AlphaFold2 and its applications in the fields of biology and medicine

Signal Transduction and Targeted Therapy, Год журнала: 2023, Номер 8(1)

Опубликована: Март 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.

Язык: Английский

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Using AlphaFold to predict the impact of single mutations on protein stability and function

PLoS ONE, Год журнала: 2023, Номер 18(3), С. e0282689 - e0282689

Опубликована: Март 16, 2023

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that folding problem is "solved". However, more than just sequence. Presently, it unknown if AlphaFold-triggered revolution could help solve other problems related folding. Here we assay ability predict impact single mutations on stability (ΔΔG) and function. To study question extracted pLDDT metrics predictions before after mutation in a correlated predicted change with experimentally known ΔΔG values. Additionally, same using large scale dataset GFP assayed levels fluorescence. We found very weak or no correlation between output Our results imply may not be immediately applied applications

Язык: Английский

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The application of large language models in medicine: A scoping review

iScience, Год журнала: 2024, Номер 27(5), С. 109713 - 109713

Опубликована: Апрель 23, 2024

This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted drafting documents, creating training simulations, streamlining research processes. Despite their growing utility diagnosis improving doctor-patient communication, challenges persisted, including limitations contextual understanding risk over-reliance. The surge LLM-related indicated focus on patient but highlighted need for careful integration, considering validation, ethical concerns, balance with traditional practice. Future directions suggested multimodal LLMs, deeper algorithmic understanding, ensuring responsible, effective use healthcare.

Язык: Английский

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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Nature Chemical Biology, Год журнала: 2024, Номер 20(8), С. 950 - 959

Опубликована: Июнь 21, 2024

Язык: Английский

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Analysis of AlphaMissense data in different protein groups and structural context

Scientific Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 14, 2024

Abstract Single amino acid substitutions can profoundly affect protein folding, dynamics, and function. The ability to discern between benign pathogenic is pivotal for therapeutic interventions research directions. Given the limitations in experimental examination of these variants, AlphaMissense has emerged as a promising predictor pathogenicity missense variants. Since heterogenous performance on different types proteins be expected, we assessed efficacy across several groups (e.g. soluble, transmembrane, mitochondrial proteins) regions intramembrane, membrane interacting, high confidence AlphaFold segments) using ClinVar data validation. Our comprehensive evaluation showed that delivers outstanding performance, with MCC scores predominantly 0.6 0.74. We observed low disordered datasets related CFTR ABC protein. However, superior was shown when benchmarked against quality CFTR2 database. results emphasizes AlphaMissense’s potential pinpointing functional hot spots, its likely surpassing benchmarks calculated from ProteinGym datasets.

Язык: Английский

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Computational drug development for membrane protein targets

Nature Biotechnology, Год журнала: 2024, Номер 42(2), С. 229 - 242

Опубликована: Фев. 1, 2024

Язык: Английский

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Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

Pharmacology & Therapeutics, Год журнала: 2025, Номер unknown, С. 108797 - 108797

Опубликована: Янв. 1, 2025

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged fact that approximately half human proteome made up proteins do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered (IDPs), are involved numerous physiological functions and associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), type 2 diabetes. Targeting these challenging for two reasons: i) we need to preserve their functions, ii) drug design molecular docking possible due lack reliable starting conditions. Faced this challenge, solutions proposed artificial intelligence (AI) such as AlphaFold clearly unsuitable. Instead, suggest an innovative approach consisting mimicking, short synthetic peptides, conformational flexibility IDPs. which call adaptive derived from domains IDPs become structured after interacting ligand. Adaptive peptides designed aim selectively antagonizing harmful effects IDPs, without targeting them directly but through selected ligands, affecting properties. This"target target, arrow" strategy promised open new route discovery currently undruggable proteins.

Язык: Английский

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De novo design of transmembrane fluorescence-activating proteins

Nature, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

Язык: Английский

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AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function

Frontiers in Molecular Biosciences, Год журнала: 2022, Номер 9

Опубликована: Май 17, 2022

The artificial intelligence program AlphaFold 2 is revolutionizing the field of protein structure determination as it accurately predicts 3D two thirds human proteome. Its predictions can be used directly structural models or indirectly aids for experimental using X-ray crystallography, CryoEM NMR spectroscopy. Nevertheless, neither afford insight into how proteins fold, nor determine stability dynamics. Rare folds minor alternative conformations are also not predicted by and does forecast impact post translational modifications, mutations ligand binding. remaining third proteome which poorly largely corresponds to intrinsically disordered regions proteins. Key regulation signaling networks, these often form biomolecular condensates amyloids. Fortunately, limitations complemented This approach provides information on folding dynamics well amyloids their modulation conditions, small molecules, mutations, flanking sequence, interactions with other proteins, RNA virus. Together, spectroscopy collaborate advance our comprehension

Язык: Английский

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TMbed: transmembrane proteins predicted through language model embeddings

BMC Bioinformatics, Год журнала: 2022, Номер 23(1)

Опубликована: Авг. 8, 2022

Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today's top methods such as AlphaFold2 accurately predict many TMPs, but annotating regions remains a limiting step proteome-wide predictions.

Язык: Английский

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