AlphaFold is an artificial intelligence (AI) program developed by DeepMind , a subsidiary of Alphabet , which performs predictions of protein structure . The program is designed as a deep learning system.
100-497: AlphaFold software has had three major versions. A team of researchers that used AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. The program was particularly successful at predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing template structures were available from proteins with
200-520: A x ( ( h 0 d W Q ) ( H W K ) T ) ( H W V ) {\displaystyle c_{0}=\mathrm {Attention} (h_{0}^{d}W^{Q},HW^{K},HW^{V})=\mathrm {softmax} ((h_{0}^{d}W^{Q})\;(HW^{K})^{T})(HW^{V})} where the matrix H {\displaystyle H} is the matrix whose rows are h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } . Note that
300-583: A query vector q 0 = h 0 d W Q {\displaystyle q_{0}=h_{0}^{d}W^{Q}} . Meanwhile, the hidden vectors outputted by the encoder are transformed by another linear map W K {\displaystyle W^{K}} into key vectors k 0 = h 0 W K , k 1 = h 1 W K , … {\displaystyle k_{0}=h_{0}W^{K},k_{1}=h_{1}W^{K},\dots } . The linear maps are useful for providing
400-437: 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
500-740: A 100-point scale of prediction accuracy for moderately difficult protein targets. AlphaFold was made open source in 2021, and in CASP15 in 2022, while DeepMind did not enter, virtually all of the high-ranking teams used AlphaFold or modifications of AlphaFold. Automated assessments for CASP15 (2022) Automated assessments for CASP14 (2020) Automated assessments for CASP13 (2018) Automated assessments for CASP12 (2016) Automated assessments for CASP11 (2014) Automated assessments for CASP10 (2012) Automated assessments for CASP9 (2010) Automated assessments for CASP8 (2008) Automated assessments for CASP7 (2006) Attention (machine learning) Attention
600-500: A decoder network converts those vectors to sentences in the target language. The Attention mechanism was grafted onto this structure in 2014, and later refined into the Transformer design. Consider the seq2seq language English-to-French translation task. To be concrete, let us consider the translation of "the zone of international control <end>", which should translate to "la zone de contrôle international <end>". Here, we use
700-995: A dot-product attention mechanism, to obtain h 0 ′ = A t t e n t i o n ( h 0 W Q , H W K , H W V ) h 1 ′ = A t t e n t i o n ( h 1 W Q , H W K , H W V ) ⋯ {\displaystyle {\begin{aligned}h_{0}'&=\mathrm {Attention} (h_{0}W^{Q},HW^{K},HW^{V})\\h_{1}'&=\mathrm {Attention} (h_{1}W^{Q},HW^{K},HW^{V})\\&\cdots \end{aligned}}} or more succinctly, H ′ = A t t e n t i o n ( H W Q , H W K , H W V ) {\displaystyle H'=\mathrm {Attention} (HW^{Q},HW^{K},HW^{V})} . This can be applied repeatedly, to obtain
800-410: A fixed-length vector. (Xu et al 2015), citing (Bahdanau et al 2014), applied the attention mechanism as used in the seq2seq model to image captioning. One problem with seq2seq models was their use of recurrent neural networks, which are not parallelizable as both the encoder and the decoder must process the sequence token-by-token. Decomposable attention attempted to solve this problem by processing
900-603: 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, the program achieved a GDT score of 68.5. In January 2020, implementations and illustrative code of AlphaFold 1 was released open-source on GitHub . but, as stated in the "Read Me" file on that website: "This code can't be used to predict structure of an arbitrary protein sequence. It can be used to predict structure only on
1000-506: A minimum 50% improvement in accuracy for protein interactions with other molecules compared to existing methods. Moreover, for certain key categories of interactions, the prediction accuracy has effectively doubled. Demis Hassabis and John Jumper from the team that developed AlphaFold won the Nobel Prize in Chemistry in 2024 for their work on “protein structure prediction”. The two had won
1100-614: A multilayered encoder. This is the "encoder self-attention", sometimes called the "all-to-all attention", as the vector at every position can attend to every other. For decoder self-attention, all-to-all attention is inappropriate, because during the autoregressive decoding process, the decoder cannot attend to future outputs that has yet to be decoded. This can be solved by forcing the attention weights w i j = 0 {\displaystyle w_{ij}=0} for all i < j {\displaystyle i<j} , called "causal masking". This attention mechanism
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#17328842884441200-500: A partially similar sequence. A team that used AlphaFold 2 (2020) repeated the placement in the CASP14 competition in November 2020. The team achieved a level of accuracy much higher than any other group. It scored above 90 for around two-thirds of the proteins in CASP's global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to
