Tag: Proteins

Harmonizome 1.5 – Integrated Knowledge About Genes & Proteins

Harmonizome 1.5

:: DESCRIPTION

Harmonizome is a collection of processed datasets gathered to serve and mine knowledge about genes and proteins from over 70 major online resources.

::DEVELOPER

Ma’ayan Laboratory

:: SCREENSHOTS

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

Harmonizome 

:: MORE INFORMATION

Citation

Database (Oxford). 2016 Jul 3;2016. pii: baw100. doi: 10.1093/database/baw100. Print 2016.
The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins.
Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, Ma’ayan A.

iLoc-Cell – Predictors for Subcellular Localtions of Proteins

iLoc-Cell

:: DESCRIPTION

iLoc-Hum: a predictor for subcellular locations of human proteins with multiple sites.

iLoc-Euk: a predictor for subcellular locations of eukaryotic proteins with multiple sites.

iLoc-Plant: a predictor for subcellular locations of plant proteins with multiple sites.

iLoc-Virus: a predictor for subcellular locations of Viral proteins with multiple sites.

iLoc-Gpos: a predictor for subcellular locations of Gram-Positive proteins with multiple sites.

iLoc-Gneg: a predictor for subcellular locations of human proteins with multiple sites.

::DEVELOPER

Xiao Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites.
Chou KC, Wu ZC, Xiao X.
Mol Biosyst. 2012 Feb;8(2):629-41. doi: 10.1039/c1mb05420a. Epub 2011 Dec 1.

iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins.
Chou KC, Wu ZC, Xiao X.
PLoS One. 2011 Mar 30;6(3):e18258. doi: 10.1371/journal.pone.0018258.

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.
Wu ZC, Xiao X, Chou KC.
Mol Biosyst. 2011 Dec;7(12):3287-97. doi: 10.1039/c1mb05232b. Epub 2011 Oct 10.

iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites.
Xiao X, Wu ZC, Chou KC.
J Theor Biol. 2011 Sep 7;284(1):42-51. doi: 10.1016/j.jtbi.2011.06.005. Epub 2011 Jun 17.

iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins.
Wu ZC, Xiao X, Chou KC.
Protein Pept Lett. 2012 Jan;19(1):4-14.

PLoS One. 2011;6(6):e20592. doi: 10.1371/journal.pone.0020592. Epub 2011 Jun 17.
A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites.
Xiao X1, Wu ZC, Chou KC.

SLIDER – High-throughput Prediction of Proteins with long Disordered Segments

SLIDER

:: DESCRIPTION

SLIDER (Super-fast predictor of proteins with Long Intrinsically DisordERed regions) predicts whether a given protein sequence has long disordered segment(s), i.e., segment(s) with at least 30 consecutive disordered residues.

::DEVELOPER

Kurgan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Proteins. 2014 Jan;82(1):145-58. doi: 10.1002/prot.24348. Epub 2013 Sep 17.
Genome-scale prediction of proteins with long intrinsically disordered regions.
Peng Z1, Mizianty MJ, Kurgan L.

HIVcleave – Predicting HIV Protease Cleavage Sites in Proteins

HIVcleave

:: DESCRIPTION

HIVcleave is a web-server for predicting HIV protease cleavage sites in proteins

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Anal Biochem. 2008 Apr 15;375(2):388-90. doi: 10.1016/j.ab.2008.01.012. Epub 2008 Jan 15.
HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins.
Shen HB1, Chou KC.

Multi-VORFFIP / VORFFIP – Predicts protein-, peptide-, DNA- and RNA-binding sites in Proteins

Multi-VORFFIP / VORFFIP

:: DESCRIPTION

Multi-VORFFIP is a structure-based, machine learning, computational method designed to predict protein-protein, protein-peptide, protein-DNA and protein-RNA binding sites. M-VORFFIP integrates a wide and heterogeneous set of residue- and environment-based information using a two-step Random Forest ensemble classifier.

VORFFIP (Voronoi Random Forest Feedback Interface Predictor) is structure-based computational method for prediction of protein binding sites.

::DEVELOPER

 Bioinformatics Lab :: IBERS :: Aberystwyth University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Jul 15;28(14):1845-50. doi: 10.1093/bioinformatics/bts269. Epub 2012 May 4.
A holistic in silico approach to predict functional sites in protein structures.
Segura J1, Jones PF, Fernandez-Fuentes N.

BMC Bioinformatics. 2011 Aug 23;12:352. doi: 10.1186/1471-2105-12-352.
Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams.
Segura J1, Jones PF, Fernandez-Fuentes N.

mCSM – Predicting Effect of Mutations in Proteins using Graph-based Signatures

mCSM

:: DESCRIPTION

mCSM is a novel approach to the study of missense mutations which relies on graph-based signatures.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WEb browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

mCSM: predicting the effects of mutations in proteins using graph-based signatures.
Pires DE, Ascher DB, Blundell TL.
Bioinformatics. 2014 Feb 1;30(3):335-42. doi: 10.1093/bioinformatics/btt691.

Bluues 2.0 – Electrostatic properties of Proteins based on generalized Born radii

Bluues 2.0

:: DESCRIPTION

Bluues isa novel GB (Generalized Born) based web server to calculate numerous electrostatic features including pKa-values and surface potentials.

:: DESCRIPTION

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Aug 15;28(16):2189-90. doi: 10.1093/bioinformatics/bts343. Epub 2012 Jun 17.
Bluues server: electrostatic properties of wild-type and mutated protein structures.
Walsh I1, Minervini G, Corazza A, Esposito G, Tosatto SC, Fogolari F.

STRING 11.0 – Search Tool for the Retrieval of Interacting Genes/Proteins

STRING 11.0

:: DESCRIPTION

STRING (search tool for recurring instances of neighbouring genes) is a database and web resource dedicated to protein-protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins.

::DEVELOPER

Bork Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

STRING v10: protein-protein interaction networks, integrated over the tree of life.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C.
Nucleic Acids Res. 2015 Jan;43(Database issue):D447-52. doi: 10.1093/nar/gku1003

Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15. doi: 10.1093/nar/gks1094. Epub 2012 Nov 29.
STRING v9.1: protein-protein interaction networks, with increased coverage and integration.
Franceschini A1, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ.

STRING 8–a global view on proteins and their functional interactions in 630 organisms.
Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M, Bork P, von Mering C.
Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6. Epub 2008 Oct 21.

The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored.
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C.
Nucleic Acids Res. 2011 Jan;39(Database issue):D561-8. Epub 2010 Nov 2.

TetraMito – Prediction of Submitochondria Locations of Proteins

TetraMito

:: DESCRIPTION

TetraMito is a sequence-based predictor for identifying submitochondria location of proteins

::DEVELOPER

LinDing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Acta Biotheor. 2013 Jun;61(2):259-68. doi: 10.1007/s10441-013-9181-9. Epub 2013 Mar 10.
Using over-represented tetrapeptides to predict protein submitochondria locations.
Lin H1, Chen W, Yuan LF, Li ZQ, Ding H.

ChloPred – Prediction of Subchloroplast Locations of Proteins

ChloPred

:: DESCRIPTION

ChloPred is a sequence-based predictor for identifying subchloroplast location of proteins

::DEVELOPER

LinDing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Hao Lin, Chen Ding, Lu-Feng Yuan, Wei Chen, Hui Ding, Zi-Qiang Li, Feng-Biao Guo, Jian Huang, Ni-Ni Rao. (2013)
Predicting subchloroplast locations of proteins based on the general form of Chou’s pseudo amino acid composition: approached from optimal tripeptide composition.
International Journal of Biomathematics, 6(2): 1350003.