AlphaFold

s, fold to form a protein Proteins consist of chains of amino acids which spontaneously fold to form the three dimensional (3-D) structures of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. Protein structures can be determined experimentally through techniques such as X-ray crystallography, cryo-electron microscopy and nuclear magnetic resonance (NMR), which are all expensive and time-consuming. Such efforts, using the experimental methods, have identified the structures of about 170,000 proteins over the last 60 years, while there are over 200 million known proteins across all life forms. Over the years, researchers have applied numerous computational methods to predict the 3D structures of proteins from their amino acid sequences, accuracy of such methods in best possible scenario is close to experimental techniques (NMR) by the use of homology modeling based on molecular evolution. CASP, which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that GDT scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016. AlphaFold started competing in the 2018 CASP using an artificial intelligence (AI) deep learning technique. == Algorithm ==

DeepMind is known to have trained the program on over 170,000 proteins from the Protein Data Bank, a public repository of protein sequences and structures. The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. AlphaFold 2 (2020) force field. This step only slightly adjusts the predicted structure. A key part of the 2020 system are two modules, believed to be based on a transformer design, which are used to progressively refine a vector of information for each relationship (or "edge" in graph-theory terminology) between an amino acid residue of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input sequence alignment (these relationships are represented by the array shown in red). and is itself then iterated. In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations – i.e. unphysical bond angles or lengths. With subsequent iterations the number of stereochemical violations fell. By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero. The training data was originally restricted to single peptide chains. However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data. DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions. AlphaFold 3 (2024) Announced on 8 May 2024, AlphaFold 3 was co-developed by Google DeepMind and Isomorphic Labs, both subsidiaries of Alphabet. AlphaFold 3 is not limited to single-chain proteins, as it can also predict the structures of protein complexes with DNA, RNA, post-translational modifications and selected ligands and ions. The Pairformer module's initial predictions are refined by a diffusion model. This model begins with a cloud of atoms and iteratively refines their positions, guided by the Pairformer's output, to generate a 3D representation of the molecular structure. As of November 2025, the AlphaFold 3 research paper has been directly cited more than 9,000 times. ==Competitions==

of their experimental positions that they start to affect the CASP GDS-TS measure). The orange trend-line shows that by 2020 online prediction servers had been able to learn from and match this performance, while the best other groups (green curve) had on average been able to make some improvements on it. However, the black trend curve shows the degree to which AlphaFold 2 had surpassed this again in 2020, across the board. The detailed spread of data points indicates the degree of consistency or variation achieved by AlphaFold. Outliers represent the handful of sequences for which it did not make such a successful prediction. CASP13 In December 2018, DeepMind's AlphaFold placed first in the overall rankings of the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP). achieving a median score of 58.9 on the CASP's global distance test (GDT) score, ahead of 52.5 and 52.4 by the two next best-placed teams, who were also using deep learning to estimate contact distances. Overall, across all targets, AlphaFold 1 achieved a GDT score of 68.5. In January 2020, implementations and illustrative code of AlphaFold 1 was released open-source on GitHub. Overall, AlphaFold 2 made the best prediction for 88 out of the 97 targets. a level of accuracy reported to be comparable to experimental techniques like X-ray crystallography. with a median RMS deviation in its predictions of 2.1 Å for a set of overlapped CA atoms. == Reception ==

AlphaFold 2 scoring more than 90 in CASP's global distance test (GDT) is a great achievement in computational biology. News pieces appeared in the science press, such as Nature, and the story was covered by national newspapers. A frequent theme was the ability to predict protein structures based on the constituent amino acid sequence, expected to have benefits in the life sciences—accelerating drug discovery and enabling better understanding of diseases. Some have noted that even a perfect answer to the protein prediction problem still leaves questions about the protein folding problem (and thus protein dynamics)—understanding in detail how the folding process actually occurs in nature (and how sometimes they can also misfold). In 2023, Demis Hassabis and John Jumper won the Breakthrough Prize in Life Sciences as well as the Albert Lasker Award for Basic Medical Research for their management of the AlphaFold project. Hassabis and Jumper proceeded to win the Nobel Prize in Chemistry in 2024 for their work on "protein structure prediction" with David Baker of the University of Washington. ==Source code==

Open access to source code of several AlphaFold versions (excluding AlphaFold 3) has been provided by DeepMind in 2022 after requests from the scientific community. The source code and weights of AlphaFold 3 were made available for non-commercial use to the scientific community upon request in November 2024. It became publicly available in February 2025, still retaining the non-commercial restriction. Clones and derivatives A number of AlphaFold clones have also been published, mostly with permissive license terms. Clones for AlphaFold3 include ByteDance's Protenix (Apache 2.0 License), AlQuraishi Laboratory's OpenFold-3 (MIT license), and Boltz-1/2 (MIT license). There are also clones for older versions, though they became less relevant with the open-source release of AlphaFold 1 and 2 source codes. Still relevant are models, both open- and closed-source, that include modifications to the AlphaFold architecture. For AlphaFold 2, a notable example is ESMFold from Meta, which replaces the multiple sequence alignment with the latent space of a protein language model. Open-source tools that complement AlphaFold have also been made. One well-cited example is ColabFold, which uses MMseqs2 instead of HHblits to speed up the sequence search, allowing the AlphaFold pipelines to run quickly on Google Colab. == Database of protein models generated by AlphaFold ==

