IntFOLD

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, ModFOLD for 3D model quality estimation,{{Cite journal|last1=Maghrabi|first1=Ali H. A.|last2=McGuffin|first2=Liam J.|date=2017-04-29|title=ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models|url=http://dx.doi.org/10.1093/nar/gkx332|journal=Nucleic Acids Research|volume=45|issue=W1|pages=W416–W421|doi=10.1093/nar/gkx332|pmid=28460136|pmc=5570241|issn=0305-1048}} ReFOLD for refinement of 3D models,{{Cite journal|last1=Adiyaman|first1=Recep|last2=McGuffin|first2=Liam J|date=2021-05-01|title=ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts|url=http://dx.doi.org/10.1093/nar/gkab300|journal=Nucleic Acids Research|volume=49|issue=W1|pages=W589–W596|doi=10.1093/nar/gkab300|issn=0305-1048|pmc=8218204|pmid=34009387}} DisoCLUST for disorder prediction,{{Cite journal|last1=Atkins|first1=Jennifer|last2=Boateng|first2=Samuel|last3=Sorensen|first3=Thomas|last4=McGuffin|first4=Liam|date=2015-08-13|title=Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies|journal=International Journal of Molecular Sciences|volume=16|issue=8|pages=19040–19054|doi=10.3390/ijms160819040|pmid=26287166|pmc=4581285|issn=1422-0067|doi-access=free}} DomFOLD for structural domain prediction,{{Cite journal|last1=McGuffin|first1=Liam J.|last2=Atkins|first2=Jennifer D.|last3=Salehe|first3=Bajuna R.|last4=Shuid|first4=Ahmad N.|last5=Roche|first5=Daniel B.|date=2015-03-27|title=IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences: Figure 1.|url=http://dx.doi.org/10.1093/nar/gkv236|journal=Nucleic Acids Research|volume=43|issue=W1|pages=W169–W173|doi=10.1093/nar/gkv236|issn=0305-1048|pmc=4489238|pmid=25820431}} and FunFOLD for protein ligand binding site prediction.{{Cite journal|last1=Roche|first1=Daniel B.|last2=Buenavista|first2=Maria T.|last3=McGuffin|first3=Liam J.|date=2013-06-11|title=The FunFOLD2 server for the prediction of protein–ligand interactions|url=http://dx.doi.org/10.1093/nar/gkt498|journal=Nucleic Acids Research|volume=41|issue=W1|pages=W303–W307|doi=10.1093/nar/gkt498|pmid=23761453|pmc=3692132|issn=1362-4962}} 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.

The only required input is a protein sequence for the prediction of the protein 3D structure and function. 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.

The IntFOLD method was firstly benchmarked in Critical Assessment of Techniques for Protein Structure Prediction 9 (CASP9) and ranked among the top 5.{{Cite journal|last1=Roche|first1=D. B.|last2=Buenavista|first2=M. T.|last3=Tetchner|first3=S. J.|last4=McGuffin|first4=L. J.|date=2011-03-31|title=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|url=http://dx.doi.org/10.1093/nar/gkr184|journal=Nucleic Acids Research|volume=39|issue=suppl|pages=W171–W176|doi=10.1093/nar/gkr184|issn=0305-1048|pmc=3125722|pmid=21459847}} The IntFOLD server has consolidated its performance in the following CASP experiments

Its performance is being continually evaluated in Continuous Automated Model Evaluation (CAMEO) experiment.{{Cite journal |last1=Haas |first1=Jürgen |last2=Barbato |first2=Alessandro |last3=Behringer |first3=Dario |last4=Studer |first4=Gabriel |last5=Roth |first5=Steven |last6=Bertoni |first6=Martino |last7=Mostaguir |first7=Khaled |last8=Gumienny |first8=Rafal |last9=Schwede |first9=Torsten |date=2017-12-17 |title=Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12 |url=http://dx.doi.org/10.1002/prot.25431 |journal=Proteins: Structure, Function, and Bioinformatics |volume=86 |issue=Suppl 1 |pages=387–398 |doi=10.1002/prot.25431 |pmid=29178137 |issn=0887-3585|pmc=5820194 }}

