IntFOLD

Last updated
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
Website https://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]

Contents

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]

Related Research Articles

<span class="mw-page-title-main">CASP</span> Protein structure prediction challenge

Critical Assessment of Structure Prediction (CASP), sometimes called Critical Assessment of Protein Structure Prediction, is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994. CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence many view the experiment more as a "world championship" in this field of science. More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions.

InterPro is a database of protein families, protein domains and functional sites in which identifiable features found in known proteins can be applied to new protein sequences in order to functionally characterise them.

Nucleic acid structure prediction is a computational method to determine secondary and tertiary nucleic acid structure from its sequence. Secondary structure can be predicted from one or several nucleic acid sequences. Tertiary structure can be predicted from the sequence, or by comparative modeling.

This is a list of notable computer programs that are used for nucleic acids simulations.

SUPERFAMILY is a database and search platform of structural and functional annotation for all proteins and genomes. It classifies amino acid sequences into known structural domains, especially into SCOP superfamilies. Domains are functional, structural, and evolutionary units that form proteins. Domains of common Ancestry are grouped into superfamilies. The domains and domain superfamilies are defined and described in SCOP. Superfamilies are groups of proteins which have structural evidence to support a common evolutionary ancestor but may not have detectable sequence homology.

Phyre and Phyre2 are free web-based services for protein structure prediction. Phyre is among the most popular methods for protein structure prediction having been cited over 1,500 times. Like other remote homology recognition techniques, it is able to regularly generate reliable protein models when other widely used methods such as PSI-BLAST cannot. Phyre2 has been designed to ensure a user-friendly interface for users inexpert in protein structure prediction methods. Its development is funded by the Biotechnology and Biological Sciences Research Council.

<span class="mw-page-title-main">Knotted protein</span> Proteins with backbone entangled in a knot

Knotted proteins are proteins whose backbones entangle themselves in a knot. One can imagine pulling a protein chain from both termini, as though pulling a string from both ends. When a knotted protein is “pulled” from both termini, it does not get disentangled. Knotted proteins are very rare, making up only about one percent of the proteins in the Protein Data Bank, and their folding mechanisms and function are not well understood. Although there are experimental and theoretical studies that hint to some answers, systematic answers to these questions have not yet been found.

<span class="mw-page-title-main">David T. Jones (biochemist)</span> British bioinformatician

David Tudor Jones is a Professor of Bioinformatics, and Head of Bioinformatics Group in the University College London. He is also the director in Bloomsbury Center for Bioinformatics, which is a joint Research Centre between UCL and Birkbeck, University of London and which also provides bioinformatics training and support services to biomedical researchers. In 2013, he is a member of editorial boards for PLoS ONE, BioData Mining, Advanced Bioinformatics, Chemical Biology & Drug Design, and Protein: Structure, Function and Bioinformatics.

Swiss-model is a structural bioinformatics web-server dedicated to homology modeling of 3D protein structures. As of 2024, homology modeling is the most accurate method to generate reliable three-dimensional protein structure models and is routinely used in many practical applications. Homology modelling methods make use of experimental protein structures (templates) to build models for evolutionary related proteins (targets).

<span class="mw-page-title-main">Protein fold class</span> Categories of protein tertiary structure

In molecular biology, protein fold classes are broad categories of protein tertiary structure topology. They describe groups of proteins that share similar amino acid and secondary structure proportions. Each class contains multiple, independent protein superfamilies.

Computer Atlas of Surface Topography of Proteins (CASTp) aims to provide comprehensive and detailed quantitative characterization of topographic features of protein, is now updated to version 3.0. Since its release in 2006, the CASTp server has ≈45000 visits and fulfills ≈33000 calculation requests annually. CASTp has been proven as a confident tool for a wide range of researches, including investigations of signaling receptors, discoveries of cancer therapeutics, understanding of mechanism of drug actions, studies of immune disorder diseases, analysis of protein–nanoparticle interactions, inference of protein functions and development of high-throughput computational tools. This server is maintained by Jie Liang's lab in University of Illinois at Chicago.

<span class="mw-page-title-main">I-TASSER</span>

I-TASSER is a bioinformatics method for predicting three-dimensional structure model of protein molecules from amino acid sequences. It detects structure templates from the Protein Data Bank by a technique called fold recognition. The full-length structure models are constructed by reassembling structural fragments from threading templates using replica exchange Monte Carlo simulations. I-TASSER is one of the most successful protein structure prediction methods in the community-wide CASP experiments.

