Software:IntFOLD

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The IntFOLD server
Developer(s)Prof Liam McGuffin

Dr Recep Adiyaman

Dr Bajuna Salehe
Stable release
IntFOLD version 5.0
Preview release
IntFOLD version 6.0
Written inJava,

Python,

R
Websitehttps://www.reading.ac.uk/bioinf/IntFOLD/

IntFOLD (Integrated Fold Recognition) is fully automated, integrated pipeline for prediction of 3D structure and function from amino acid sequences.[1] The pipeline is wrapped up and deployed as a publicly-available Web Server.[2] The core of the server method is quality assessment using built-in accuracy self-estimates (ASE) which improves performance prediction of 3D model using ModFOLD.[3]

Description

IntFOLD server provides the tertiary structure prediction at a competitive accuracy and combines the cutting edge methods including IntFOLD-TS for generation of 3D models,[1] ModFOLD for 3D model quality estimation,[3] ReFOLD for refinement of 3D models,[4] DisoCLUST for disorder prediction,[5] DomFOLD for structural domain prediction,[6] and FunFOLD for protein ligand binding site prediction.[7] The integration of the tools enables users to reach all related information in a pipeline. IntFOLD Web Server has completed over 200,000 structure predictions since January 2010.[1]

The only required input is a protein sequence for the prediction of the protein 3D structure and function.[1] The IntFOLD output is presented via a user-friendly interface for the use of life scientists. The raw data is also formatted in Critical Assessment of Methods for Protein Structure Prediction (CASP) standards with a detailed help page.[1]

Performance in CASP and CAMEO experiments

The IntFOLD method was firstly benchmarked in Critical Assessment of Techniques for Protein Structure Prediction 9 (CASP9) and ranked among the top 5.[8] The IntFOLD server has consolidated its performance in the following CASP experiments [1]

Its performance is being continually evaluated in Continuous Automated Model Evaluation (CAMEO) experiment.[9]

Applications of IntFOLD server

Public health

IntFOLD was used to generate 3D models of the SARS-CoV-2 targets for the CASP Commons COVID-19 initiative[10] and elsewhere [11] accelerating the race of vaccines and other therapeutics development with regard to COVID-19 pandemic. In other aspect of chronic diseases, IntFOLD was used to model HEV PCP, an essential protein of Hepatitis E virus causing Hepatitis E disease.[12] Additionally, IntFOLD was used to model disordered region of the Bovine milk αS2-casein proteins which were implicated in the formation amyloidogenic fibrils some of which are known to be major causes of neurodegenerative diseases.[13]

Food Security

IntFOLD has been used in different aspects of food security. For instance, it has been used to model effector proteins molecules that causes fungus in Barley.[14] Furthermore, it has been applied in modelling several proteins involved in the functioning of key systems in Atlantic salmon, and HaACBP1 protein, which is vital for development and growth of sunflower, a key crop plant used for production of widely used cooking oil.[15][16] IntFOLD was used to model Chitin proteins in Podosphaera xanthii, a causal agent of fungal disease called cucurbit powdery mildew, which hamper crop productivity.[17]

Contribution to Protein Structure Prediction Methods Development

IntFOLD has been used as one of the standard server-based methods in validating the performance of some of the newer methods used in prediction of the 3D-protein models. This is important in advancing the structural bioinformatics field.[18]