1300-565: A probability distribution over 0 , 1 , … {\displaystyle 0,1,\dots } . This can be accomplished by the softmax function , thus giving us the attention weights: ( w 00 , w 01 , … ) = s o f t m a x ( q 0 k 0 T , q 0 k 1 T , … ) {\displaystyle (w_{00},w_{01},\dots )=\mathrm {softmax} (q_{0}k_{0}^{T},q_{0}k_{1}^{T},\dots )} This
1400-404: A protein sequence of known structure (called a template), comparative protein modeling may be used to predict the tertiary structure . Templates can be found using sequence alignment methods (e.g. BLAST or HHsearch ) or protein threading methods, which are better in finding distantly related templates. Otherwise, de novo protein structure prediction must be applied (e.g. Rosetta), which
1500-440: A regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions. In order to ensure that no predictor can have prior information about a protein's structure that would put them at an advantage, it is important that the experiment be conducted in a double-blind fashion: Neither predictors nor
1600-477: A scene. These research developments inspired algorithms such as the Neocognitron and its variants. Meanwhile, developments in neural networks had inspired circuit models of biological visual attention. One well-cited network from 1998, for example, was inspired by the low-level primate visual system . It produced saliency maps of images using handcrafted (not learned) features, which were then used to guide
1700-455: A second neural network in processing patches of the image in order of reducing saliency. A key aspect of attention mechanism can be written (schematically) as: ∑ i ⟨ ( query ) i , ( key ) i ⟩ ( value ) i {\displaystyle \sum _{i}\langle ({\text{query}})_{i},({\text{key}})_{i}\rangle ({\text{value}})_{i}} where
1800-493: A single differentiable end-to-end model, based entirely on pattern recognition, which was trained in an integrated way as a single integrated structure. Local physics, in the form of energy refinement based on the AMBER model, is applied only as a final refinement step once the neural network prediction has converged, and only slightly adjusts the predicted structure. A key part of the 2020 system are two modules, believed to be based on
1900-514: Is permutation equivariant in the sense that: By noting that the transpose of a permutation matrix is also its inverse, it follows that: which shows that QKV attention is equivariant with respect to re-ordering the queries (rows of Q {\displaystyle \mathbf {Q} } ); and invariant to re-ordering of the key-value pairs in K , V {\displaystyle \mathbf {K} ,\mathbf {V} } . These properties are inherited when applying linear transforms to
2000-477: Is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that sequence. In natural language processing , importance is represented by "soft" weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size. Unlike "hard" weights, which are computed during
2100-455: Is aligned with the third word aime . Stacking soft row vectors together for je , t' , and aime yields an alignment matrix : Sometimes, alignment can be multiple-to-multiple. For example, the English phrase look it up corresponds to cherchez-le . Thus, "soft" attention weights work better than "hard" attention weights (setting one attention weight to 1, and the others to 0), as we would like
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#17328842884442200-970: Is computed with QKV attention as: head i = Attention ( Q W i Q , K W i K , V W i V ) {\displaystyle {\text{head}}_{i}={\text{Attention}}(\mathbf {Q} \mathbf {W} _{i}^{Q},\mathbf {K} \mathbf {W} _{i}^{K},\mathbf {V} \mathbf {W} _{i}^{V})} and W i Q , W i K , W i V {\displaystyle \mathbf {W} _{i}^{Q},\mathbf {W} _{i}^{K},\mathbf {W} _{i}^{V}} , and W O {\displaystyle \mathbf {W} ^{O}} are parameter matrices. The permutation properties of (standard, unmasked) QKV attention apply here also. For permutation matrices, A , B {\displaystyle \mathbf {A} ,\mathbf {B} } : from which we also see that multi-head self-attention :
2300-559: 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. The overall training was conducted on processing power between 100 and 200 GPUs . AlphaFold 1 (2018)
2400-424: Is large, q 0 k 1 T {\displaystyle q_{0}k_{1}^{T}} is small, and the rest are very small. This can be interpreted as saying that the attention weight should be mostly applied to the 0th hidden vector of the encoder, a little to the 1st, and essentially none to the rest. In order to make a properly weighted sum, we need to transform this list of dot products into
