From nature to industry: Harnessing enzymes for biocatalysis

Science, Год журнала: 2023, Номер 382(6673)

Опубликована: Ноя. 23, 2023

Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale industrial production and allows practitioners access complex organic molecules, often with fewer synthetic steps reduced waste. The last decade has seen an explosion the development of experimental computational tools tailor enzymatic properties, equipping enzyme engineers ability create biocatalysts that perform reactions not present nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate cascades, scientists can synthesize elaborate targets ranging DNA pharmaceuticals starch made vitro CO2-derived methanol. In addition, new chemistries have emerged through combination biocatalysis transition metal catalysis, photocatalysis, electrocatalysis. review highlights recent key developments, identifies current limitations, provides a future prospect for this rapidly developing technology.

Язык: Английский

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AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Nature Methods, Год журнала: 2023, Номер 21(1), С. 110 - 116

Опубликована: Ноя. 30, 2023

Abstract Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well can be expected to describe the a by comparing directly with experimental crystallographic maps. In many cases, matched maps remarkably closely. even very high-confidence differed from on global scale through distortion domain orientation, local in backbone side-chain conformation. We suggest considering exceptionally useful hypotheses. further that it is important consider confidence when interpreting carry out determination verify details, particularly those involve interactions included prediction.

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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Improved AlphaFold modeling with implicit experimental information

Nature Methods, Год журнала: 2022, Номер 19(11), С. 1376 - 1382

Опубликована: Окт. 20, 2022

Abstract Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized by implicitly including new experimental information a density map, greater portion of model could be accurately, this might synergistically improve parts the were not fully addressed either machine learning experiment alone. An iterative procedure was developed in which automatically rebuilt on basis maps used templates predictions. show improves beyond improvement obtained simple rebuilding guided data. This for modeling has been incorporated into an automated interpretation crystallographic electron cryo-microscopy maps.

Язык: Английский

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High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Март 27, 2024

Abstract This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, AI-powered method that has revolutionized biology by enabling accurate prediction structures. While 2 shown exceptional accuracy and speed, it is designed to predict proteins’ ground state limited in its ability conformational landscapes. Here, we demonstrate how can directly different subsampling multiple sequence alignments. We tested our against nuclear magnetic resonance experiments on two proteins with drastically amounts available data, Abl1 kinase granulocyte-macrophage colony-stimulating factor, predicted changes their more than 80% accuracy. Our worked best when used qualitatively effects mutations or evolution landscape well-populated states proteins. It thus offers a fast cost-effective way at even single-point mutation resolution, making useful tool pharmacology, analysis experimental results, evolution.

Язык: Английский

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Accelerating Cryptic Pocket Discovery Using AlphaFold

Journal of Chemical Theory and Computation, Год журнала: 2023, Номер 19(14), С. 4355 - 4363

Опубликована: Март 22, 2023

Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate discovery either by generating structures with open directly partially that starting points for simulations. We use generate ensembles 10 known examples, including five were deposited after AlphaFold's training data extracted from PDB. find 6 out cases samples state. For plasmepsin II, an aspartic protease causative agent malaria, only captures a partial opening. As result, ran ensemble AlphaFold-generated and show this strategy opening, even though equivalent amount launched ligand-free experimental structure fails do so. Markov state models (MSMs) constructed AlphaFold-seeded quickly yield free energy landscape opening is good agreement same generated well-tempered metadynamics. Taken together, our results demonstrate has useful role play but many may remain difficult sample using alone.

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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From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 18, 2024

The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. analysis networks obtained from proteomics experiments does not systematically provide delimitations regions. This is particular concern in case interactions mediated intrinsically disordered regions, which site generally small. Using a dataset protein-peptide complexes involving regions that are non-redundant with structures used training, we show when using full sequences proteins, AlphaFold2-Multimer only achieves 40% success rate identifying correct and structure interface. By delineating region into fragments decreasing size combining different strategies for integrating evolutionary information, manage raise this up 90%. We obtain similar rates much larger protein taken ELM database. Beyond identification site, our study also explores specificity issues. advantages limitations confidence score discriminate between alternative binding partners, task can be particularly challenging small motifs.

Язык: Английский

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AlphaFold predictions of fold-switched conformations are driven by structure memorization

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Авг. 24, 2024

Abstract Recent work suggests that AlphaFold (AF)–a deep learning-based model can accurately infer protein structure from sequence–may discern important features of folded energy landscapes, defined by the diversity and frequency different conformations in state. Here, we test limits its predictive power on fold-switching proteins, which assume two structures with regions distinct secondary and/or tertiary structure. We find (1) AF is a weak predictor fold switching (2) some successes result memorization training-set rather than learned energetics. Combining >280,000 models several implementations AF2 AF3, 35% success rate was achieved for switchers likely AF’s training sets. AF2’s confidence metrics selected against consistent experimentally determined failed to discriminate between low high conformations. Further, captured only one out seven confirmed outside sets despite extensive sampling an additional ~280,000 models. Several observations indicate has memorized structural information during training, AF3 misassigns coevolutionary restraints. These limitations constrain scope successful predictions, highlighting need physically based methods readily predict multiple

Язык: Английский

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