Editing Protein structure prediction (section)

===AI methods===
[[AlphaFold]] was one of the first AIs to predict protein structures. It was introduced by Google's DeepMind in the 13th CASP competition, which was held in 2018.<ref name=":0"/> [[AlphaFold]] relies on a [[artificial neural network|
neural network]] approach, which directly predicts the 3D coordinates of all non-hydrogen atoms for a given protein using the amino acid sequence and aligned [[sequence homology|homologous sequences]]. The [[AlphaFold]] network consists of a trunk which processes the inputs through repeated layers, and a structure module which introduces an explicit 3D structure.<ref name=":0"/> Earlier neural networks for protein structure prediction used [[LSTM]].<ref name="hochreiter2007"/><ref name="thireou2007"/>

[[File:The performance of AlphaFold.png|thumb|alt=a, The performance of [[AlphaFold]] on the CASP14 dataset (n=87 protein domains) relative to the top-15 entries (out of 146 entries), group numbers correspond to the numbers assigned to entrants by CASP. Data are median and the 95% confidence interval of the median, estimated from 10,000 bootstrap samples. b, Our prediction of CASP14 target T1049 (PDB 6Y4F, blue) compared with the true (experimental) structure (green). Four residues in the C terminus of the crystal structure are B-factor outliers and are not depicted. c, CASP14 target T1056 (PDB 6YJ1). An example of a well-predicted zinc-binding site (AlphaFold has accurate side chains even though it does not explicitly predict the zinc ion). d, CASP target T1044 (PDB 6VR4)—a 2,180-residue single chain—was predicted with correct domain packing (the prediction was made after CASP using AlphaFold without intervention).]]

[[File:Model architecture.png|thumb|alt=Model architecture. Arrows show the information flow among the various components described in this paper. Array shapes are shown in parentheses with s, number of sequences (Nseq in the main text); r, number of residues (Nres in the main text); c, number of channels.]]

Since [[AlphaFold]] outputs protein coordinates directly, [[AlphaFold]] produces predictions in graphics processing unit (GPU) minutes to GPU hours, depending on the length of protein sequence.<ref name=":0"/> 

The [[European Bioinformatics Institute]] together with [[DeepMind]] have constructed the AlphaFold – EBI database<ref>{{cite web |author=<!--Not stated--> |title=AlphaFold Protein Structure Database |url=https://alphafold.ebi.ac.uk |access-date=November 30, 2022 |website=EMBL-EBI |publisher=}}</ref> for predicted protein structures.<ref name="pmid34791371">{{cite journal |display-authors=6 |vauthors=Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, Žídek A, Green T, Tunyasuvunakool K, Petersen S, Jumper J, Clancy E, Green R, Vora A, Lutfi M, Figurnov M, Cowie A, Hobbs N, Kohli P, Kleywegt G, Birney E, Hassabis D, Velankar S |date=January 2022 |title=AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |journal=Nucleic Acids Res |volume=50 |issue=D1 |pages=D439–D444 |doi=10.1093/nar/gkab1061 |pmc=8728224 |pmid=34791371}}</ref>