EVfold_membrane statistics

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Citations per year

Number of citations per year for the bioinformatics software tool EVfold_membrane

Tool usage distribution map

This map represents all the scientific publications referring to EVfold_membrane per scientific context
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EVfold_membrane specifications


Unique identifier OMICS_13939
Name EVfold_membrane
Interface Web user interface
Restrictions to use None
Input data An amino acid sequence or protein, an UniProt ID Number, an UniProt Name
Input format RAW, FASTA
Programming languages MATLAB
Computer skills Basic
Stability Stable
Source code URL http://evfold.org/evfold-web/code.do
Maintained Yes


Publication for EVfold_membrane

EVfold_membrane citations


Structural basis for xenobiotic extrusion by eukaryotic MATE transporter

Nat Commun
PMCID: 5696359
PMID: 29158478
DOI: 10.1038/s41467-017-01541-0

[…] tracellular gate formation (Fig. ). To verify the putative interactions in the extracellular gate, we calculated the evolutionary coupling pairs from the 36,873 MATE family members, using the program EVfold_membrane,. The high EC values for the predicted residue pairs in the putative extracellular gate indicated that these residue pairs have co-evolved to allow the extracellular gate formation in […]


Deciphering membrane protein structures from protein sequences

Genome Biol
PMCID: 3446308
PMID: 22738306
DOI: 10.1186/gb-2012-13-6-160

[…] sequences to predict membrane protein structures. They demonstrate that the three-dimensional structure of α-helical membrane proteins can be determined with very good accuracy using their algorithm EVfold_membrane []. Their approach identifies positions in protein sequences that show correlated patterns of mutation and uses this information as a structural constraint to model membrane protein st […]

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EVfold_membrane institution(s)
Department of Systems Biology, Harvard Medical School, Boston, MA, USA; Department of Informatics, Technische Universität München, Munich, Germany; MRC Laboratory of Molecular Biology, Hills Road, Cambridge, UK; Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York City, NY, USA

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