The ViennaRNA Package is software, a set of standalone programs and libraries used for predicting and analysing RNA nucleic acid secondary structures. The source code for the package is released as free and open-source software and compiled binaries are available for the operating systems Linux, macOS, and Windows. The original paper has been cited over 2,000 times.

Rita Casadio is an Adjunct Professor of Biochemistry/Biophysics in the Department of Pharmacy and Biotechnology at the University of Bologna.

<span class="mw-page-title-main">David S. Wishart</span> Canadian bioinformatician (born 1961)

[[

Computational methods that use protein sequence and/ or protein structure to predict protein aggregation. The table below, shows the main features of software for prediction of protein aggregation

<span class="mw-page-title-main">Predicted Aligned Error</span> Predicted output by AlphaFold indicating expected position error of protein structures

The Predicted Aligned Error (PAE) is a quantitative output produced by AlphaFold, a protein structure prediction system developed by DeepMind. PAE estimates the expected positional error for each residue in a predicted protein structure if it were aligned to a corresponding residue in the true protein structure. This measurement helps scientists assess the confidence in the relative positions and orientations of different parts of the predicted protein model.

References

  1. 1 2 3 4 5 6 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. PMC   6602432 . 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. PMC   6602432 . PMID   31045208 . Retrieved 2024-11-12.
  3. 1 2 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. PMC   5570241 . 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. PMC   8218204 . 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. PMC   4581285 . 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. PMC   4489238 . 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. PMC   3692132 . 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. PMC   3125722 . PMID   21459847.
  9. ↑ Haas, Jürgen; Barbato, Alessandro; Behringer, Dario; Studer, Gabriel; Roth, Steven; Bertoni, Martino; Mostaguir, Khaled; Gumienny, Rafal; Schwede, Torsten (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. PMC   5820194 . PMID   29178137.
  10. ↑ Kryshtafovych, Andriy; Moult, John; Billings, Wendy M.; Corte, Dennis Della; Fidelis, Krzysztof; Kwon, Sohee; Olechnovič, Kliment; Seok, Chaok; Venclovas, Česlovas; Won, Jonghun (2021). "Modeling SARS-CoV2 proteins in the CASP-commons experiment". Proteins: Structure, Function, and Bioinformatics. 89 (12): 1987–1996. doi:10.1002/prot.26231. ISSN   1097-0134. PMC   8616790 . 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". Molecular Biotechnology. 63 (5): 389–409. doi:10.1007/s12033-021-00303-0. ISSN   1559-0305. PMC   7902242 . 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. PMC   6989534 . PMID   32039053.
  13. ↑ Thorn, David C.; Bahraminejad, Elmira; Grosas, Aidan B.; Koudelka, Tomas; Hoffmann, Peter; Mata, Jitendra P.; Devlin, Glyn L.; Sunde, Margaret; Ecroyd, Heath; Holt, Carl; Carver, John A. (2021-03-01). "Native disulphide-linked dimers facilitate amyloid fibril formation by bovine milk αS2-casein". Biophysical Chemistry. 270: 106530. doi:10.1016/j.bpc.2020.106530. ISSN   0301-4622. PMID   33545456. S2CID   230603636.
  14. ↑ Bauer, Saskia; Yu, Dongli; Lawson, Aaron W.; Saur, Isabel M. L.; Frantzeskakis, Lamprinos; Kracher, Barbara; Logemann, Elke; Chai, Jijie; Maekawa, Takaki; Schulze-Lefert, Paul (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". PLOS Pathogens. 17 (2): e1009223. doi: 10.1371/journal.ppat.1009223 . ISSN   1553-7374. PMC   7857584 . 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". Plant Science. 300: 110630. Bibcode:2020PlnSc.30010630A. doi:10.1016/j.plantsci.2020.110630. hdl: 10261/221145 . ISSN   0168-9452. PMID   33180709. S2CID   225009983.
  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. PMC   7471746 . 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". Molecular Plant Pathology. 22 (5): 580–601. doi:10.1111/mpp.13045. ISSN   1364-3703. PMC   8035642 . 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". Advanced Science. 8 (24): 2102592. doi:10.1002/advs.202102592. ISSN   2198-3844. PMC   8693034 . PMID   34719864.
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