2500-476: Is much less reliable but can sometimes yield models with the correct fold (usually, for proteins less than 100-150 amino acids). Truly new folds are becoming quite rare among the targets, making that category smaller than desirable. The primary method of evaluation is a comparison of the predicted model α-carbon positions with those in the target structure. The comparison is shown visually by cumulative plots of distances between pairs of equivalents α-carbon in
2600-452: Is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind. The software design used in AlphaFold 1 contained a number of modules, each trained separately, that were used to produce the guide potential that was then combined with the physics-based energy potential. AlphaFold 2 replaced this with a system of sub-networks coupled together into
2700-451: Is the "causally masked self-attention". The size of the attention matrix is proportional to the square of the number of input tokens. Therefore, when the input is long, calculating the attention matrix requires a lot of GPU memory. Flash attention is an implementation that reduces the memory needs and increases efficiency without sacrificing accuracy. It achieves this by partitioning the attention computation into smaller blocks that fit into
2800-495: Is then lower triangular , with zeros in all elements above the diagonal. The masking ensures that for all 1 ≤ i < j ≤ n {\displaystyle 1\leq i<j\leq n} , row i {\displaystyle i} of the attention output is independent of row j {\displaystyle j} of any of the three input matrices. The permutation invariance and equivariance properties of standard QKV attention do not hold for
2900-475: Is then used to compute the context vector : c 0 = w 00 v 0 + w 01 v 1 + ⋯ {\displaystyle c_{0}=w_{00}v_{0}+w_{01}v_{1}+\cdots } where v 0 = h 0 W V , v 1 = h 1 W V , … {\displaystyle v_{0}=h_{0}W^{V},v_{1}=h_{1}W^{V},\dots } are
3000-610: Is used as a building block for an autoregressive decoder, and when at training time all input and output matrices have n {\displaystyle n} rows, a masked attention variant is used: Attention ( Q , K , V ) = softmax ( Q K T d k + M ) V {\displaystyle {\text{Attention}}(\mathbf {Q} ,\mathbf {K} ,\mathbf {V} )={\text{softmax}}\left({\frac {\mathbf {Q} \mathbf {K} ^{T}}{\sqrt {d_{k}}}}+\mathbf {M} \right)\mathbf {V} } where
3100-751: The Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research earlier in 2023. 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 , which are all expensive and time-consuming. Such efforts, using
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3200-596: The permutation invariance and permutation equivariance properties of QKV attention, let A ∈ R m × m {\displaystyle \mathbf {A} \in \mathbb {R} ^{m\times m}} and B ∈ R n × n {\displaystyle \mathbf {B} \in \mathbb {R} ^{n\times n}} be permutation matrices ; and D ∈ R m × n {\displaystyle \mathbf {D} \in \mathbb {R} ^{m\times n}} an arbitrary matrix. The softmax function
3300-521: The softmax function is applied independently to every row of its argument. The matrix Q {\displaystyle \mathbf {Q} } contains m {\displaystyle m} queries, while matrices K , V {\displaystyle \mathbf {K} ,\mathbf {V} } jointly contain an unordered set of n {\displaystyle n} key-value pairs. Value vectors in matrix V {\displaystyle \mathbf {V} } are weighted using
3400-417: The value vectors, linearly transformed by another matrix to provide the model with freedom to find the best way to represent values. Without the matrices W Q , W K , W V {\displaystyle W^{Q},W^{K},W^{V}} , the model would be forced to use the same hidden vector for both key and value, which might not be appropriate, as these two tasks are not
3500-639: The "Pairformer", a deep learning architecture inspired from the transformer, considered similar but simpler than the Evoformer introduced with AlphaFold 2. The raw predictions from the Pairformer module are passed to a diffusion model , which starts with a cloud of atoms and uses these predictions to iteratively progress towards a 3D depiction of the molecular structure. The AlphaFold server was created to provide free access to AlphaFold 3 for non-commercial research. In December 2018, DeepMind's AlphaFold placed first in
3600-437: The 2020 competition had a GDT_TS score of more than 80. On the group of targets classed as the most difficult, AlphaFold 2 achieved a median score of 87. Measured by the root-mean-square deviation (RMS-D) of the placement of the alpha-carbon atoms of the protein backbone chain, which tends to be dominated by the performance of the worst-fitted outliers, 88% of AlphaFold 2's predictions had an RMS deviation of less than 4 Å for