IntFOLD was used to generate 3D models of the SARS-CoV-2 targets for the CASP Commons COVID-19 initiative{{Cite journal|last1=Kryshtafovych|first1=Andriy|last2=Moult|first2=John|last3=Billings|first3=Wendy M.|last4=Corte|first4=Dennis Della|last5=Fidelis|first5=Krzysztof|last6=Kwon|first6=Sohee|last7=Olechnovič|first7=Kliment|last8=Seok|first8=Chaok|last9=Venclovas|first9=Česlovas|last10=Won|first10=Jonghun|title=Modeling SARS-CoV2 proteins in the CASP-commons experiment|journal=Proteins: Structure, Function, and Bioinformatics|year=2021|volume=89|issue=12|pages=1987–1996|language=en|doi=10.1002/prot.26231|pmid=34462960|pmc=8616790|issn=1097-0134}} and elsewhere {{Cite journal|last1=Sadat|first1=Seyed Mehdi|last2=Aghadadeghi|first2=Mohammad Reza|last3=Yousefi|first3=Masoume|last4=Khodaei|first4=Arezoo|last5=Sadat Larijani|first5=Mona|last6=Bahramali|first6=Golnaz|date=2021-05-01|title=Bioinformatics Analysis of SARS-CoV-2 to Approach an Effective Vaccine Candidate Against COVID-19|url=https://doi.org/10.1007/s12033-021-00303-0|journal=Molecular Biotechnology|language=en|volume=63|issue=5|pages=389–409|doi=10.1007/s12033-021-00303-0|issn=1559-0305|pmc=7902242|pmid=33625681}} 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.{{Cite journal|last1=Saraswat|first1=Shweta|last2=Chaudhary|first2=Meenakshi|last3=Sehgal|first3=Deepak|date=2020|title=Hepatitis E Virus Cysteine Protease Has Papain Like Properties Validated by in silico Modeling and Cell-Free Inhibition Assays|journal=Frontiers in Cellular and Infection Microbiology|volume=9|page=478|doi=10.3389/fcimb.2019.00478|issn=2235-2988|pmc=6989534|pmid=32039053|doi-access=free}} 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.{{Cite journal|last1=Thorn|first1=David C.|last2=Bahraminejad|first2=Elmira|last3=Grosas|first3=Aidan B.|last4=Koudelka|first4=Tomas|last5=Hoffmann|first5=Peter|last6=Mata|first6=Jitendra P.|last7=Devlin|first7=Glyn L.|last8=Sunde|first8=Margaret|last9=Ecroyd|first9=Heath|last10=Holt|first10=Carl|last11=Carver|first11=John A.|date=2021-03-01|title=Native disulphide-linked dimers facilitate amyloid fibril formation by bovine milk αS2-casein|url=https://www.sciencedirect.com/science/article/pii/S0301462220302386|journal=Biophysical Chemistry|language=en|volume=270|pages=106530|doi=10.1016/j.bpc.2020.106530|pmid=33545456|s2cid=230603636|issn=0301-4622|url-access=subscription}}

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.{{Cite journal|last1=Bauer|first1=Saskia|last2=Yu|first2=Dongli|last3=Lawson|first3=Aaron W.|last4=Saur|first4=Isabel M. L.|last5=Frantzeskakis|first5=Lamprinos|last6=Kracher|first6=Barbara|last7=Logemann|first7=Elke|last8=Chai|first8=Jijie|last9=Maekawa|first9=Takaki|last10=Schulze-Lefert|first10=Paul|date=2021-02-03|title=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|journal=PLOS Pathogens|language=en|volume=17|issue=2|pages=e1009223|doi=10.1371/journal.ppat.1009223|issn=1553-7374|pmc=7857584|pmid=33534797 |doi-access=free }} 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.{{Cite journal|last1=Aznar-Moreno|first1=Jose A.|last2=Venegas-Calerón|first2=Mónica|last3=Du|first3=Zhi-Yan|last4=Garcés|first4=Rafael|last5=Tanner|first5=Julian A.|last6=Chye|first6=Mee-Len|last7=Martínez-Force|first7=Enrique|last8=Salas|first8=Joaquín J.|date=2020-11-01|title=Characterization and function of a sunflower (Helianthus annuus L.) Class II acyl-CoA-binding protein|url=https://www.sciencedirect.com/science/article/pii/S0168945220302363|journal=Plant Science|language=en|volume=300|pages=110630|doi=10.1016/j.plantsci.2020.110630|pmid=33180709|bibcode=2020PlnSc.30010630A |hdl=10261/221145|s2cid=225009983|issn=0168-9452|hdl-access=free}}{{Cite journal|last1=Kalananthan|first1=Tharmini|last2=Lai|first2=Floriana|last3=Gomes|first3=Ana S.|last4=Murashita|first4=Koji|last5=Handeland|first5=Sigurd|last6=Rønnestad|first6=Ivar|date=2020|title=The Melanocortin System in Atlantic Salmon (Salmo salar L.) and Its Role in Appetite Control|journal=Frontiers in Neuroanatomy|volume=14|page=48|doi=10.3389/fnana.2020.00048|issn=1662-5129|pmc=7471746|pmid=32973463|doi-access=free}} IntFOLD was used to model Chitin proteins in Podosphaera xanthii, a causal agent of fungal disease called cucurbit powdery mildew, which hamper crop productivity.{{Cite journal|last1=Polonio|first1=Álvaro|last2=Fernández-Ortuño|first2=Dolores|last3=Vicente|first3=Antonio de|last4=Pérez-García|first4=Alejandro|date=2021|title=A haustorial-expressed lytic polysaccharide monooxygenase from the cucurbit powdery mildew pathogen Podosphaera xanthii contributes to the suppression of chitin-triggered immunity|journal=Molecular Plant Pathology|language=en|volume=22|issue=5|pages=580–601|doi=10.1111/mpp.13045|issn=1364-3703|pmc=8035642|pmid=33742545}}

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.{{Cite journal|last1=Su|first1=Hong|last2=Wang|first2=Wenkai|last3=Du|first3=Zongyang|last4=Peng|first4=Zhenling|last5=Gao|first5=Shang-Hua|last6=Cheng|first6=Ming-Ming|last7=Yang|first7=Jianyi|date=2021|title=Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates|journal=Advanced Science|language=en|volume=8|issue=24|pages=2102592|doi=10.1002/advs.202102592|issn=2198-3844|pmc=8693034|pmid=34719864}}

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Category:Bioinformatics software