Prediction of Protein Structures, Functions and Interactions Using the IntFOLD7, MultiFOLD and ModFOLDdock Servers

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2023 Apr 27

PMID 37102670

Authors

Liam J McGuffin

Nicholas S Edmunds

Ahmet G Genc

Shuaa M A Alharbi

Bajuna R Salehe

Recep Adiyaman

Affiliations

Soon will be listed here.

Abstract

The IntFOLD server based at the University of Reading has been a leading method over the past decade in providing free access to accurate prediction of protein structures and functions. In a post-AlphaFold2 world, accurate models of tertiary structures are widely available for even more protein targets, so there has been a refocus in the prediction community towards the accurate modelling of protein-ligand interactions as well as modelling quaternary structure assemblies. In this paper, we describe the latest improvements to IntFOLD, which maintains its competitive structure prediction performance by including the latest deep learning methods while also integrating accurate model quality estimates and 3D models of protein-ligand interactions. Furthermore, we also introduce our two new server methods: MultiFOLD for accurately modelling both tertiary and quaternary structures, with performance which has been independently verified to outperform the standard AlphaFold2 methods, and ModFOLDdock, which provides world-leading quality estimates for quaternary structure models. The IntFOLD7, MultiFOLD and ModFOLDdock servers are available at: https://www.reading.ac.uk/bioinf/.

Citing Articles

TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

Han B, Zhang Y, Li L, Gong X, Xia K Brief Bioinform. 2025; 26(2).

PMID: 40062613 PMC: 11891663. DOI: 10.1093/bib/bbaf083.

Immunoinformatic design of a multivalent vaccine against and its evaluation in a murine model using a DNA prime-protein boost strategy.

Molina R, Osorio A, Flores-Concha M, Gomez L, Alvarado I, Ferrari I Front Immunol. 2024; 15:1456078.

PMID: 39640259 PMC: 11617539. DOI: 10.3389/fimmu.2024.1456078.

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Genc A, McGuffin L Methods Mol Biol. 2024; 2867:121-139.

PMID: 39576578 DOI: 10.1007/978-1-0716-4196-5_7.

Unraveling antibiotic resistance in IA strain: genomic insights, structural analysis, and prospects for targeted therapeutics.

Al-Asadi S, Abdul Wahhab B, Bootwala J, Alwatar W, Al-Kahachi R Microbiol Spectr. 2024; :e0392623.

PMID: 39472000 PMC: 11619425. DOI: 10.1128/spectrum.03926-23.

Putative MutS2 Homologs in Algae: More Goods in Shopping Bag?.

Berdieva M, Kalinina V, Palii O, Skarlato S J Mol Evol. 2024; 92(6):815-833.

PMID: 39365456 DOI: 10.1007/s00239-024-10210-y.

References

McGuffin L, Roche D . Automated tertiary structure prediction with accurate local model quality assessment using the IntFOLD-TS method. Proteins. 2011; 79 Suppl 10:137-46. DOI: 10.1002/prot.23120. View

Robin X, Haas J, Gumienny R, Smolinski A, Tauriello G, Schwede T . Continuous Automated Model EvaluatiOn (CAMEO)-Perspectives on the future of fully automated evaluation of structure prediction methods. Proteins. 2021; 89(12):1977-1986. PMC: 8673552. DOI: 10.1002/prot.26213. View

Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T . Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Sci Rep. 2017; 7(1):10480. PMC: 5585393. DOI: 10.1038/s41598-017-09654-8. View

Elofsson A, Joo K, Keasar C, Lee J, Maghrabi A, Manavalan B . Methods for estimation of model accuracy in CASP12. Proteins. 2017; 86 Suppl 1:361-373. DOI: 10.1002/prot.25395. View

McGuffin L, Aldowsari F, Alharbi S, Adiyaman R . ModFOLD8: accurate global and local quality estimates for 3D protein models. Nucleic Acids Res. 2021; 49(W1):W425-W430. PMC: 8218196. DOI: 10.1093/nar/gkab321. View

Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J . Critical assessment of methods of protein structure prediction (CASP)-Round XIV. Proteins. 2021; 89(12):1607-1617. PMC: 8726744. DOI: 10.1002/prot.26237. View

Biasini M, Schmidt T, Bienert S, Mariani V, Studer G, Haas J . OpenStructure: an integrated software framework for computational structural biology. Acta Crystallogr D Biol Crystallogr. 2013; 69(Pt 5):701-9. PMC: 3640466. DOI: 10.1107/S0907444913007051. View

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O . Highly accurate protein structure prediction with AlphaFold. Nature. 2021; 596(7873):583-589. PMC: 8371605. DOI: 10.1038/s41586-021-03819-2. View

Roche D, Buenavista M, Tetchner S, McGuffin L . The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Res. 2011; 39(Web Server issue):W171-6. PMC: 3125722. DOI: 10.1093/nar/gkr184. View

10.

Hiranuma N, Park H, Baek M, Anishchenko I, Dauparas J, Baker D . Improved protein structure refinement guided by deep learning based accuracy estimation. Nat Commun. 2021; 12(1):1340. PMC: 7910447. DOI: 10.1038/s41467-021-21511-x. View

11.

McGuffin L, Adiyaman R, Maghrabi A, Shuid A, Brackenridge D, Nealon J . IntFOLD: an integrated web resource for high performance protein structure and function prediction. Nucleic Acids Res. 2019; 47(W1):W408-W413. PMC: 6602432. DOI: 10.1093/nar/gkz322. View

12.

McGuffin L . Intrinsic disorder prediction from the analysis of multiple protein fold recognition models. Bioinformatics. 2008; 24(16):1798-804. DOI: 10.1093/bioinformatics/btn326. View

13.

McGuffin L, Shuid A, Kempster R, Maghrabi A, Nealon J, Salehe B . Accurate template-based modeling in CASP12 using the IntFOLD4-TS, ModFOLD6, and ReFOLD methods. Proteins. 2017; 86 Suppl 1:335-344. DOI: 10.1002/prot.25360. View

14.

Anishchenko I, Baek M, Park H, Hiranuma N, Kim D, Dauparas J . Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14. Proteins. 2021; 89(12):1722-1733. PMC: 8616808. DOI: 10.1002/prot.26194. View

15.

Mariani V, Biasini M, Barbato A, Schwede T . lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics. 2013; 29(21):2722-8. PMC: 3799472. DOI: 10.1093/bioinformatics/btt473. View

16.

Adiyaman R, McGuffin L . ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts. Nucleic Acids Res. 2021; 49(W1):W589-W596. PMC: 8218204. DOI: 10.1093/nar/gkab300. View

17.

Roche D, Buenavista M, McGuffin L . The FunFOLD2 server for the prediction of protein-ligand interactions. Nucleic Acids Res. 2013; 41(Web Server issue):W303-7. PMC: 3692132. DOI: 10.1093/nar/gkt498. View

18.

Mirdita M, Schutze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M . ColabFold: making protein folding accessible to all. Nat Methods. 2022; 19(6):679-682. PMC: 9184281. DOI: 10.1038/s41592-022-01488-1. View

19.

Basu S, Wallner B . DockQ: A Quality Measure for Protein-Protein Docking Models. PLoS One. 2016; 11(8):e0161879. PMC: 4999177. DOI: 10.1371/journal.pone.0161879. View

20.

Olechnovic K, Venclovas C . Voronota: A fast and reliable tool for computing the vertices of the Voronoi diagram of atomic balls. J Comput Chem. 2014; 35(8):672-81. DOI: 10.1002/jcc.23538. View