3700-524: The CASP13 dataset (links below). The feature generation code is tightly coupled to our internal infrastructure as well as external tools, hence we are unable to open-source it." Therefore, in essence, the code deposited is not suitable for general use but only for the CASP13 proteins. The company has not announced plans to make their code publicly available as of 5 March 2021. In November 2020, DeepMind's new version, AlphaFold 2, won CASP14. Overall, AlphaFold 2 made
3800-672: The GPU's faster on-chip memory, reducing the need to store large intermediate matrices and thus lowering memory usage while increasing computational efficiency. For matrices: Q ∈ R m × d k , K ∈ R n × d k {\displaystyle \mathbf {Q} \in \mathbb {R^{m\times d_{k}}} ,\mathbf {K} \in \mathbb {R^{n\times d_{k}}} } and V ∈ R n × d v {\displaystyle \mathbf {V} \in \mathbb {R^{n\times d_{v}}} } ,
3900-887: The Query and Key vectors, where one item of interest (the Query vector "that") is matched against all possible items (the Key vectors of each word in the sentence). However, Attention's parallel calculations matches all words of a sentence with itself; therefore the roles of these vectors are symmetric . Possibly because the simplistic database analogy is flawed, much effort has gone into understand Attention further by studying their roles in focused settings, such as in-context learning, masked language tasks, stripped down transformers, bigram statistics, N-gram statistics, pairwise convolutions, and arithmetic factoring. Many variants of attention implement soft weights, such as For convolutional neural networks , attention mechanisms can be distinguished by
4000-501: The SARS-CoV-2 virus. Specifically, AlphaFold 2's prediction of the structure of the ORF3a protein was very similar to the structure determined by researchers at University of California, Berkeley using cryo-electron microscopy . This specific protein is believed to assist the virus in breaking out of the host cell once it replicates. This protein is also believed to play a role in triggering
4100-538: The University of Washington. The open access to source code of several AlphaFold versions (excluding AlphaFold 3) has been provided by DeepMind after requests from the scientific community. Full source code of AlphaFold-3 is expected to be provided to open access by the end of 2024. The AlphaFold Protein Structure Database was launched on July 22, 2021, as a joint effort between AlphaFold and EMBL-EBI . At launch
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4200-467: The alignment of the model and the structure, such as shown in the figure (a perfect model would stay at zero all the way across), and is assigned a numerical score GDT-TS (Global Distance Test—Total Score) describing percentage of well-modeled residues in the model with respect to the target. Free modeling (template-free, or de novo ) is also evaluated visually by the assessors, since the numerical scores do not work as well for finding loose resemblances in
4300-661: The angled brackets denote dot product. This shows that it involves a multiplicative operation. Multiplicative operations within artificial neural networks had been studied under the names of Group Method of Data Handling (1965) (where Kolmogorov-Gabor polynomials implement multiplicative units or "gates" ), higher-order neural networks , multiplication units , sigma-pi units , fast weight controllers , and hyper-networks . In fast weight controller ( Schmidhuber , 1992), one of its two networks has "fast weights" or "dynamic links" (1981). A slow neural network learns by gradient descent to generate keys and values for computing
4400-399: The array shown in red). Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learnt from training data. These transformations are iterated, the updated information output by one step becoming the input of the next, with
4500-436: The attention mechanism is more nuanced. On the first pass through the decoder, 94% of the attention weight is on the first English word I , so the network offers the word je . On the second pass of the decoder, 88% of the attention weight is on the third English word you , so it offers t' . On the last pass, 95% of the attention weight is on the second English word love , so it offers aime . As hand-crafting weights defeats
4600-412: The attention mechanism was developed to address the weaknesses of leveraging information from the hidden layers of recurrent neural networks. Recurrent neural networks favor more recent information contained in words at the end of a sentence, while information earlier in the sentence tends to be attenuated . Attention allows a token equal access to any part of a sentence directly, rather than only through
4700-445: The backwards training pass, "soft" weights exist only in the forward pass and therefore change with every step of the input. Earlier designs implemented the attention mechanism in a serial recurrent neural network language translation system, but a more recent design, namely the transformer , removed the slower sequential RNN and relied more heavily on the faster parallel attention scheme. Inspired by ideas about attention in humans ,