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 1.5 McGuffin, Liam J; Adiyaman, Recep; Maghrabi, Ali H A; Shuid, Ahmad N; Brackenridge, Danielle A; Nealon, John O; Philomina, Limcy S (2019-05-02). "IntFOLD: an integrated web resource for high performance protein structure and function prediction". Nucleic Acids Research 47 (W1): W408–W413. doi:10.1093/nar/gkz322. ISSN 0305-1048. PMID 31045208. 
  2. ↑ McGuffin, Liam J.; Adiyaman, Recep; Maghrabi, Ali H A.; Shuid, Ahmad N.; Brackenridge, Danielle A.; Nealon, John O.; Philomina, Limcy S. (2019). "IntFOLD: An integrated web resource for high performance protein structure and function prediction". Nucleic Acids Research 47 (W1): W408–W413. doi:10.1093/nar/gkz322. PMID 31045208. PMC 6602432. https://academic.oup.com/nar/article/47/W1/W408/5482507. Retrieved 2024-11-12. 
  3. ↑ 3.0 3.1 Maghrabi, Ali H. A.; McGuffin, Liam J. (2017-04-29). "ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models". Nucleic Acids Research 45 (W1): W416–W421. doi:10.1093/nar/gkx332. ISSN 0305-1048. PMID 28460136. 
  4. ↑ Adiyaman, Recep; McGuffin, Liam J (2021-05-01). "ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts". Nucleic Acids Research 49 (W1): W589–W596. doi:10.1093/nar/gkab300. ISSN 0305-1048. PMID 34009387. 
  5. ↑ Atkins, Jennifer; Boateng, Samuel; Sorensen, Thomas; McGuffin, Liam (2015-08-13). "Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies". International Journal of Molecular Sciences 16 (8): 19040–19054. doi:10.3390/ijms160819040. ISSN 1422-0067. PMID 26287166. 
  6. ↑ McGuffin, Liam J.; Atkins, Jennifer D.; Salehe, Bajuna R.; Shuid, Ahmad N.; Roche, Daniel B. (2015-03-27). "IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences: Figure 1.". Nucleic Acids Research 43 (W1): W169–W173. doi:10.1093/nar/gkv236. ISSN 0305-1048. PMID 25820431. 
  7. ↑ Roche, Daniel B.; Buenavista, Maria T.; McGuffin, Liam J. (2013-06-11). "The FunFOLD2 server for the prediction of protein–ligand interactions". Nucleic Acids Research 41 (W1): W303–W307. doi:10.1093/nar/gkt498. ISSN 1362-4962. PMID 23761453. 
  8. ↑ Roche, D. B.; Buenavista, M. T.; Tetchner, S. J.; McGuffin, L. J. (2011-03-31). "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 Research 39 (suppl): W171–W176. doi:10.1093/nar/gkr184. ISSN 0305-1048. PMID 21459847. 
  9. ↑ Haas, Jürgen; Barbato, Alessandro; Behringer, Dario; Studer, Gabriel; Roth, Steven; Bertoni, Martino; Mostaguir, Khaled; Gumienny, Rafal et al. (2017-12-17). "Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12". Proteins: Structure, Function, and Bioinformatics 86 (Suppl 1): 387–398. doi:10.1002/prot.25431. ISSN 0887-3585. PMID 29178137. 
  10. ↑ Kryshtafovych, Andriy; Moult, John; Billings, Wendy M.; Corte, Dennis Della; Fidelis, Krzysztof; Kwon, Sohee; Olechnovič, Kliment; Seok, Chaok et al. (2021). "Modeling SARS-CoV2 proteins in the CASP-commons experiment" (in en). Proteins: Structure, Function, and Bioinformatics 89 (12): 1987–1996. doi:10.1002/prot.26231. ISSN 1097-0134. PMID 34462960. 
  11. ↑ Sadat, Seyed Mehdi; Aghadadeghi, Mohammad Reza; Yousefi, Masoume; Khodaei, Arezoo; Sadat Larijani, Mona; Bahramali, Golnaz (2021-05-01). "Bioinformatics Analysis of SARS-CoV-2 to Approach an Effective Vaccine Candidate Against COVID-19" (in en). Molecular Biotechnology 63 (5): 389–409. doi:10.1007/s12033-021-00303-0. ISSN 1559-0305. PMID 33625681. 
  12. ↑ Saraswat, Shweta; Chaudhary, Meenakshi; Sehgal, Deepak (2020). "Hepatitis E Virus Cysteine Protease Has Papain Like Properties Validated by in silico Modeling and Cell-Free Inhibition Assays". Frontiers in Cellular and Infection Microbiology 9: 478. doi:10.3389/fcimb.2019.00478. ISSN 2235-2988. PMID 32039053. 
  13. ↑ Thorn, David C.; Bahraminejad, Elmira; Grosas, Aidan B.; Koudelka, Tomas; Hoffmann, Peter; Mata, Jitendra P.; Devlin, Glyn L.; Sunde, Margaret et al. (2021-03-01). "Native disulphide-linked dimers facilitate amyloid fibril formation by bovine milk αS2-casein" (in en). Biophysical Chemistry 270. doi:10.1016/j.bpc.2020.106530. ISSN 0301-4622. PMID 33545456. https://www.sciencedirect.com/science/article/pii/S0301462220302386. 
  14. ↑ Bauer, Saskia; Yu, Dongli; Lawson, Aaron W.; Saur, Isabel M. L.; Frantzeskakis, Lamprinos; Kracher, Barbara; Logemann, Elke; Chai, Jijie et al. (2021-02-03). "The leucine-rich repeats in allelic barley MLA immune receptors define specificity towards sequence-unrelated powdery mildew avirulence effectors with a predicted common RNase-like fold" (in en). PLOS Pathogens 17 (2). doi:10.1371/journal.ppat.1009223. ISSN 1553-7374. PMID 33534797. 
  15. ↑ Aznar-Moreno, Jose A.; Venegas-Calerón, Mónica; Du, Zhi-Yan; Garcés, Rafael; Tanner, Julian A.; Chye, Mee-Len; Martínez-Force, Enrique; Salas, Joaquín J. (2020-11-01). "Characterization and function of a sunflower (Helianthus annuus L.) Class II acyl-CoA-binding protein" (in en). Plant Science 300. doi:10.1016/j.plantsci.2020.110630. ISSN 0168-9452. PMID 33180709. Bibcode: 2020PlnSc.30010630A. https://www.sciencedirect.com/science/article/pii/S0168945220302363. 
  16. ↑ Kalananthan, Tharmini; Lai, Floriana; Gomes, Ana S.; Murashita, Koji; Handeland, Sigurd; Rønnestad, Ivar (2020). "The Melanocortin System in Atlantic Salmon (Salmo salar L.) and Its Role in Appetite Control". Frontiers in Neuroanatomy 14: 48. doi:10.3389/fnana.2020.00048. ISSN 1662-5129. PMID 32973463. 
  17. ↑ Polonio, Álvaro; Fernández-Ortuño, Dolores; Vicente, Antonio de; Pérez-García, Alejandro (2021). "A haustorial-expressed lytic polysaccharide monooxygenase from the cucurbit powdery mildew pathogen Podosphaera xanthii contributes to the suppression of chitin-triggered immunity" (in en). Molecular Plant Pathology 22 (5): 580–601. doi:10.1111/mpp.13045. ISSN 1364-3703. PMID 33742545. 
  18. ↑ Su, Hong; Wang, Wenkai; Du, Zongyang; Peng, Zhenling; Gao, Shang-Hua; Cheng, Ming-Ming; Yang, Jianyi (2021). "Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates" (in en). Advanced Science 8 (24). doi:10.1002/advs.202102592. ISSN 2198-3844. PMID 34719864. 



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