GASS – Identifying Enzymes Active Site with Genetic Algorithms

GASS

:: DESCRIPTION

GASS (Genetic Active Site Search) searches for given active site 3D templates in unknown proteins.

::DEVELOPER

GASS  team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 GASS

:: MORE INFORMATION

Citation

GASS: Identifying Enzyme Active Sites with Genetic Algorithms.
Izidoro SC, de Melo-Minardi RC, Pappa GL.
Bioinformatics. 2014 Nov 10. pii: btu746.

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.

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.

HMMSTR 20120205 – Protein Secondary Structure Prediction

HMMSTR 20120205

:: DESCRIPTION

HMMSTR ( Hidden Markov Model for Local Sequence-Structur) is a hidden Markov model for protein structure prediction. The program takes as input an amino acid probability distribution (or profile) for each residue position.  A profile may be derived from a multiple sequence alignment, or by running the database search program such as PSI_BLAST. It contains the programs needed to predict secondary structure starting with a sequence profile. The sequence profile (a vector of 20 probabilities for each residue in the sequence) can be the output of a profile HMM such as HMMer. It may also be the output of Psi-Blast, which uses profiles internally, or may be generated from a multiple sequence alignment. The programs in this package, HMMSTR and associated format converters, will give you a probabilistic prediction of each of the six DSSP symbols: H,E,G,S,T and _. For now, this is a bare-bones package.

HMMSTR Online Version

::DEVELOPER

Chris Bystroff

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

HMMSTR

:: MORE INFORMATION

Citaiton

BMC Bioinformatics. 2008 Oct 10;9:429. doi: 10.1186/1471-2105-9-429.
Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure.
Bystroff C, Webb-Robertson BJ.

Bystroff C, Thorsson V & Baker D. (2000).
HMMSTR: A hidden markov model for local sequence-structure correlations in proteins.
Journal of Molecular Biology 301, 173-90.

ContextShapes – Protein Docking and Partial Shape Matching

ContextShapes

:: DESCRIPTION

ContextShapes does rigid-body protein docking. It uses a novel contextshapes data structure to represent local surface regions/shapes on the protein. All critical points on both the receptor and ligand are represented via context shapes, and the best docking is found via pair-wise matching.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 ContextShapes

:: MORE INFORMATION

Citation

Zujun Shentu, Mohammad Al Hasan, Chris Bystroff and Mohammad J. Zaki,
Context Shapes: Efficient Complementary Shape Matching for Protein-Protein Docking.
Proteins: Structure, Function and Bioinformatics, 70(3):1056-1073. Feb 2008

PSIST – Protein Indexing

PSIST

:: DESCRIPTION

PSIST (protein structures using suffix trees) uses suffix trees to index protein 3D structure. It first converts the 3D structure into a structure-feature sequence over a new structural alphabet, which is then used to index protein structures. The PSIST index makes it very fast to query for a matching structural fragment.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 PSIST

:: MORE INFORMATION

Citation

Feng Gao and Mohammed J. Zaki,
PSIST: A Scalable Approach to Indexing Protein Structures using Suffix Trees.
Journal of Parallel and Distributed Computing, 68(1):55-63. Jan 2008

UNFOLD – Protein Folding Pathways

UNFOLD

:: DESCRIPTION

 UNFOLD uses a recursive min-cut on a weighted secondary structure element graph to predict the sequence of protein (un)folding events.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Perl

:: DOWNLOAD

 UNFOLD

:: MORE INFORMATION

Citation

Mohammed J. Zaki, Vinay Nadimpally, Deb Bardhan and Chris Bystroff,
Predicting protein folding pathways.
Bioinformatics, 20(1):i386-i393. Aug 2004.

transFold – Super-secondary Structure Prediction of Transmembrane β-barrel proteins

transFold

:: DESCRIPTION

transFold is a web server for beta-barrel supersecondary structure prediction. Unlike other software which employ machine learning methods, transFold uses multi-tape S-attribute grammars to describe the space of all possible supersecondary structures, then applies dynamic programming to compute the global energy minimum structure.

::DEVELOPER

Clote Lab 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J. Waldispühl, B. Berger, P. Clote, J.-M. Steyaert,
TransFold: a Web Server for predicting the structure and residue contacts of transmembrane beta-barrels,
Nucleic Acids Res. 34(Web Server Issue):189-193 (2006).