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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Prediction of multiple conformational states by combining sequence clustering with AlphaFold2

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Окт. 17, 2022

Abstract AlphaFold2 (AF2) has revolutionized structural biology by accurately predicting single structures of proteins and protein-protein complexes. However, biological function is rooted in a protein’s ability to sample different conformational substates, disease-causing point mutations are often due population changes these substates. This sparked immense interest expanding AF2’s capability predict We demonstrate that clustering an input multiple sequence alignment (MSA) similarity enables AF2 alternate states known metamorphic proteins, including the circadian rhythm protein KaiB, transcription factor RfaH, spindle checkpoint Mad2, score with high confidence. Moreover, we use identify minimal set two predicted switch KaiB between its states. Finally, used our method, AF-cluster, screen for families without fold-switching, identified putative state oxidoreductase DsbE. Similarly DsbE thioredoxin-like fold novel fold. prediction subject future experimental testing. Further development such bioinformatic methods tandem experiments will likely have profound impact on energy landscapes, essential shedding light into function.

Язык: Английский

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AlphaFold2-RAVE: From Sequence to Boltzmann Ranking

Journal of Chemical Theory and Computation, Год журнала: 2023, Номер 19(14), С. 4351 - 4354

Опубликована: Май 12, 2023

While AlphaFold2 is rapidly being adopted as a new standard in protein structure predictions, it limited to single structures. This can be insufficient for the inherently dynamic world of biomolecules. In this Letter, we propose AlphaFold2-RAVE, an efficient protocol obtaining Boltzmann-ranked ensembles from sequence. The method uses structural outputs initializations artificial intelligence-augmented molecular dynamics. We release open-source code and demonstrate results on different proteins.

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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DisProt in 2024: improving function annotation of intrinsically disordered proteins

Nucleic Acids Research, Год журнала: 2023, Номер 52(D1), С. D434 - D441

Опубликована: Окт. 30, 2023

Abstract DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of brings significant advancements, including a broader representation functions an enhanced curation process. These improvements aim to increase both quality annotations coverage at sequence level. Higher has been achieved by adopting additional evidence codes. Quality improved systematically applying Minimum Information About Disorder Experiments (MIADE) principles reporting all details experimental setup that could potentially influence structural state protein. now includes new thematic datasets expanded adoption Gene Ontology terms, resulting in extensive functional repertoire which automatically propagated UniProtKB. Finally, we show DisProt's curated strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights utility explaining apparent uncertainty certain well-defined predicted structures, often correspond folding-upon-binding fragments. Overall, serves as comprehensive resource, combining enhance our understanding implications.

Язык: Английский

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AlphaFold2 protein structure prediction: Implications for drug discovery

Current Opinion in Structural Biology, Год журнала: 2023, Номер 78, С. 102526 - 102526

Опубликована: Янв. 6, 2023

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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State-specific protein–ligand complex structure prediction with a multiscale deep generative model

Nature Machine Intelligence, Год журнала: 2024, Номер 6(2), С. 195 - 208

Опубликована: Фев. 12, 2024

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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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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