Accurate structure prediction of biomolecular interactions with AlphaFold 3

Nature, Journal Year: 2024, Volume and Issue: 630(8016), P. 493 - 500

Published: May 8, 2024

Abstract The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure proteins and their interactions, enabling huge range applications protein design 2–6 . Here we describe our 3 model with substantially updated diffusion-based architecture that is capable predicting joint complexes including proteins, nucleic acids, small molecules, ions modified residues. new demonstrates improved accuracy over many previous specialized tools: far greater for protein–ligand interactions compared state-of-the-art docking tools, much higher protein–nucleic acid nucleic-acid-specific predictors antibody–antigen prediction AlphaFold-Multimer v.2.3 7,8 Together, these results show high-accuracy across biomolecular space possible within single unified deep-learning framework.

Language: Английский

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Sampling alternative conformational states of transporters and receptors with AlphaFold2

eLife, Journal Year: 2022, Volume and Issue: 11

Published: March 3, 2022

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate passage molecules across cell membranes by alternating between inward- outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although plasticity these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, recent success AlphaFold2 (AF2) in CASP14 culminated modeling transporter multiple conformations to high accuracy. Given AF2 was designed predict static structures proteins, it remains unclear if this result represents an underexplored capability accurately and/or heterogeneity. Here, we present approach drive sample alternative topologically diverse G-protein-coupled are absent from training set. Whereas models most generated using default pipeline conformationally homogeneous nearly identical one another, reducing depth input sequence alignments stochastic subsampling led generation accurate conformations. In our benchmark, spanned range two experimental interest, with at extremes distributions observed be among (average template score 0.94). These results suggest straightforward identifying native-like also highlighting need next deep learning algorithms ensembles biophysically relevant states.

Language: Английский

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High-resolutionde novostructure prediction from primary sequence

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: July 22, 2022

Abstract Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan or fast-evolving like antibodies, and a typically folds natural setting from its primary amino acid into three-dimensional structure, suggesting that should be necessary protein’s folded form. Here, we introduce OmegaFold, the first computational method successfully high-resolution structure single alone. Using new combination language model allows us make predictions sequences geometry-inspired transformer trained on structures, OmegaFold outperforms RoseTTAFold achieves similar prediction accuracy AlphaFold2 recently released enables accurate do belong any functionally characterized family antibodies tend noisy due fast evolution. Our study fills much-encountered gap brings step closer understanding folding nature.

Language: Английский

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AlphaFold2 and its applications in the fields of biology and medicine

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: March 14, 2023

Abstract AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction one the most challenging problems in computational biology and chemistry, has puzzled scientists for 50 years. The advent AF2 presents unprecedented progress protein attracted much attention. Subsequent release more than 200 million predicted further aroused great enthusiasm science community, especially fields medicine. thought to have a significant impact on structural research areas need information, such as drug discovery, design, function, et al. Though time not long since was developed, there are already quite few application studies medicine, many them having preliminarily proved potential AF2. To better understand promote its applications, we will this article summarize principle architecture well recipe success, particularly focus reviewing applications Limitations current also be discussed.

Language: Английский

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Predicting multiple conformations via sequence clustering and AlphaFold2

Nature, Journal Year: 2023, Volume and Issue: 625(7996), P. 832 - 839

Published: Nov. 13, 2023

AlphaFold2 (ref. 1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates2, and disease-causing point mutations cause population changes within these substates3,4. We demonstrate that clustering multiple-sequence alignment sequence similarity enables to sample alternative states known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution predicted for protein KaiB5 found predictions both conformations were distributed in clusters across KaiB family. used nuclear magnetic resonance spectroscopy confirm an AF-Cluster prediction: cyanobacteria variant is stabilized opposite state compared more widely studied variant. To test AF-Cluster's sensitivity mutations, designed experimentally verified set three flip from Rhodobacter sphaeroides ground fold-switched state. Finally, screening families without fold switching identified putative oxidoreductase Mpt53 Mycobacterium tuberculosis. Further development such bioinformatic methods tandem experiments will probably have considerable impact energy landscapes, essential illuminating function.