4800-545: The best prediction for 88 out of the 97 targets. On the competition's preferred global distance test (GDT) measure of accuracy, the program achieved a median score of 92.4 (out of 100), meaning that more than half of its predictions were scored at better than 92.4% for having their atoms in more-or-less the right place, a level of accuracy reported to be comparable to experimental techniques like X-ray crystallography . In 2018 AlphaFold 1 had only reached this level of accuracy in two of all of its predictions. 88% of predictions in
4900-481: The clumps in a larger whole." The output of these iterations then informs the final structure prediction module, which also uses transformers, 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
5000-401: The database contains AlphaFold-predicted models of protein structures of nearly the full UniProt proteome of humans and 20 model organisms , amounting to 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 planned to add more sequences to the collection,
5100-406: The dimension on which they operate, namely: spatial attention, channel attention, or combinations. Much effort has gone into understand Attention further by studying their roles in focused settings, such as in-context learning, masked language tasks, stripped down transformers, bigram statistics, N-gram statistics, pairwise convolutions, and arithmetic factoring. These variants recombine
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#17328842884445200-399: The encoder has finished processing, the decoder starts operating over the hidden vectors, to produce an output sequence y 0 , y 1 , … {\displaystyle y_{0},y_{1},\dots } , autoregressively. That is, it always takes as input both the hidden vectors produced by the encoder, and what the decoder itself has produced before, to produce
5300-471: The encoder-side inputs to redistribute those effects to each target output. Often, a correlation-style matrix of dot products provides the re-weighting coefficients. In the figures below, W is the matrix of context attention weights, similar to the formula in Core Calculations section above. Self-attention is essentially the same as cross-attention, except that query, key, and value vectors all come from
5400-518: The entrants used AlphaFold or tools incorporating AlphaFold. AlphaFold 2 scoring more than 90 in CASP 's global distance test (GDT) is considered a significant achievement in computational biology and great progress towards a decades-old grand challenge of biology. Nobel Prize winner and structural biologist Venki Ramakrishnan called the result "a stunning advance on the protein folding problem", adding that "It has occurred decades before many people in
5500-423: 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
5600-417: The experimentally determined SARS-CoV-2 spike protein that was shared in the Protein Data Bank , an international open-access database, before releasing the computationally determined structures of the under-studied protein molecules. 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
5700-456: The field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research." Propelled by press releases from CASP and DeepMind, AlphaFold 2's success received wide media attention. As well as news pieces in the specialist science press, such as Nature , Science , MIT Technology Review , and New Scientist , the story was widely covered by major national newspapers,. A frequent theme
5800-440: 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
5900-557: The inflammatory response to the infection. Critical Assessment of Structure Prediction Critical Assessment of Structure Prediction ( CASP ), sometimes called Critical Assessment of Protein Structure Prediction , is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994. CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of
6000-492: The initial goal (as of beginning of 2022) being to cover most of the UniRef90 set of more than 100 million proteins. As of May 15, 2022, 992,316 predictions were available. 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
6100-408: The input sequence in parallel, before computing a "soft alignment matrix" ( alignment is the terminology used by Bahdanau et al ) in order to allow for parallel processing. The idea of using the attention mechanism for self-attention, instead of in an encoder-decoder (cross-attention), was also proposed during this period, such as in differentiable neural computers and neural Turing machines . It
