TM-score 20190822 – Calculate Similarity of Topologies of two Protein Structures

TM-score 20190822

:: DESCRIPTION

TM-score is an algorithm to calculate the similarity of topologies of two protein structures. It can be exploited to quantitatively access the quality of protein structure predictions relative to native. Because TM-score weights the close matches stronger than the distant matches, TM-score is more sensitive than root-mean-square deviation (RMSD)

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

 TM-score

:: MORE INFORMATION

Citation

J. Xu, Y. Zhang,
How significant is a protein structure similarity with TM-score=0.5?
Bioinformatics, 2010 26, 889-895

PredAlgo 1.0 – Protein Subcellular Localization Prediction in Green Algae

PredAlgo 1.0

:: DESCRIPTION

PredAlgo is a new sequence analysis tool, dedicated to the prediction of protein subcellular localization in green algae. It uses a neural network trained with carefuly curated sets of Chlamydomonas reinhardtii proteins. PredAlgo predicts the localization to one of three compartments: the mitochondrion, the chloroplast, the secretory pathway within the cell.

::DEVELOPER

Nicolas J. Tourasse or Olivier Vallon

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Mol Biol Evol. 2012 Dec;29(12):3625-39. doi: 10.1093/molbev/mss178.
PredAlgo: a new subcellular localization prediction tool dedicated to green algae.
Tardif M, Atteia A, Specht M, Cogne G, Rolland N, Brugière S, Hippler M, Ferro M, Bruley C, Peltier G, Vallon O, Cournac L.

PseAAC / PseAAC-Builder 3.0 / PseAAC-General – Generating Pseudo Amino Acid Composition

PseAAC / PseAAC-Builder 3.0 / PseAAC-General

:: DESCRIPTION

PseAAC is an algorithm that could convert a protein sequence into a digital vector that could be processed by pattern recognition algorithms. The design of PseAAC incorporated the sequence order information to improve the conventional amino acid compositions. The application of pseudo amino acid composition is very common, including almost every branch of computational proteomics.

PseAAC-Builder (PseAAC-General) is a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions.

::DEVELOPER

PseAAC team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows with Cygwin

:: DOWNLOAD

 PseAAC-Builder 

:: MORE INFORMATION

Citation:

Hong-Bin Shen and Kuo-Chen Chou.
PseAAC: a flexible web-server for generating various kinds of protein pseudo amino acid composition.
Analytical Biochemistry, 2008, 373: 386-388.

Anal Biochem. 2012 Jun 15;425(2):117-9. doi: 10.1016/j.ab.2012.03.015. Epub 2012 Mar 27.
PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions.
Du P1, Wang X, Xu C, Gao Y.

Pufeng Du, Shuwang Gu, Yasen Jiao.
PseAAC-General: Fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets.
International Journal of Molecular Sciences 15 (2014) pp.3495-3506

EzyPred – Predicting Enzyme Functional Classes and Sub-classes

EzyPred

:: DESCRIPTION

EzyPred is a top-down approach for predicting enzyme functional classes and sub-classes purely based on protein sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

EzyPred: a top-down approach for predicting enzyme functional classes and subclasses.
Shen HB, Chou KC.
Biochem Biophys Res Commun. 2007 Dec 7;364(1):53-9.

COMSPA – Predicting Protein Structural Classes from Primary Sequence

COMSPA

:: DESCRIPTION

COMSPA is a web server for predicting protein structural classes from primary sequence by learning multi-view features in complex space

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Amino Acids. 2013 May;44(5):1365-79. doi: 10.1007/s00726-013-1472-6. Epub 2013 Feb 28.
Learning protein multi-view features in complex space.
Yu DJ1, Hu J, Wu XW, Shen HB, Chen J, Tang ZM, Yang J, Yang JY.

Cell-PLoc 2.0 – Predicting Subcellular localization of Proteins in different Organisms

Cell-PLoc 2.0

:: DESCRIPTION

Cell-PLoc is a package of Web servers for predicting subcellular localization of proteins in various organisms.The package contains the following six predictors: Euk-mPLoc 2.0 , Hum-mPLoc 2.0, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively.

::DEVELOPER

Computational Systems Biology GroupShanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Nat Protoc. 2008;3(2):153-62. doi: 10.1038/nprot.2007.494.
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.
Chou KC1, Shen HB.

Kuo-Chen Chou, and Hong-Bin Shen,
Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms,
Natural Science, 2010, 2: 1090-1103
PLoS One. 2010 Apr 1;5(4):e9931. doi: 10.1371/journal.pone.0009931.
A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0.
Chou KC1, Shen HB.

A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0.
Shen HB, Chou KC.
Anal Biochem. 2009 Nov 15;394(2):269-74. doi: 10.1016/j.ab.2009.07.046.

Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization.
Chou KC, Shen HB.
PLoS One. 2010 Jun 28;5(6):e11335. doi: 10.1371/journal.pone.0011335.

Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins.
Shen HB, Chou KC.
Protein Pept Lett. 2009;16(12):1478-84.

Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins.
Shen HB, Chou KC.
J Theor Biol. 2010 May 21;264(2):326-33. doi: 10.1016/j.jtbi.2010.01.018.

J Biomol Struct Dyn. 2010 Oct;28(2):175-86.
Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites.
Shen HB1, Chou KC.

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.

LabCaS – Prediction of the Calpain Substrate Cleavage Sites

LabCaS

:: DESCRIPTION

LabCaS  (Labeling Calpain substrate cleavage Sites) is a new computational approach for accurate prediction of the calpain substrate cleavage sites from amino acid sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Proteins. 2013 Apr;81(4):622-34. doi: 10.1002/prot.24217. Epub 2012 Dec 24.
LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields.
Fan YX1, Zhang Y, Shen HB.

LnSignal – Predicting Protein N-terminal Signal Peptides

LnSignal

:: DESCRIPTION

The web server LnSignal (Labelling N-terminal Signal petide cleavage site) was developed by integrating position-specific amino acid propensities based on the highest average positions and conditional random fields.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Yong-Xian Fan, Jiangning Song, Chen Xu and Hong-Bin Shen,
Predicting protein N-terminal signal peptides using position-specific amino acid propensities and conditional random fields ,
Current Bioinformatics, 2013, 8: 183-192.

MemBrain 20200114 – Transmembrane Protein Structure Prediction

MemBrain 20200114

:: DESCRIPTION

MemBrain is a web server developed for transmembrane protein structure prediction. It contains two main prediction functions, i.e., transmembrane helix (TMH) prediction and TMH-TMH residue contact prediction.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

MemBrain: improving the accuracy of predicting transmembrane helices.
Shen H, Chou JJ.
PLoS One. 2008 Jun 11;3(6):e2399. doi: 10.1371/journal.pone.0002399.