PIPS 1.0 – Phylogenetic Inference of Protein Stability

PIPS 1.0

:: DESCRIPTION

PIPS (Phylogenetic Inference of Protein Stability) was used to analyze cold shock protein, ribonuclease HI, thioredoxin, and H1 influenza hemagglutinin

::DEVELOPER

Bloom Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 PIPS

:: MORE INFORMATION

Citation

Jesse D. Bloom and Matthew J. Glassman.
Inferring stabilizing mutations from protein phylogenies: application to influenza hemagglutinin.”
PLoS Comput. Biol. 5:e1000349 (2009)

DUET – Predicting Effects of Mutations on Protein Stability

DUET

:: DESCRIPTION

DUET is a web server for an integrated computational approach for studying missense mutations in proteins.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.
Pires DE, Ascher DB, Blundell TL.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W314-9. doi: 10.1093/nar/gku411.

iStable – Integrated Predictor for Protein Stability change upon Single Mutation

iStable

:: DESCRIPTION

iStable ,an integrated predictor, constructed by using sequence information and prediction results from different element predictors.

::DEVELOPER

Natural Computing and Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013;14 Suppl 2:S5. doi: 10.1186/1471-2105-14-S2-S5. Epub 2013 Jan 21.
iStable: off-the-shelf predictor integration for predicting protein stability changes.
Chen CW, Lin J, Chu YW.

MLSTA – Machine Learning Integration for Predicting the Effect of Single Amino Acid Substitutions on Protein Stability

MLSTA

:: DESCRIPTION

MLSTA is a machine learning integration for predicting the effect of single amino acid substitutions on protein stability.

::DEVELOPER

Polymer Research Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Struct Biol. 2009 Oct 19;9:66. doi: 10.1186/1472-6807-9-66.
Machine learning integration for predicting the effect of single amino acid substitutions on protein stability.
Ozen A1, Gönen M, Alpaydan E, Haliloğlu T.

INPS – Predictor of the Impact of Non-Synonymous Mutations on Protein Stability from Sequence

INPS

:: DESCRIPTION

INPS (Impact of Non-synonymous mutations on Protein Stability) is a web server for predicting the impact of non-synonymous Single Nucleatodi Polymorphisms (nsSNPs) on protein stability starting from protein sequence.

::DEVELOPER

Bologna Biocomputing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No

:: MORE INFORMATION

Citation

INPS: Predicting the Impact of Non-Synonymous Variations on Protein Stability from Sequence.
Fariselli P, Martelli PL, Savojardo C, Casadio R.
Bioinformatics. 2015 May 7. pii: btv291.

I-Mutant 2.0.7 – A Tool for Predicting Protein Stability upon Mutation

I-Mutant 2.0.7

:: DESCRIPTION

I-Mutant is a support vector machine (SVM)-based tool for the automatic prediction of protein stability changes upon single point mutations. The software’s predictions are performed starting either from the protein structure or, more importantly, from the protein sequence.

::DEVELOPER

the Structural Bioinformatics Unit

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac / Windows
  • Python

:: DOWNLOAD

 I-Mutant

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W306-10.
I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure.
Capriotti E, Fariselli P, Casadio R.

MUpro 1.1 – Prediction of Protein Stability Changes for Single Site Mutations from Sequences

MUpro 1.1

:: DESCRIPTION

MUpro is a set of machine learning programs to predict how single-site amino acid mutation affects protein stability. We developed two machine learning methods: Support Vector Machines and Neural Networks. Both of them were trained on a large mutation dataset and show accuracy above 84% via 20 fold cross validation, which is better than other methods in the literature. One advantage of our methods is that they do not require tertiary structures to predict protein stability changes. Our experimental results show that the prediction accuracy using sequence information alone is comparable to that of using tertiary structures. So even you do not have protein tertiary structures available, you still can use this server to get rather accurate prediction. Of course, if you provide tertiary structures, our methods will take advantage of them and you might get slightly better predictions.

::DEVELOPER

Institute for Genomics and Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MUpro

:: MORE INFORMATION

Citation:

J. Cheng, A. Randall, and P. Baldi.
Prediction of Protein Stability Changes for Single Site Mutations Using Support Vector Machines.
Proteins. 2006 Mar 1;62(4):1125-32.