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#17328842884446200-434: The inputs and outputs of QKV attention blocks. For example, a simple self-attention function defined as: is permutation equivariant with respect to re-ordering the rows of the input matrix X {\displaystyle X} in a non-trivial way, because every row of the output is a function of all the rows of the input. Similar properties hold for multi-head attention , which is defined below. When QKV attention
6300-490: The lab experiment determined structure, with 100 being a complete match, within the distance cutoff used for calculating GDT. AlphaFold 2's results at CASP14 were described as "astounding" and "transformational". Some researchers noted that the accuracy is not high enough for a third of its predictions, and that it does not reveal the mechanism or rules of protein folding for the protein folding problem to be considered solved. Nevertheless, there has been widespread respect for
6400-468: The mask, M ∈ R n × n {\displaystyle \mathbf {M} \in \mathbb {R} ^{n\times n}} is a strictly upper triangular matrix , with zeros on and below the diagonal and − ∞ {\displaystyle -\infty } in every element above the diagonal. The softmax output, also in R n × n {\displaystyle \mathbb {R} ^{n\times n}}
6500-418: The masked variant. Multi-head attention MultiHead ( Q , K , V ) = Concat ( head 1 , . . . , head h ) W O {\displaystyle {\text{MultiHead}}(\mathbf {Q} ,\mathbf {K} ,\mathbf {V} )={\text{Concat}}({\text{head}}_{1},...,{\text{head}}_{h})\mathbf {W} ^{O}} where each head
6600-441: The model to make a context vector consisting of a weighted sum of the hidden vectors, rather than "the best one", as there may not be a best hidden vector. This view of the attention weights addresses some of the neural network explainability problem. Networks that perform verbatim translation without regard to word order would show the highest scores along the (dominant) diagonal of the matrix. The off-diagonal dominance shows that
6700-498: The model with enough freedom to find the best way to represent the data. Now, the query and keys are compared by taking dot products: q 0 k 0 T , q 0 k 1 T , … {\displaystyle q_{0}k_{0}^{T},q_{0}k_{1}^{T},\dots } . Ideally, the model should have learned to compute the keys and values, such that q 0 k 0 T {\displaystyle q_{0}k_{0}^{T}}
6800-536: The most difficult cases. High-accuracy template-based predictions were evaluated in CASP7 by whether they worked for molecular-replacement phasing of the target crystal structure with successes followed up later, and by full-model (not just α-carbon ) model quality and full-model match to the target in CASP8. Evaluation of the results is carried out in the following prediction categories: Tertiary structure prediction category
6900-419: The next output word: Here, we use the special <start> token as a control character to delimit the start of input for the decoder. The decoding terminates as soon as "<end>" appears in the decoder output. In translating between languages, alignment is the process of matching words from the source sentence to words of the translated sentence. In the I love you example above, the second word love
7000-539: 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
7100-440: The organizers and assessors know the structures of the target proteins at the time when predictions are made. Targets for structure prediction are either structures soon-to-be solved by X-ray crystallography or NMR spectroscopy, or structures that have just been solved (mainly by one of the structural genomics centers ) and are kept on hold by the Protein Data Bank . If the given sequence is found to be related by common descent to
7200-460: The overall rankings of the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP). The program was particularly successfully predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing template structures were available from proteins with a partially similar sequence. AlphaFold gave the best prediction for 25 out of 43 protein targets in this class, achieving
7300-564: The planet. AlphaFold has various limitations: 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. Results were examined by the scientists at the Francis Crick Institute in the United Kingdom before release into the larger research community. The team also confirmed accurate prediction against
7400-539: The previous state. Academic reviews of the history of the attention mechanism are provided in Niu et al. and Soydaner. Selective attention in humans had been well studied in neuroscience and cognitive psychology. In 1953, Colin Cherry studied selective attention in the context of audition, known as the cocktail party effect . In 1958, Donald Broadbent proposed the filter model of attention . Selective attention of vision
7500-408: The purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries , we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs. The decoder sends in a query , and obtains a reply in the form of a weighted sum of the values , where