The AlphaFold Protein Structure Database (AFDB), a joint project between AlphaFold and EMBL-EBI, was launched on July 22, 2021. At launch, the database contained AlphaFold 1-predicted models for nearly the complete UniProt proteome of humans and 20 model organisms, totaling over 365,000 proteins. The database does not include proteins with fewer than 16 or more than 2700 amino acid residues, but for humans they are available in the whole batch file. AlphaFold's initial goal (as of early 2022) was to expand the database to cover most of the UniRef90 set, which contains over 100 million proteins. As of May 15, 2022, the database contained 992,316 predictions. In July 2021, UniProt-KB and InterPro has been updated to show AlphaFold predictions when available. On July 28, 2022, the team uploaded to the database the structures of around 200 million proteins from 1 million species, covering nearly every known protein on the planet. The number as of 2024 is 214 million, with 26 million being duplicates (exact sequence matches) of another protein in the database. The predicted structures can differ significantly between duplicates. As of 2025, the AFDB uses AlphaFold 2 for its predictions. All structures produced remain monomeric, but multimeric structures produced by other databases are linked on the page through the 3D-Beacons API. Foldseek, which provides fast and accurate structure searches, is also integrated. Information from AlphaMissense (a tool that uses AlphaFold to predict the outcome of missense mutations) is also integrated. Derived databases AlphaFill adds cofactors to AlphaFold models where appropriate. This is achieved by searching the Protein Data Bank for similar structures and transplanting cofactors to analogous positions. It is also linked to by UniProt. TmAlphaFold docks AlphaFold models to biological membranes, similar to what OPM does for PDB structures. AFTM uses AlphaFold models to identify transmembrane regions in human proteins, similar to what PDBTM does for PDB structures. The AFDB is not updated with UniProt sequences chanegs. AlphaSync keeps the AFDB in sync with UniProt entry changes, generating updated structures, residue-level features and contacts. It tries to use an AFDB entry for the exact updated sequence when available and run AlphaFold 2 independently otherwise. It fills in AFDB's blank for large (> 2,700 aa) proteins and proteins with special FASTA characters such as B, Z, U or X. The Encyclopedia of Domains (TED) applies the domain-recognition method from CATH database to 188 million unique structures from the AFDB, identifying nearly 365 million domains, which is 100 million more than what sequence-based methods could identify. == Performance, validations and limitations ==

AlphaFold has shown certain limitations. AlphaFold 1, 2, and AlphaFold DB • AlphaFold DB provides models of individual protein chains (monomers), rather than their biologically relevant complexes. • Many protein regions are predicted with low confidence score, including the intrinsically disordered protein regions. • Alphafold-2 was validated for predicting effects of point mutations on structure and free energy, with a partial success. AlphaFold 3 • Across several benchmarks, AlphaFold3 has demonstrated, on average, superior performance to conventional search-based docking algorithms in predicting small-molecule–protein binding modes. • AlphaFold 3 version can predict structures of protein complexes with a very limited set of selected cofactors and co- and post-translational modifications. Between 50% and 70% of the structures of the human proteome are incomplete without covalently-attached glycans. Other work has found that AlphaFold is insensitive to adversarial decoys generated by altering the physicochemical properties of binding pockets, suggesting potential reliance on training-set memorization rather than genuine chemical awareness. General • In the algorithm, the residues are moved freely, without any restraints. Therefore, during modeling the integrity of the chain is not maintained. As a result, AlphaFold may produce topologically wrong results, like structures with an arbitrary number of knots. (The study uses AlphaFold 2.3.2.) • The model relies, to some extent, on co-evolutionary information from similar proteins. Therefore, it may not perform as well on synthetic proteins or proteins with very low homology to those in the training database. Benchmarks support this limitation: when applied to naturally evolved de novo proteins, AlphaFold2 often yields low-confidence and predictor-dependent models, and protein language model–based (alignment-free) structure predictors can perform better for orphan proteins than AlphaFold2. More broadly, comparative analyses show that structure/disorder predictors (including AlphaFold2 and ESMFold) behave differently on de novo and random-sequence proteins than on conserved proteins, and that confidence metrics can show different relationships with predicted disorder in these sequence classes. • The model's ability to predict multiple native conformations of proteins is limited. • Proteins are inherently dynamic, and accessing multiple native conformations is often crucial for understanding their function. However, the model has limited capability to represent these alternative conformational states, particularly those that coexist or interconvert in biological environments. == Applications ==

AlphaFold has been used to predict structures of proteins of SARS-CoV-2, the causative agent of COVID-19. The structures of these proteins were pending experimental detection in early 2020. The team acknowledged that although these protein structures might not be the subject of ongoing therapeutical research efforts, they will add to the community's understanding of the SARS-CoV-2 virus. == Published works ==

• Andrew W. Senior et al. (December 2019), "Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)", Proteins: Structure, Function, Bioinformatics 87(12) 1141–1148 • Andrew W. Senior et al. (15 January 2020), "Improved protein structure prediction using potentials from deep learning", Nature 577 706–710 • John Jumper et al. (December 2020), "High Accuracy Protein Structure Prediction Using Deep Learning", in Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book), pp. 22–24 • John Jumper et al. (December 2020), "AlphaFold 2". Presentation given at CASP 14. • Abramson, J., Adler, J., Dunger, J. et al. (May 2024), "Accurate structure prediction of biomolecular interactions with AlphaFold 3", Nature 630, 493–500 (2024) ==See also==