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 structure
. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold
, 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 introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.
We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein Structure Prediction ...(CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end‐to‐end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large‐scale structure prediction.
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort
, the structures of around ...100,000 unique proteins have been determined
, but this represents a small fraction of the billions of known protein sequences
. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'
-has been an important open research problem for more than 50 years
. Despite recent progress
, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)
, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence
. This problem is of fundamental importance as the structure of a protein ...largely determines its function
; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures
. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force
that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction
(CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores
of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined
.
The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant ...population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.
Editor’s summary
Single–amino acid changes in proteins sometimes have little effect but can often lead to problems in protein folding, activity, or stability. Only a small fraction of variants have been experimentally investigated, but there are vast amounts of biological sequence data that are suitable for use as training data for machine learning approaches. Cheng
et al
. developed AlphaMissense, a deep learning model that builds on the protein structure prediction tool AlphaFold2 (see the Perspective by Marsh and Teichmann). The model is trained on population frequency data and uses sequence and predicted structural context, all of which contribute to its performance. The authors evaluated the model against related methods using clinical databases not included in the training and demonstrated agreement with multiplexed assays of variant effect. Predictions for all single–amino acid substitutions in the human proteome are provided as a community resource. —Michael A. Funk
AlphaFold fine-tuned on human and primate population variant frequency databases predicts variant pathogenicity.
INTRODUCTION
Genome sequencing has revealed extensive genetic variation in human populations. Missense variants are genetic variants that alter the amino acid sequence of proteins. Pathogenic missense variants disrupt protein function and reduce organismal fitness, while benign missense variants have limited effect.
RATIONALE
Classifying these variants is an important ongoing challenge in human genetics. Of more than 4 million observed missense variants, only an estimated 2% have been clinically classified as pathogenic or benign, while the vast majority of them are of unknown clinical significance. This limits the diagnosis of rare diseases, as well as the development or application of clinical treatments that target the underlying genetic cause. Machine learning approaches could close the variant interpretation gap by exploiting patterns in biological data to predict the pathogenicity of unannotated variants. Specifically, AlphaFold, which accurately predicts protein structure from protein sequence, may be used as a foundation to predict the pathogenicity of variants on proteins.
RESULTS
We developed AlphaMissense to leverage advances on multiple fronts: (i) unsupervised protein language modeling to learn amino acid distributions conditioned on sequence context; (ii) incorporating structural context by using an AlphaFold-derived system; and (iii) fine-tuning on weak labels from population frequency data, thereby avoiding bias from human-curated annotations. AlphaMissense achieves state-of-the-art missense pathogenicity predictions in clinical annotation, de novo disease variants, and experimental assay benchmarks without explicitly training on such data. As a resource to the community, we provide a database of predictions for all possible single amino acid substitutions in the human proteome. We classify 32% of all missense variants as likely pathogenic and 57% as likely benign using a cutoff yielding 90% precision on the ClinVar dataset, thereby providing a confident prediction for most human missense variants.
We show how this resource can be used to accelerate research in multiple fields. Molecular biologists could use the database as a starting point for designing and interpreting experiments that probe saturating amino acid substitutions across the human proteome. Human geneticists could combine gene-level AlphaMissense predictions with population cohort–based approaches to quantify the functional significance of genes, especially for shorter human genes where cohort-based approaches lack statistical power. Finally, clinicians could benefit from the boost in coverage of confidently classified pathogenic variants when prioritizing de novo variants for rare disease diagnostics, and AlphaMissense predictions could inform studies of complex trait genetics that use annotations of rare, likely deleterious variants.
CONCLUSION
AlphaMissense predictions may illuminate the molecular effects of variants on protein function, contribute to the identification of pathogenic missense mutations and previously unknown disease-causing genes, and increase the diagnostic yield of rare genetic diseases. AlphaMissense will also foster further development of specialized protein variant effect predictors from structure prediction models.
AlphaMissense pathogenicity prediction.
AlphaMissense takes as input a missense variant and predicts its pathogenicity. We fine-tuned AlphaFold on human and primate variant population frequency data and calibrated the confidence on known disease variants. AlphaMissense predicts the probability of a missense variant being pathogenic and classifies it as either likely benign, likely pathogenic, or uncertain. We provide predictions for all possible human missense variants as a resource for the community.
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 assessors' ranking by summed z‐scores, this system scored highest with 68.3 vs 48.2 for the next closest group (an average GDT_TS of 61.4). The system produced high‐accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 FM domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template‐based methods.
Conventional approaches to statistical parametric speech synthesis typically use decision tree-clustered context-dependent hidden Markov models (HMMs) to represent probability densities of speech ...parameters given texts. Speech parameters are generated from the probability densities to maximize their output probabilities, then a speech waveform is reconstructed from the generated parameters. This approach is reasonably effective but has a couple of limitations, e.g. decision trees are inefficient to model complex context dependencies. This paper examines an alternative scheme that is based on a deep neural network (DNN). The relationship between input texts and their acoustic realizations is modeled by a DNN. The use of the DNN can address some limitations of the conventional approach. Experimental results show that the DNN-based systems outperformed the HMM-based systems with similar numbers of parameters.
Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech recognition tasks. CNNs, LSTMs and ...DNNs are complementary in their modeling capabilities, as CNNs are good at reducing frequency variations, LSTMs are good at temporal modeling, and DNNs are appropriate for mapping features to a more separable space. In this paper, we take advantage of the complementarity of CNNs, LSTMs and DNNs by combining them into one unified architecture. We explore the proposed architecture, which we call CLDNN, on a variety of large vocabulary tasks, varying from 200 to 2,000 hours. We find that the CLDNN provides a 4-6% relative improvement in WER over an LSTM, the strongest of the three individual models.
The claim that incoherent scatter radar data show electron density enhancements of 50–80% during some ionospheric heating experiments in a recent paper is questioned. The backscatter from the ...monostatic radar can indeed be enhanced during these experiments, but the conclusion that a large electron density increase is the cause is almost certainly wrong. Some natural plasma line data are presented in support of our claim. Previously published studies of similar events and a possible explanation for the observed increases in backscattered power are pointed out.
Statistical parametric speech synthesis (SPSS) using deep neural networks (DNNs) has shown its potential to produce naturally-sounding synthesized speech. However, there are limitations in the ...current implementation of DNN-based acoustic modeling for speech synthesis, such as the unimodal nature of its objective function and its lack of ability to predict variances. To address these limitations, this paper investigates the use of a mixture density output layer. It can estimate full probability density functions over real-valued output features conditioned on the corresponding input features. Experimental results in objective and subjective evaluations show that the use of the mixture density output layer improves the prediction accuracy of acoustic features and the naturalness of the synthesized speech.
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