7600-541: The querying vector, h 0 d {\displaystyle h_{0}^{d}} , is not necessarily the same as the key-value vector h 0 {\displaystyle h_{0}} . In fact, it is theoretically possible for query, key, and value vectors to all be different, though that is rarely done in practice. This attention scheme has been compared to the Query-Key analogy of relational databases. That comparison suggests an asymmetric role for
7700-478: The same model. Both encoder and decoder can use self-attention, but with subtle differences. For encoder self-attention, we can start with a simple encoder without self-attention, such as an "embedding layer", which simply converts each input word into a vector by a fixed lookup table . This gives a sequence of hidden vectors h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } . These can then be applied to
7800-554: The same. This is the dot-attention mechanism. The particular version described in this section is "decoder cross-attention", as the output context vector is used by the decoder, and the input keys and values come from the encoder, but the query comes from the decoder, thus "cross-attention". More succinctly, we can write it as c 0 = A t t e n t i o n ( h 0 d W Q , H W K , H W V ) = s o f t m
7900-612: The scaled dot-product, or QKV attention is defined as: Attention ( Q , K , V ) = softmax ( Q K T d k ) V ∈ R m × d v {\displaystyle {\text{Attention}}(\mathbf {Q} ,\mathbf {K} ,\mathbf {V} )={\text{softmax}}\left({\frac {\mathbf {Q} \mathbf {K} ^{T}}{\sqrt {d_{k}}}}\right)\mathbf {V} \in \mathbb {R} ^{m\times d_{v}}} where T {\displaystyle {}^{T}} denotes transpose and
8000-514: The scientific journal Proteins , all of which are accessible through the CASP website. A lead article in each of these supplements describes specifics of the experiment while a closing article evaluates progress in the field. In December 2018, CASP13 made headlines when it was won by AlphaFold , an artificial intelligence program created by DeepMind . In November 2020, an improved version 2 of AlphaFold won CASP14. According to one of CASP co-founders John Moult, AlphaFold scored around 90 on
8100-414: The sequence, allowing a contact map to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this to estimate a probability distribution for just how close the residues might be likely to be—turning the contact map into a likely distance map. It also used more advanced learning methods than previously to develop the inference. The 2020 version of the program ( AlphaFold 2 , 2020)
8200-564: The set of overlapped C-alpha atoms. 76% of predictions achieved better than 3 Å, and 46% had a C-alpha atom RMS accuracy better than 2 Å, with a median RMS deviation in its predictions of 2.1 Å for a set of overlapped CA atoms. AlphaFold 2 also achieved an accuracy in modelling surface side chains described as "really really extraordinary". To additionally verify AlphaFold-2 the conference organisers approached four leading experimental groups for structures they were finding particularly challenging and had been unable to determine. In all four cases
8300-466: The sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information. As the iteration progresses, according to one report, the "attention algorithm ... mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps—in this case clusters of amino acids—and then searching for ways to join
8400-612: The special <end> token as a control character to delimit the end of input for both the encoder and the decoder. An input sequence of text x 0 , x 1 , … {\displaystyle x_{0},x_{1},\dots } is processed by a neural network (which can be an LSTM, a Transformer encoder, or some other network) into a sequence of real-valued vectors h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , where h {\displaystyle h} stands for "hidden vector". After
8500-405: The state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence many view the experiment more as a "world championship" in this field of science. More than 100 research groups from all over the world participate in CASP on
8600-504: The technical achievement. On 15 July 2021 the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes . The paper has since been cited more than 27 thousand times. AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA , RNA , various ligands , and ions . The new prediction method shows
8700-511: The three-dimensional models produced by AlphaFold 2 were sufficiently accurate to determine structures of these proteins by molecular replacement . These included target T1100 (Af1503), a small membrane protein studied by experimentalists for ten years. Of the three structures that AlphaFold 2 had the least success in predicting, two had been obtained by protein NMR methods, which define protein structure directly in aqueous solution, whereas AlphaFold
