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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BenchmarkingAlphaFoldfor protein complex modeling reveals accuracy determinants

Protein Science, Год журнала: 2022, Номер 31(8)

Опубликована: Июль 13, 2022

High-resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due the massive number and limitations there is a need for computational methods that can accurately model their structures. Here we explore use recently developed deep learning method, AlphaFold, predict structures protein complexes from sequence. With benchmark 152 diverse heterodimeric complexes, multiple implementations parameters AlphaFold were tested accuracy. Remarkably, many cases (43%) had near-native models (medium or high critical assessment predicted accuracy) generated as top-ranked predictions by greatly surpassing performance unbound docking (9% success rate models), however modeling antibody-antigen within our set was unsuccessful. We identified sequence features associated with lack success, also investigated impact alignment input. Benchmarking multimer-optimized version (AlphaFold-Multimer) released confirmed low (11% success), found T cell receptor-antigen are likewise not modeled algorithm, showing adaptive immune recognition poses challenge current algorithm model. Overall, study demonstrates end-to-end transient highlights areas improvement future developments reliably any interaction interest.

Язык: Английский

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Before and after AlphaFold2: An overview of protein structure prediction

Frontiers in Bioinformatics, Год журнала: 2023, Номер 3

Опубликована: Фев. 28, 2023

Three-dimensional protein structure is directly correlated with its function and determination critical to understanding biological processes addressing human health life science problems in general. Although new structures are experimentally obtained over time, there still a large difference between the number of sequences placed Uniprot those resolved tertiary structure. In this context, studies have emerged predict by methods based on template or free modeling. last years, different been combined overcome their individual limitations, until emergence AlphaFold2, which demonstrated that predicting high accuracy at unprecedented scale possible. Despite current impact field, AlphaFold2 has limitations. Recently, language models promised revolutionize structural biology allowing discovery only from evolutionary patterns present sequence. Even though these do not reach accuracy, they already covered some being able more than 200 million proteins metagenomic databases. mini-review, we provide an overview breakthroughs prediction before after emergence.

Язык: Английский

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Structure-based prediction of T cell receptor:peptide-MHC interactions

eLife, Год журнала: 2023, Номер 12

Опубликована: Янв. 20, 2023

The regulatory and effector functions of T cells are initiated by the binding their cell-surface cell receptor (TCR) to peptides presented major histocompatibility complex (MHC) proteins on other cells. specificity TCR:peptide-MHC interactions, thus, underlies nearly all adaptive immune responses. Despite intense interest, generalizable predictive models remain out reach; two key barriers diversity TCR recognition modes paucity training data. Inspired recent breakthroughs in protein structure prediction achieved deep neural networks, we evaluated structural modeling as a potential avenue for epitope specificity. We show that specialized version network predictor AlphaFold can generate interactions be used discriminate correct from incorrect peptide epitopes with substantial accuracy. Although much work remains done these predictions have widespread practical utility, optimistic learning-based represents path interaction

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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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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Critical assessment of methods of protein structure prediction (CASP)—Round XV

Proteins Structure Function and Bioinformatics, Год журнала: 2023, Номер 91(12), С. 1539 - 1549

Опубликована: Ноя. 2, 2023

Abstract Computing protein structure from amino acid sequence information has been a long‐standing grand challenge. Critical assessment of prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation deep learning methods delivering accuracy comparable for many single proteins. There is an expectation that these will have much wider application in computational structural biology. Here we summarize results most recent experiment, CASP15, 2022, emphasis on new learning‐driven progress. Other papers special issue proteins provide more detailed analysis. For structures, AlphaFold2 method still superior other approaches, but there two points note. First, although was core all successful methods, wide variety implementation combination methods. Second, using standard protocol default parameters only produces highest quality result about thirds targets, extensive sampling required others. advance CASP enormous increase computed complexes, achieved by use overall do not fully match performance too, based perform best, again than defaults often required. Also note encouraging early compute ensembles macromolecular structures. Critically usability both derived estimates local global high quality, however interface regions slightly less reliable. CASP15 also included computation RNA structures first time. Here, classical approaches produced better agreement ones, limited. Also, time, protein–ligand area interest drug design. were ones. Many discussed conference, it clear continue advance.

Язык: Английский

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AlphaFold2 structures guide prospective ligand discovery

Science, Год журнала: 2024, Номер 384(6702)

Опубликована: Май 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

Язык: Английский

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Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy

Protein Science, Год журнала: 2023, Номер 33(1)

Опубликована: Дек. 11, 2023

Abstract High resolution antibody–antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination the diversity repertoire underscore necessity accurate computational tools for modeling complexes. Initial benchmarking showed that despite overall success in protein–protein complexes, AlphaFold AlphaFold‐Multimer have limited interactions. In this study, we performed a thorough analysis AlphaFold's performance on 427 nonredundant complex structures, identifying useful confidence metrics predicting model quality, features complexes associated with improved success. Notably, found latest version improves near‐native to over 30%, versus approximately 20% previous version, while increased sampling gives 50% With success, generate models many cases, additional training or other optimization may further improve performance.

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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