdu…

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   AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

   Mihály Váradi, Stephen Anyango, Mandar Deshpande et al. · 2021 · Nucleic Acids Research · cited 8,142 times · Not summarized yet

   Abstract

   The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) repre…

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   Accurate structure prediction of biomolecular interactions with AlphaFold 3

   Josh Abramson, Jonas Adler, Jack Dunger et al. · 2024 · Nature · cited 17,983 times · Not summarized yet

   Abstract

   Abstract The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2–6 . Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein–ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein–nucleic acid i…

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   AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

   Mihály Váradi, Damian Bertoni, Paulyna Magaña et al. · 2023 · Nucleic Acids Research · cited 2,245 times · Not summarized yet

   Abstract

   The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We det…

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   Computational structural insights into alternative proteins: The impact of AlphaFold and AI-based prediction.

   S. Natarajan, Bagavathi Lakshmi Ramarajan, Rani Sivakumar et al. · 2026 · Advances in Protein Chemistry and Structural Biology · Not summarized yet
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   High‐accuracy protein structure prediction in CASP14

   J. Pereira, A. Simpkin, M. Hartmann et al. · 2021 · Proteins: Structure, Function, and Bioinformatics · cited 288 times · Not summarized yet

   Abstract

   The application of state‐of‐the‐art deep‐learning approaches to the protein modeling problem has expanded the “high‐accuracy” category in CASP14 to encompass all targets. Building on the metrics used for high‐accuracy assessment in previous CASPs, we evaluated the performance of all groups that submitted models for at least 10 targets across all difficulty classes, and judged the usefulness of those produced by AlphaFold2 (AF2) as molecular replacement search models with AMPLE. Driven by the qualitative diversity of the targets submitted to CASP, we also introduce DipDiff as a new measure for the improvement in backbone geometry provided by a model versus available templates. Although a larg…

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   Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

   A. Senior, Richard Evans, J. Jumper et al. · 2019 · Proteins: Structure, Function, and Bioinformatics · cited 277 times · Not summarized yet

   Abstract

   We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free‐modeling (FM) methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 FM assessor…

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   Structural compatibility is not enough: Current challenges in protein-protein interaction prediction.

   A. Elofsson · 2026 · Molecules and Cells · Not summarized yet

   Abstract

   Since the introduction of AlphaFold2, AI-driven methods have greatly advanced protein structure and complex prediction. However, the performance varies strongly across interaction classes. The performance drops when dynamics are important and when interactions lack a strong co-evolutionary history. We propose to organize these limitations into distinct regions: interactions that are largely tractable by current methods; interactions in which structural compatibility is ambiguous due to competition, paralogy, or stoichiometry; interactions that lack sufficient co-evolutionary signal; and interactions whose biological relevance depends on cellular context, dynamics, or disorder. These limitati…

Prior work it builds on

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   Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII

   Andriy Kryshtafovych, T. Schwede, M. Topf et al. · 2019 · Proteins: Structure, Function, and Bioinformatics · cited 454 times · Not summarized yet

   Abstract

   CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically “ab initio” modeling). Progress was driven by the successful application of deep learning techniques to predict inter‐residue distances. In turn, these results drove dramatic improvements in three‐dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long‐standing problem of predicting the fold topology of mo…

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   Improved protein structure prediction using predicted interresidue orientations

   Jian-Yi Yang, I. Anishchenko, Hahnbeom Park et al. · 2019 · Proceedings of the National Academy of Sciences of the United States of America · cited 1,311 times · Not summarized yet

   Abstract

   Significance Protein structure prediction is a longstanding challenge in computational biology. Through extension of deep learning-based prediction to interresidue orientations in addition to distances, and the development of a constrained optimization by Rosetta, we show that more accurate models can be generated. Results on a set of 18 de novo-designed proteins suggests the proposed method should be directly applicable to current challenges in de novo protein design. The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network …

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   Protein structure prediction from sequence variation

   D. Marks, T. Hopf, C. Sander · 2012 · Nature Biotechnology · cited 650 times · Not summarized yet

   Abstract

   Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational…

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   Deep learning techniques have significantly impacted protein structure prediction and protein design

   Robin Pearce, Yang Zhang · 2021 · Current Opinion in Structural Biology · cited 116 times · Not summarized yet

   Abstract

   Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the last two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into d…

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   End-to-end differentiable learning of protein structure

   Mohammed AlQuraishi · 2018 · bioRxiv · cited 364 times · Not summarized yet

   Abstract

   Predicting protein structure from sequence is a central challenge of biochemistry. Co‐evolution methods show promise, but an explicit sequence‐to‐structure map remains elusive. Advances in deep learning that replace complex, human‐designed pipelines with differentiable models optimized end‐to‐end suggest the potential benefits of similarly reformulating structure prediction. Here we report the first end‐to‐end differentiable model of protein structure. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co‐evolutionary da…

Papers that cite it

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   Fast and accurate protein structure search with Foldseek

   Michel van Kempen, Stephanie S. Kim, Charlotte Tumescheit et al. · 2023 · Nature Biotechnology · cited 2,858 times · Not summarized yet

   Abstract

   As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing tertiary amino acid interactions within proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively.

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   ColabFold: making protein folding accessible to all

   Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki et al. · 2022 · Nature Methods · cited 9,567 times · Not summarized yet

   Abstract

   ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .

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   Predicting protein structures at any folding stage from sequence

   Li-Sheng Zhang, Qing-Song Li, Zi-Nan Xie et al. · 2026 · Not summarized yet

   Abstract

   Protein structure prediction has largely focused on native conformations, leaving the continuum of unfolded states, folding intermediates, and native structures difficult to access. We present FoldScape, a sequence-conditioned deep learning framework for generating protein conformational ensembles at specified folding fractions $Q$. Trained on approximately one million conformations from 10,000 protein sequences, FoldScape combines coarse-grained folding and all-atom mechanical-unfolding trajectories. It reconstructs three-dimensional $C_\alpha$ chains through hierarchical sliding-window regressions of inter-residue distances, chain-level assembly, and a rectified-flow decoder. By conditioni…

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   Evolutionary-scale prediction of atomic-level protein structure with a language model

   Zeming Lin, Halil Akin, Roshan Rao et al. · 2023 · Science · cited 6,428 times · Not summarized yet

   Abstract

   Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 …

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   From structure prediction to ligand–target recognition: expanding the reach of AlphaFold in natural product research

   Guang-Rui Xu, Hai-Feng Hu, Wei-Hui Zhao et al. · 2026 · Natural Products and Bioprospecting · Not summarized yet

   Abstract

   Natural products constitute a structurally diverse reservoir of bioactive molecules and remain an important source of lead compounds for drug discovery. However, the limited availability of structural information on pharmacological targets and biomolecular interactions continues to hinder mechanism elucidation and structure-guided optimization, particularly for complex natural products such as bioactive peptides, proteinaceous natural products, and natural molecular glues. Recent advances in AlphaFold have transformed structural biology by enabling high-confidence prediction of protein structures and biomolecular complexes, providing unprecedented opportunities for structure-guided natural p…