AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

Nucleic Acids Research, Год журнала: 2023, Номер 52(D1), С. D368 - D375

Опубликована: Ноя. 2, 2023

The AlphaFold Database Protein Structure (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled groundbreaking AlphaFold2 artificial intelligence (AI) system, predictions archived DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, a host of curated datasets. We detail access mechanisms direct file via FTP to advanced queries using Google Cloud Public Datasets programmatic endpoints database. also discuss improvements services added since its release, including Predicted Aligned Error viewer, customisation options for 3D search engine DB.

Язык: Английский

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Engineered enzymes for the synthesis of pharmaceuticals and other high-value products

Nature Synthesis, Год журнала: 2024, Номер 3(1), С. 19 - 32

Опубликована: Янв. 4, 2024

Язык: Английский

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DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

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

Опубликована: Фев. 5, 2024

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding function and facilitating drug discovery. Traditional docking methods, frequently used studying protein-ligand interactions, typically treat proteins as rigid. molecular simulations can propose appropriate conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks construct smooth energy landscape, promoting efficient different DynamicBind accurately recovers ligand-specific conformations from unbound structures without need holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance virtual screening benchmarks. Our experiments reveal accommodate wide range large conformational changes identify cryptic pockets unseen targets. As result, shows potential accelerating development small molecules previously undruggable targets expanding horizons computational

Язык: Английский

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Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development

Biomolecules, Год журнала: 2024, Номер 14(3), С. 339 - 339

Опубликована: Март 12, 2024

Recent advancements in AI-driven technologies, particularly protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on question how these technological breakthroughs, exemplified by AlphaFold2, revolutionizing our understanding function changes underlying cancer improve approaches to counter them. By enhancing precision speed at which targets identified candidates can be designed optimized, technologies streamlining entire development process. We explore use AlphaFold2 development, scrutinizing its efficacy, limitations, potential challenges. also compare with other algorithms like ESMFold, explaining diverse methodologies employed this field practical effects differences for application specific algorithms. Additionally, we discuss broader applications including prediction complex structures generative design novel proteins.

Язык: Английский

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Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Июль 2, 2024

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than predecessor 2, this innovation has astonished world with capacity speed. It takes humans years to determine various proteins how shape works receptors but 3 predicts same seconds. The version's utility is unimaginable field drug discoveries, vaccines, enzymatic processes, determining rate effect different biological processes. uses similar machine learning deep models such as Gemini (Google Limited). already established itself a turning point computational biochemistry development along receptor modulation development. With help this, researchers will gain unparalleled insights into structural dynamics their interactions, opening up avenues for scientists doctors exploit benefit patient. integration AI like 3, bolstered by rigorous validation against high-standard research publications, set catalyze further innovations offer glimpse future biomedicine.

Язык: Английский

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Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Bioorganic Chemistry, Год журнала: 2023, Номер 141, С. 106894 - 106894

Опубликована: Сен. 27, 2023

Язык: Английский

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Allosteric drugs: New principles and design approaches

Current Opinion in Structural Biology, Год журнала: 2024, Номер 84, С. 102758 - 102758

Опубликована: Янв. 2, 2024

Язык: Английский

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Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge

Journal of Molecular Biology, Год журнала: 2025, Номер unknown, С. 169050 - 169050

Опубликована: Фев. 1, 2025

Язык: Английский

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Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models

Bioorganic & Medicinal Chemistry, Год журнала: 2023, Номер 91, С. 117414 - 117414

Опубликована: Июль 12, 2023

Язык: Английский

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Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер 64(5), С. 1433 - 1455

Опубликована: Янв. 31, 2024

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that involved in the transportation diverse solute ions and small molecules into cells. There approximately 450 SLCs within human body, more than quarter them emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, diabetes. However, only 44 unique (∼9.8% SLC superfamily) with 3D structures specific binding sites have been reported. To design innovative effective drugs targeting SLCs, there number obstacles need to be overcome. computational chemistry, including physics-based molecular modeling machine learning- deep learning-based artificial intelligence (AI), provides an alternative complementary way classical drug discovery approach. Here, we present comprehensive overview on recent advances existing challenges techniques structure-based from three main aspects: (i) characterizing conformations during functional process transportation, (ii) identifying druggability especially cryptic allosteric ones substrates binding, (iii) discovering or synthetic protein binders sites. This work is expected provide guidelines understanding structure function superfamily facilitate rational novel modulators aid state-of-the-art chemistry technologies intelligence.

Язык: Английский

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