8800-427: The time at accurately predicting protein-protein interactions. 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 . AlphaFold 3 introduces
8900-418: 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. DeepMind
9000-403: The weight changes of the fast neural network which computes answers to queries. This was later shown to be equivalent to the unnormalized linear Transformer. A follow-up paper developed a similar system with active weight changing. During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. In machine translation, the seq2seq model, as it
9100-413: The weight is proportional to how closely the query resembles each key . The decoder first processes the "<start>" input partially, to obtain an intermediate vector h 0 d {\displaystyle h_{0}^{d}} , the 0th hidden vector of decoder. Then, the intermediate vector is transformed by a linear map W Q {\displaystyle W^{Q}} into
9200-444: The weights resulting from the softmax operation, so that the rows of the m {\displaystyle m} -by- d v {\displaystyle d_{v}} output matrix are confined to the convex hull of the points in R d v {\displaystyle \mathbb {R} ^{d_{v}}} given by the rows of V {\displaystyle \mathbf {V} } . To understand
9300-456: Was built on work developed by various teams in the 2010s, work that looked at the large databanks of related DNA sequences now available from many different organisms (most without known 3D structures), to try to find changes at different residues that appeared to be correlated, even though the residues were not consecutive in the main chain. Such correlations suggest that the residues may be close to each other physically, even though not close in
9400-503: Was further subdivided into: Starting with CASP7, categories have been redefined to reflect developments in methods. The 'Template based modeling' category includes all former comparative modeling, homologous fold based models and some analogous fold based models. The 'template free modeling (FM)' category includes models of proteins with previously unseen folds and hard analogous fold based models. Due to limited numbers of template free targets (they are quite rare), in 2011 so called CASP ROLL
9500-438: Was introduced. This continuous (rolling) CASP experiment aims at more rigorous evaluation of template free prediction methods through assessment of a larger number of targets outside of the regular CASP prediction season. Unlike LiveBench and EVA , this experiment is in the blind-prediction spirit of CASP, i.e. all the predictions are made on yet unknown structures. The CASP results are published in special supplement issues of
9600-441: Was mostly trained on protein structures in crystals . The third exists in nature as a multidomain complex consisting of 52 identical copies of the same domain , a situation AlphaFold was not programmed to consider. For all targets with a single domain, excluding only one very large protein and the two structures determined by NMR, AlphaFold 2 achieved a GDT_TS score of over 80. In 2022, DeepMind did not enter CASP15, but most of
9700-435: Was proposed in 2014, would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector would be unable to carry enough information for accurate decoding. An attention mechanism was proposed to solve this problem. An image captioning model was proposed in 2015, citing inspiration from the seq2seq model. that would encode an input image into
9800-406: Was studied in the 1960s by George Sperling 's partial report paradigm . It was also noticed that saccade control is modulated by cognitive processes, insofar as the eye moves preferentially towards areas of high salience . As the fovea of the eye is small, the eye cannot sharply resolve the entire visual field at once. The use of saccade control allows the eye to quickly scan important features of
9900-533: Was termed intra-attention where an LSTM is augmented with a memory network as it encodes an input sequence. These strands of development were brought together in 2017 with the Transformer architecture , published in the Attention Is All You Need paper. The seq2seq method developed in the early 2010s uses two neural networks: an encoder network converts an input sentence into numerical vectors, and
10000-445: Was that ability to predict protein structures accurately based on the constituent amino acid sequence is expected to have a wide variety of benefits in the life sciences space including accelerating advanced drug discovery and enabling better understanding of diseases. Some have noted that even a perfect answer to the protein prediction problem would still leave questions about the protein folding problem—understanding in detail how
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