Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Science, Journal Year: 2024, Volume and Issue: 384(6693)

Published: March 7, 2024

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids DNA bases with an atomic all other groups model assemblies that contain proteins, nucleic acids, small molecules, metals, covalent modifications, given their sequences chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion (RFdiffusionAA), builds structures around molecules. Starting from random distributions acid residues surrounding target designed experimentally validated, through crystallography binding measurements, proteins bind the cardiac disease therapeutic digoxigenin, enzymatic cofactor heme, light-harvesting molecule bilin.

Language: Английский

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

iScience, Journal Year: 2022, Volume and Issue: 26(1), P. 105920 - 105920

Published: Dec. 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.

Language: Английский

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Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 9, 2023

Abstract Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules other non-protein that can play key roles in biological function. Here, we describe All-Atom (RFAA), a deep network capable of modeling full assemblies containing proteins, nucleic acids, molecules, metals, given the sequences polymers atomic bonded geometry modifications. Following training on structures Protein Data Bank (PDB), RFAA has comparable prediction accuracy AF2, excellent performance CAMEO for flexible backbone molecule docking, reasonable proteins multiple acid chains which, our knowledge, no existing method simultaneously. By fine-tuning diffusive denoising tasks, develop RFdiffusion (RFdiffusionAA ) , which generates binding pockets directly building around molecules. Starting from random distributions amino residues surrounding target design experimentally validate bind cardiac disease therapeutic digoxigenin, enzymatic cofactor heme, optically active bilin potential expanding range wavelengths captured photosynthesis. We anticipate RFdiffusionAA will be widely useful designing complex biomolecular systems.

Language: Английский

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AlphaFold2 structures guide prospective ligand discovery

Science, Journal Year: 2024, Volume and Issue: 384(6702)

Published: May 16, 2024

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 of the σ

Language: Английский

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How accurately can one predict drug binding modes using AlphaFold models?

eLife, Journal Year: 2023, Volume and Issue: 12

Published: Dec. 22, 2023

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal using structural models drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved prediction, with reported accuracy approaching that experimentally determined structures. To what extent do these advances translate to an ability predict more accurately how drugs and candidates bind their target proteins? Here, we carefully examine utility AF2 predicting binding poses drug-like molecules at largest class targets, G-protein-coupled receptors. We find capture pocket structures much than traditional homology models, errors nearly small differences between same different ligands bound. Strikingly, however, ligand-binding predicted computational docking is not significantly higher when lower without These results important implications all those who might use

Language: Английский

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Predicting equilibrium distributions for molecular systems with deep learning

Nature Machine Intelligence, Journal Year: 2024, Volume and Issue: 6(5), P. 558 - 567

Published: May 8, 2024

Abstract Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications not functions a single molecular but rather determined from the equilibrium distribution structures. Conventional methods obtaining these distributions, such as dynamics simulation, computationally expensive and often intractable. Here we introduce framework, called Distributional Graphormer (DiG), an attempt to predict systems. Inspired by annealing process thermodynamics, DiG uses neural networks transform simple towards distribution, conditioned on descriptor system chemical graph or protein sequence. This framework enables efficient generation diverse conformations provides estimations state densities, orders magnitude faster than conventional methods. We demonstrate several tasks, including conformation sampling, ligand catalyst–adsorbate sampling property-guided generation. presents substantial advancement methodology statistically understanding systems, opening up new research opportunities sciences.

Language: Английский

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How accurately can one predict drug binding modes using AlphaFold models?

eLife, Journal Year: 2023, Volume and Issue: 12

Published: Aug. 8, 2023

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal using structural models drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved prediction, with reported accuracy approaching that experimentally determined structures. To what extent do these advances translate to an ability predict more accurately how drugs and candidates bind their target proteins? Here, we carefully examine utility AF2 predicting binding poses drug-like molecules at largest class targets, G-protein-coupled receptors. We find capture pocket structures much than traditional homology models, errors nearly small differences between same different ligands bound. Strikingly, however, ligand-binding predicted computational docking is not significantly higher when lower without These results important implications all those who might use

Language: Английский

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Artificial intelligence for drug discovery and development in Alzheimer's disease

Current Opinion in Structural Biology, Journal Year: 2024, Volume and Issue: 85, P. 102776 - 102776

Published: Feb. 8, 2024

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics) analysis contributes to understanding precision medicine disease, including AD AD-related dementia. In this review, we summarize AI-driven methodologies for AD-agnostic development, de novo design, virtual screening, prediction drug-target interactions, all which have shown potentials. particular, AI-based repurposing emerges as a compelling strategy identify new indications existing drugs AD. We provide several emerging targets from human genetics multi-omics findings highlight recent technologies their applications in using prototypical example. closing, discuss future challenges directions other neurodegenerative diseases.

Language: Английский

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Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 176, P. 108620 - 108620

Published: May 15, 2024

Language: Английский

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AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine–associated receptor 1

Science Advances, Journal Year: 2024, Volume and Issue: 10(32)

Published: Aug. 7, 2024

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using structures generated by AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of trace amine-associated receptor 1 (TAAR1), a G protein-coupled unknown target treating neuropsychiatric disorders. Sets 30 32 highly ranked from model screens, respectively, experimentally evaluated. Of these, 25 TAAR1 agonists with potencies ranging 12 0.03 μM. The screen yielded more twofold higher hit rate (60%) discovered most potent agonists. A agonist promising selectivity profile drug-like properties showed physiological antipsychotic-like effects in wild-type but not knockout mice. These results demonstrate that can accelerate discovery.

Language: Английский

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