Accurate structure prediction of biomolecular interactions with AlphaFold 3

Nature, Год журнала: 2024, Номер 630(8016), С. 493 - 500

Опубликована: Май 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.

Язык: Английский

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

Proteins Structure Function and Bioinformatics, Год журнала: 2022, Номер 90(11), С. 1873 - 1885

Опубликована: Май 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.

Язык: Английский

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GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources

Nucleic Acids Research, Год журнала: 2022, Номер 51(D1), С. D395 - D402

Опубликована: Ноя. 18, 2022

G protein-coupled receptors (GPCRs) are physiologically abundant signaling hubs routing hundreds of extracellular signal substances and drugs into intracellular pathways. The GPCR database, GPCRdb supports >5000 interdisciplinary researchers every month with reference data, analysis, visualization, experiment design dissemination. Here, we present our fifth major release setting out an overview the many resources for receptor sequences, structures, ligands. This includes recently published additions class D generic residue numbers, a comparative structure analysis tool to identify functional determinants, trees clustering structures by 3D conformation, mutations stabilizing inactive/active states. We provide new state-specific models all human non-olfactory GPCRs built using AlphaFold2-MultiState. also resource endogenous ligands along larger number surrogate bioactivity, vendor, physiochemical descriptor data. one-stop-shop ligand integrate ligands/data from ChEMBL, Guide Pharmacology, PDSP Ki PubChem database. is available at https://gpcrdb.org.

Язык: Английский

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How good are AlphaFold models for docking-based virtual screening?

iScience, Год журнала: 2022, Номер 26(1), С. 105920 - 105920

Опубликована: Дек. 30, 2022

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures protein target. Whenever experimental were not available, homology modeling has been, so far, method choice. Recently, AlphaFold (AF), an artificial-intelligence-based structure prediction method, shown impressive results terms model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from perspective docking-based discovery. We compared high-throughput docking (HTD) performance with their corresponding PDB using a benchmark set 22 targets. The showed consistently worse four programs and two consensus techniques. Although shows remarkable ability predict architecture, this might be enough guarantee that can reliably used for HTD, post-modeling refinement strategies key increase chances success.

Язык: Английский

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Modeling conformational states of proteins with AlphaFold

Current Opinion in Structural Biology, Год журнала: 2023, Номер 81, С. 102645 - 102645

Опубликована: Июнь 29, 2023

Язык: Английский

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

Nature Biotechnology, Год журнала: 2023, Номер 41(12), С. 1810 - 1819

Опубликована: Март 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.

Язык: Английский

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Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

Journal of Chemical Information and Modeling, Год журнала: 2023, Номер 63(6), С. 1656 - 1667

Опубликована: Март 10, 2023

The recently developed AlphaFold2 (AF2) algorithm predicts proteins’ 3D structures from amino acid sequences. open AlphaFold protein structure database covers the complete human proteome. Using an industry-leading molecular docking method (Glide), we investigated virtual screening performance of 37 common drug targets, each with AF2 and known holo apo DUD-E data set. In a subset 27 targets where are suitable for refinement, show comparable early enrichment active compounds (avg. EF 1%: 13.0) to 11.4) while falling behind 24.2). With induced-fit protocol (IFD-MD), can refine using aligned binding ligand as template improve in structure-based 18.9). Glide-generated poses ligands also be used templates IFD-MD, achieving similar improvements 1% 18.0). Thus, proper preparation considerable promise silico hit identification.

Язык: Английский

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Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

Journal of Chemical Information and Modeling, Год журнала: 2023, Номер 63(6), С. 1668 - 1674

Опубликована: Март 9, 2023

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing usage these models virtual screening, none them focus on prospect hit-finding real-world screen with model based low prior information. In order to address this, we developed an AlphaFold2 version where exclude all templates more than 30% sequence identity from model-building process. previous study, used those conjunction state-of-the-art free energy perturbation methods demonstrated that it is possible obtain quantitatively accurate results. this work, using structures rigid receptor-ligand docking studies. Our results indicate out-of-the-box Alphafold2 not ideal scenario for screening campaigns; fact, strongly recommend include some post-processing modeling drive binding site into realistic holo model.

Язык: Английский

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AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug Discovery Today, Год журнала: 2023, Номер 28(6), С. 103551 - 103551

Опубликована: Март 11, 2023

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version buttressed by an innovative machine-learning approach that integrates physical biological knowledge about protein structures, raised drug hopes unsurprisingly, have not come to bear. Even though accurate, models are rigid, including pockets. AlphaFold's mixed performance poses question how its power can be harnessed in discovery. Here we discuss possible ways going forward wielding strengths, while bearing mind what AlphaFold cannot do. For kinases receptors, input enriched active (ON) state better chance rational design success.

Язык: Английский

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Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties

Frontiers in Molecular Biosciences, Год журнала: 2023, Номер 10

Опубликована: Фев. 16, 2023

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge structural biology. While integrative biology most effective way to get high-accuracy different conformations and mechanistic insights for larger proteins, advances deep machine-learning algorithms have paved fully computational predictions. In this field, AlphaFold2 (AF2) pioneered

Язык: Английский

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