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

Andriy Kryshtafovych,

Torsten Schwede, Maya Topf

и другие.

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

ColabFold: making protein folding accessible to all DOI Creative Commons
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki

и другие.

Nature Methods, Год журнала: 2022, Номер 19(6), С. 679 - 682

Опубликована: Май 30, 2022

Abstract ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster optimized model utilization enables close to 1,000 per day on a server one graphics processing unit. Coupled Google Colaboratory, becomes free accessible platform for folding. is open-source software available at https://github.com/sokrypton/ColabFold its novel environmental databases are https://colabfold.mmseqs.com .

Язык: Английский

Процитировано

6879
Evolutionary-scale prediction of atomic-level protein structure with a language model DOI Creative Commons
Zeming Lin, Halil Akin, Roshan Rao

и другие.

Science, Год журнала: 2023, Номер 379(6637), С. 1123 - 1130

Опубликована: Март 16, 2023

Recent advances in machine learning have leveraged evolutionary information multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level structure from primary using a large language model. As models sequences are scaled up 15 billion parameters, an atomic-resolution picture emerges the learned representations. This results order-of-magnitude acceleration high-resolution prediction, which enables large-scale structural characterization metagenomic proteins. apply this capability construct ESM Metagenomic Atlas by predicting structures for >617 million sequences, including >225 that predicted with high confidence, gives view into vast breadth and diversity natural

Язык: Английский

Процитировано

2225
Harnessing protein folding neural networks for peptide–protein docking DOI Creative Commons
Tomer Tsaban, Julia K. Varga, Orly Avraham

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Янв. 10, 2022

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology beyond. Here, we show that, although these learning approaches originally been developed for the in silico folding of monomers, also enables quick modeling peptide-protein interactions. Our simple implementation generates complex models without requiring multiple sequence alignment information peptide partner, can handle binding-induced conformational changes receptor. We explore what has memorized learned, describe specific examples that highlight differences compared to state-of-the-art docking protocol PIPER-FlexPepDock. These results holds great promise providing insight into a wide range complexes, serving starting point detailed characterization manipulation

Язык: Английский

Процитировано

909
AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences DOI Creative Commons
Mihály Váradi,

Damian Bertoni,

Paulyna Magaña

и другие.

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

Процитировано

647
ColabFold - Making protein folding accessible to all DOI Creative Commons
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2021, Номер unknown

Опубликована: Авг. 15, 2021

ColabFold offers accelerated protein structure and complex predictions by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40 - 60× faster optimized model use allows predicting close to a thousand structures per day on server one GPU. Coupled Google Colaboratory, becomes free accessible platform for folding. is open-source software available at github.com/sokrypton/ColabFold . Its novel environmental databases are colabfold.mmseqs.com Contact [email protected] , [email protected] [email protected]

Язык: Английский

Процитировано

555
Applying and improving AlphaFold at CASP14 DOI
John Jumper, K Taki, Alexander Pritzel

и другие.

Proteins Structure Function and Bioinformatics, Год журнала: 2021, Номер 89(12), С. 1711 - 1721

Опубликована: Окт. 4, 2021

We describe the operation and improvement of AlphaFold, system that was entered by team AlphaFold2 to "human" category in 14th Critical Assessment Protein Structure Prediction (CASP14). The AlphaFold CASP14 is entirely different one CASP13. It used a novel end-to-end deep neural network trained produce protein structures from amino acid sequence, multiple sequence alignments, homologous proteins. In assessors' ranking summed z scores (>2.0), scored 244.0 compared 90.8 next best group. predictions made had median domain GDT_TS 92.4; this first time level average accuracy has been achieved during CASP, especially on more difficult Free Modeling targets, represents significant state art structure prediction. reported how run as human improved such it now achieves an equivalent performance without intervention, opening door highly accurate large-scale

Язык: Английский

Процитировано

338
I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction DOI
Xiaogen Zhou, Wei Zheng, Yang Li

и другие.

Nature Protocols, Год журнала: 2022, Номер 17(10), С. 2326 - 2353

Опубликована: Авг. 5, 2022

Язык: Английский

Процитировано

336
High‐accuracy protein structure prediction in CASP14 DOI Creative Commons
Joana Pereira, Adam J. Simpkin, M.D. Hartmann

и другие.

Proteins Structure Function and Bioinformatics, Год журнала: 2021, Номер 89(12), С. 1687 - 1699

Опубликована: Июль 4, 2021

The application of state-of-the-art deep-learning approaches to the protein modeling problem has expanded "high-accuracy" category in CASP14 encompass all targets. Building on metrics used for high-accuracy assessment previous CASPs, we evaluated performance groups that submitted models at least 10 targets across difficulty classes, and judged usefulness those produced by AlphaFold2 (AF2) as molecular replacement search with AMPLE. Driven qualitative diversity CASP, also introduce DipDiff a new measure improvement backbone geometry provided model versus available templates. Although large leap is seen due AF2, second-best method out-performed best CASP13, illustrating role community-based benchmarking development evolution structure prediction field.

Язык: Английский

Процитировано

296
High-resolutionde novostructure prediction from primary sequence DOI Creative Commons
Ruidong Wu,

Fan Ding,

Rui Wang

и другие.

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

Опубликована: Июль 22, 2022

Abstract Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan or fast-evolving like antibodies, and a typically folds natural setting from its primary amino acid into three-dimensional structure, suggesting that should be necessary protein’s folded form. Here, we introduce OmegaFold, the first computational method successfully high-resolution structure single alone. Using new combination language model allows us make predictions sequences geometry-inspired transformer trained on structures, OmegaFold outperforms RoseTTAFold achieves similar prediction accuracy AlphaFold2 recently released enables accurate do belong any functionally characterized family antibodies tend noisy due fast evolution. Our study fills much-encountered gap brings step closer understanding folding nature.

Язык: Английский

Процитировано

284
AlphaFold2 and its applications in the fields of biology and medicine DOI Creative Commons
Zhenyu Yang, Xiaoxi Zeng, Yi Zhao

и другие.

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

Процитировано

272