PCP 1.0 – Protein Complex Prediction

PCP 1.0

:: DESCRIPTION

PCP is a sofware that searches for cliques in the modified network, and merge cliques to form clusters using a “partial clique merging” method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, the would be very useful for protein complex prediction, especially for prediction of novel protein complexes.

::DEVELOPER

Limsoon Wong Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • C++ Compiler
  • Perl

:: DOWNLOAD

 PCP

:: MORE INFORMATION

Citation:

J Bioinform Comput Biol. 2008 Jun;6(3):435-66.
Using indirect protein-protein interactions for protein complex prediction.
Chua HN, Ning K, Sung WK, Leong HW, Wong L.

pFlexAna 0.1.1 – Protein Flexibility Analyzer

pFlexAna 0.1.1

:: DESCRIPTION

The pFlexAna  detects and displays conformational changes in remotely related proteins, without relying on sequence homology

::DEVELOPER

M2AP Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX
  • C++ Compiler

:: DOWNLOAD

 pFlexAna

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W246-51. doi: 10.1093/nar/gkn259.
pFlexAna: detecting conformational changes in remotely related proteins.
Nigham A, Tucker-Kellogg L, Mihalek I, Verma C, Hsu D.

Emap2sec – Protein secondary structure detection in intermediate-resolution cryo-EM maps

Emap2sec

:: DESCRIPTION

Emap2sec is a deep learning-based tool for detecting protein secondary structures from intermediate resolution cryo-EM maps.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

Emap2sec

:: MORE INFORMATION

Citation

Nat Methods. 2019 Sep;16(9):911-917. doi: 10.1038/s41592-019-0500-1. Epub 2019 Jul 29.
Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.
Maddhuri Venkata Subramaniya SR, Terashi G, Kihara D.

3D-SURFER 2.0 – Real-time Search and Characterization of Protein Surfaces

3D-SURFER 2.0

:: DESCRIPTION

3D-SURFER is a web-based tool for real-time protein surface comparison and analysis. The server integrates a repertoire of methods to assist in high throughput screening and visualization of protein surface comparisons.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

3D-SURFER 2.0: web platform for real-time search and characterization of protein surfaces.
Xiong Y, Esquivel-Rodriguez J, Sael L, Kihara D.
Methods Mol Biol. 2014;1137:105-17. doi: 10.1007/978-1-4939-0366-5_8.

SUPRB 1.0 – Threading Strucuture Prediction

SUPRB 1.0

:: DESCRIPTION

SUPRB (threading with Suboptimal alignment-based PRoBabilistic residue contact information) is a threading strucuture prediction algorithm which employs suboptimal alignments.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SUPRB

:: MORE INFORMATION

Citation

Proteins. 2011 Jan;79(1):315-34. doi: 10.1002/prot.22885.
Effect of using suboptimal alignments in template-based protein structure prediction.
Chen H1, Kihara D.

SUBWAI 1.0 – Protein Structure Prediction program

SUBWAI 1.0

:: DESCRIPTION

SUBWAI (SUBoptimal Weighted AlIgnment) is the protein structure prediction program based on threading strategy with SPAD.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SUBWAI

:: MORE INFORMATION

Citation

Proteins. 2008 May 15;71(3):1255-74.
Estimating quality of template-based protein models by alignment stability.
Chen H1, Kihara D.

Patch-Surfer 2.0 / PL-PatchSurfer2 – Prediction of Binding Ligand for a Protein

Patch-Surfer 2.0 / PL-PatchSurfer2

:: DESCRIPTION

Patch-Surfer is a method used to predict the binding ligand for a protein. The method used the 3 Dimensional Zernike Descriptor (3DZD) and Approximate Patch Position (APP) to describe the features of different patches of the protein pocket. Then retrieve similar pockets in the pocket database based on the similarity of 3DZD and APP between patches of different pockets, so that to predict the binding ligand.

PL-PatchSurfer2 is a protein ligand virtual screening program that uses local surface matching between ligand and receptor pocket.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser / Linux

:: DOWNLOAD

PL-PatchSurfer2

:: MORE INFORMATION

Citation

PL-PatchSurfer: a novel molecular local surface-based method for exploring protein-ligand interactions.
Hu B, Zhu X, Monroe L, Bures MG, Kihara D.
Int J Mol Sci. 2014 Aug 27;15(9):15122-45. doi: 10.3390/ijms150915122.

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.
Zhu X, Xiong Y, Kihara D.
Bioinformatics. 2014 Oct 29. pii: btu724.

PFP / ESG – Sequence Similarity-based Protein Function Prediction server

PFP / ESG

:: DESCRIPTION

PFP (Protein Function Prediction) is a sequence similarity-based protein function prediction server designed to predict GO annotations for a query sequence beyond what can be found by conventional database search such as BLAST.It takes into account weakly similar sequences as well as GO term associations observed in known annotations.

ESG (Extended Similarity Group) is a sequence similarity-based protein function prediction server. It employ PSI-BLAST iteratively and essentially selects GO term annotations that appear consistently in the searches.

Combined PFP & ESG interface.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PFP/ESG: Automated protein function prediction servers enhanced with Gene Ontology visualization tool.
Khan IK, Wei Q, Chitale M, Kihara D.
Bioinformatics. 2015 Jan 15;31(2):271-2. doi: 10.1093/bioinformatics/btu646.

Peptidequant – Optimization-based Peptide Quantification tool

Peptidequant

:: DESCRIPTION

Peptidequant is an optimization-based peptide quantification tool. It is designed to take two challenges in peptide abundance estimation: peptide overlapping and peak intensity variation.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  Peptidequant

:: MORE INFORMATION

Citation:

J Proteome Res. 2010 May 7;9(5):2705-12.
A regularized method for peptide quantification.
Yang C, Yang C, Yu W.

Motif-All 1.0 – Discovering All Phosphorylation Motifs

Motif-All 1.0

:: DESCRIPTION

The Motif-All algorithm discovers motifs from a set of phosphorylated sequences P and a much larger set of background sequences N.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

Motif-All

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java

:: DOWNLOAD

  Motif-All

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S22. doi: 10.1186/1471-2105-12-S1-S22.
Motif-All: discovering all phosphorylation motifs.
He Z1, Yang C, Guo G, Li N, Yu W.