Highly accurate protein structure prediction with AlphaFold

Nature, Journal Year: 2021, Volume and Issue: 596(7873), P. 583 - 589

Published: July 15, 2021

Abstract Proteins are essential to life, and understanding their structure can facilitate a mechanistic of function. Through an enormous experimental effort 1–4 , the structures around 100,000 unique proteins have been determined 5 but this represents small fraction billions known protein sequences 6,7 . Structural coverage is bottlenecked by months years painstaking required determine single structure. Accurate computational approaches needed address gap enable large-scale structural bioinformatics. Predicting three-dimensional that will adopt based solely on its amino acid sequence—the prediction component ‘protein folding problem’ 8 —has important open research problem for more than 50 9 Despite recent progress 10–14 existing methods fall far short atomic accuracy, especially when no homologous available. Here we provide first method regularly predict with accuracy even in cases which similar known. We validated entirely redesigned version our neural network-based model, AlphaFold, challenging 14th Critical Assessment Structure Prediction (CASP14) 15 demonstrating competitive majority greatly outperforming other methods. Underpinning latest AlphaFold novel machine learning approach incorporates physical biological knowledge about structure, leveraging multi-sequence alignments, into design deep algorithm.

Language: Английский

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ColabFold: making protein folding accessible to all

Nature Methods, Journal Year: 2022, Volume and Issue: 19(6), P. 679 - 682

Published: May 30, 2022

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

Language: Английский

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Accurate prediction of protein structures and interactions using a three-track neural network

Science, Journal Year: 2021, Volume and Issue: 373(6557), P. 871 - 876

Published: July 15, 2021

Deep learning takes on protein folding In 1972, Anfinsen won a Nobel prize for demonstrating connection between protein’s amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. methods took center stage at CASP14, with DeepMind’s Alphafold2 achieving remarkable accuracy. Baek et al . explored network architectures based DeepMind framework. They used three-track to process sequence, distance, coordinate information simultaneously achieved accuracies approaching those DeepMind. The method, RoseTTA fold, can solve challenging x-ray crystallography cryo–electron microscopy modeling problems generate accurate models protein-protein complexes. —VV

Language: Английский

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A guide to machine learning for biologists

Nature Reviews Molecular Cell Biology, Journal Year: 2021, Volume and Issue: 23(1), P. 40 - 55

Published: Sept. 13, 2021

Language: Английский

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Fast and accurate protein structure search with Foldseek

Nature Biotechnology, Journal Year: 2023, Volume and Issue: 42(2), P. 243 - 246

Published: May 8, 2023

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

Language: Английский

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Accurate proteome-wide missense variant effect prediction with AlphaMissense

Science, Journal Year: 2023, Volume and Issue: 381(6664)

Published: Sept. 19, 2023

The vast majority of missense variants observed in the human genome are unknown clinical significance. We present AlphaMissense, an adaptation AlphaFold fine-tuned on and primate variant population frequency databases to predict pathogenicity. By combining structural context evolutionary conservation, our model achieves state-of-the-art results across a wide range genetic experimental benchmarks, all without explicitly training such data. average pathogenicity score genes is also predictive for their cell essentiality, capable identifying short essential that existing statistical approaches underpowered detect. As resource community, we provide database predictions possible single amino acid substitutions classify 89% as either likely benign or pathogenic.

Language: Английский

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VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses

Microbiome, Journal Year: 2021, Volume and Issue: 9(1)

Published: Feb. 1, 2021

Viruses are a significant player in many biosphere and human ecosystems, but most signals remain "hidden" metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, insufficiently advanced identification tools.Here, we introduce VirSorter2, DNA RNA virus tool that leverages genome-informed advances across collection customized automatic classifiers improve accuracy range detection. When benchmarked against genomes from both isolated uncultivated viruses, VirSorter2 uniquely performed consistently with high (F1-score > 0.8) viral diversity, while all other tools under-detected viruses outside group represented reference databases (i.e., those order Caudovirales). Among evaluated, was also able minimize errors associated atypical cellular sequences including eukaryotic plasmids. Finally, as virosphere exploration unravels novel sequences, VirSorter2's modular design makes it inherently expand new types via maintain maximal sensitivity specificity.With multi-classifier design, demonstrates higher overall major groups will advance our knowledge evolution, virus-microbe interaction various ecosystems. Source code is freely available ( https://bitbucket.org/MAVERICLab/virsorter2 ), on bioconda an iVirus app CyVerse https://de.cyverse.org/de ). Video abstract.

Language: Английский

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Improved prediction of protein-protein interactions using AlphaFold2

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: March 10, 2022

Abstract Predicting the structure of interacting protein chains is a fundamental step towards understanding function. Unfortunately, no computational method can produce accurate structures complexes. AlphaFold2, has shown unprecedented levels accuracy in modelling single chain structures. Here, we apply AlphaFold2 for prediction heterodimeric We find that protocol together with optimised multiple sequence alignments, generate models acceptable quality (DockQ ≥ 0.23) 63% dimers. From predicted interfaces create simple function to predict DockQ score which distinguishes from incorrect as well non-interacting proteins state-of-art accuracy. that, using scores, identify 51% all pairs at 1% FPR.

Language: Английский

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Macromolecular modeling and design in Rosetta: recent methods and frameworks

Nature Methods, Journal Year: 2020, Volume and Issue: 17(7), P. 665 - 680

Published: June 1, 2020

Language: Английский

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Dali server: structural unification of protein families

Nucleic Acids Research, Journal Year: 2022, Volume and Issue: 50(W1), P. W210 - W215

Published: May 2, 2022

Protein structure is key to understanding biological function. Structure comparison deciphers deep phylogenies, providing insight into functional conservation and shifts during evolution. Until recently, structural coverage of the protein universe was limited by cost labour involved in experimental determination. Recent breakthroughs learning revolutionized bioinformatics accurate models numerous families for which no information existed. The Dali server 3D widely used crystallographers relate new structures pre-existing ones. Here, we report two most recent upgrades web server: (i) foldomes organisms AlphaFold Database (version 1) are searchable Dali, (ii) alignments annotated with families. Using these features, discovered a novel functionally diverse subgroup within WRKY/GCM1 clan. This accomplished linking structurally characterized SWI/SNF NAM as well CG-1 family uncharacterized proteins Gti1/Pac2, previously known member available at http://ekhidna2.biocenter.helsinki.fi/dali. website free open all users there login requirement.

Language: Английский

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