Language: Английский

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Multi‐state modeling of G‐protein coupled receptors at experimental accuracy

Proteins Structure Function and Bioinformatics, Journal Year: 2022, Volume and Issue: 90(11), P. 1873 - 1885

Published: May 5, 2022

The family of G-protein coupled receptors (GPCRs) is one the largest protein families in human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts state biased toward either or conformation depending on GPCR class. Here, multi-state prediction protocol introduced that extends predict at very high accuracy using state-annotated templated databases. predicted models accurately capture main structural changes activation atomic level. For most benchmarked (10 out 15), were closer their corresponding structures. Median RMSDs transmembrane 1.12 Å 1.41 for models, respectively. more suitable protein-ligand docking than original template-based models. Finally, our accurate GPCR-peptide complex Dock 2021, blind GPCR-ligand modeling competition. We expect both will promote understanding mechanisms drug discovery GPCRs. At time, new paves way towards capturing dynamics proteins machine-learning methods.

Language: Английский

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SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(8), P. e1010483 - e1010483

Published: Aug. 22, 2022

The unprecedented performance of Deepmind's Alphafold2 in predicting protein structure CASP XIV and the creation a database structures for multiple proteomes sequence repositories is reshaping structural biology. However, because this returns single structure, it brought into question Alphafold's ability to capture intrinsic conformational flexibility proteins. Here we present general approach drive model alternate conformations through simple manipulation alignment via silico mutagenesis. grounded hypothesis that must also encode heterogeneity, thus its rational will enable sample conformations. A systematic modeling pipeline benchmarked against canonical examples applied interrogate landscape membrane This work broadens applicability by generating be tested biologically, biochemically, biophysically, use structure-based drug design.

Language: Английский

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AlphaFold2 fails to predict protein fold switching

Protein Science, Journal Year: 2022, Volume and Issue: 31(6)

Published: May 26, 2022

AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model folds with atomic-level accuracy. Nevertheless, previous work shown that these predictions tend be inaccurate for structurally heterogeneous proteins. To systematically assess factors contribute this inaccuracy, we tested AlphaFold2's performance on 98-fold-switching proteins, which assume at least two distinct-yet-stable secondary and tertiary structures. Topological similarities were quantified between five predicted experimentally determined structures of each fold-switching protein. Overall, 94% captured one conformation but not the other. Despite biased results, estimated confidences moderate-to-high 74% residues, a result contrasts overall low intrinsically disordered are also heterogeneous. investigate contributing disparity, variation within multiple alignments used generate Unlike regions, whose show conservation, regions had conservation rates statistically similar canonical single-fold Furthermore, lower than either or regardless conservation. high fold switchers indicate it uses sophisticated pattern recognition search most probable conformer rather biophysics protein's structural ensemble. Thus, is surprising its often fail proteins properties fully apparent from solved Our results emphasize need look as an ensemble suggest systematic examination sequences may reveal propensities stable

Language: Английский

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Modeling conformational states of proteins with AlphaFold

Current Opinion in Structural Biology, Journal Year: 2023, Volume and Issue: 81, P. 102645 - 102645

Published: June 29, 2023

Language: Английский

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Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning

Nature Biotechnology, Journal Year: 2023, Volume and Issue: 41(12), P. 1810 - 1819

Published: March 20, 2023

While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or which few homologous sequences are known. Here we introduce AlphaLink, a modified version of algorithm incorporates experimental distance restraint information into its network architecture. By employing sparse contacts as anchor points, AlphaLink improves on performance in predicting challenging targets. We confirm this experimentally by using noncanonical amino acid photo-leucine to obtain residue-residue inside cells crosslinking mass spectrometry. The program distinct conformations basis restraints provided, demonstrating value data driving structure prediction. noise-tolerant framework integrating prediction presented here opens path characterization in-cell data.

Language: Английский

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