PPIevo – Protein-protein Interaction Prediction from PSSM based Evolutionary Information

PPIevo

:: DESCRIPTION

PPIevo is a software of protein-protein interaction prediction from PSSM based evolutionary information

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

PPIevo

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 PPIevo

:: MORE INFORMATION

Citation:

Genomics. 2013 Oct;102(4):237-42. doi: 10.1016/j.ygeno.2013.05.006. Epub 2013 Jun 6.
PPIevo: protein-protein interaction prediction from PSSM based evolutionary information.
Zahiri J1, Yaghoubi O, Mohammad-Noori M, Ebrahimpour R, Masoudi-Nejad A.

EPC-map – Combining Physicochemical and Evolutionary Information for Protein Contact Prediction

EPC-map

:: DESCRIPTION

EPC-map (Evolutionary and Physicochemical information to predict Contact maps) is a novel contact prediction method that predicts contacts using two sources of information: evolutionary information from multiple sequence alignments and information from physicochemical energy potentials .

::DEVELOPER

the Robotics and Biology Lab (RBO), Technische Universität Berlin.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PLoS One. 2014 Oct 22;9(10):e108438. doi: 10.1371/journal.pone.0108438. eCollection 2014.
Combining physicochemical and evolutionary information for protein contact prediction.
Schneider M, Brock O

CCHMMPROF – Predictor of Coiled-Coils Regions in Proteins Exploiting Evolutionary Information

CCHMMPROF

:: DESCRIPTION

CCHMM_PROF is a hidden Markov model that exploits the information contained in multiple sequence alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches an accuracy of 80% at the residue level and a best per-segment and per-protein efficiency of 81% and 80%, respectively. The results indicate that CCHMM_PROF outperforms all the existing tools and can be adopted for large-scale genome annotation.

::DEVELOPER

Bologna Biocomputing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Python

:: DOWNLOAD

 CCHMMPROF

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Nov 1;25(21):2757-63. Epub 2009 Sep 10.
CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information.
Bartoli L, Fariselli P, Krogh A, Casadio R.