SecretomeP 2.0 – Prediction of Non-classical Protein Secretion

SecretomeP 2.0

:: DESCRIPTION

SecretomeP server produces ab initio predictions of non-classical i.e. not signal peptide triggered protein secretion. The method queries a large number of other feature prediction servers to obtain information on various post-translational and localizational aspects of the protein, which are integrated into the final secretion prediction.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

SecretomeP

:: MORE INFORMATION

Citation

Feature based prediction of non-classical and leaderless protein secretion
J. Dyrløv Bendtsen, L. Juhl Jensen, N. Blom, G. von Heijne and S. Brunak
Protein Eng. Des. Sel., 17(4):349-356, 2004

PSIPRED 4.02 – Accurate Protein Secondary Structure Prediction

PSIPRED 4.02

:: DESCRIPTION

PSIPRED (Position Specific Iterated Prediction) is a highly accurate method for protein secondary structure prediction.

Bioinformatics Group – University College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 PSIPRED

:: MORE INFORMATION

Citation:

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W349-57. doi: 10.1093/nar/gkt381. Epub 2013 Jun 8.
Scalable web services for the PSIPRED Protein Analysis Workbench.
Buchan DW1, Minneci F, Nugent TC, Bryson K, Jones DT.

Bioinformatics. 2000 Apr;16(4):404-5.
The PSIPRED protein structure prediction server.
McGuffin LJ, Bryson K, Jones DT.

CSS-Palm 4.0 – Palmitoylation Site Prediction with a Clustering and Scoring Strategy

CSS-Palm 4.0

:: DESCRIPTION

CSS-Palm is a computer program for palmitoylation site prediction, Clustering and Scoring Strategy for Palmitoylation Sites Prediction.The program’s prediction performance is encouraging with highly positive Jack-Knife validation results (sensitivity 82.16% and specificity 83.17% for cut-off score 2.6).

::DEVELOPER

The CUCKOO Workgroup

:: SCREENSHOTS

:: REQUIREMENTS

  • WIndows / Linux / MacOsX
  • Java

:: DOWNLOAD

 CSS-Palm

:: MORE INFORMATION

Citation

CSS-Palm 2.0: an updated software for palmitoylation sites prediction
Jian Ren, Longping Wen, Xinjiao Gao, Changjiang Jin, Yu Xue and Xuebiao Yao.
Protein Engineering, Design and Selection.2008 21(11):639-644

Discriminative HMMs – Find Discriminative Motif to Predict Protein Subcellular Localization

Discriminative HMMs

:: DESCRIPTION

Discriminative HMMs (Hidden Markov models) predicts localizations using motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism.

::DEVELOPER

Tien-ho LinRobert F. Murphy, and Ziv Bar-Joseph

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Discriminative HMMs

:: MORE INFORMATION

Citation

IEEE/ACM Trans Comput Biol Bioinform. 2011 Mar-Apr;8(2):441-51.
Discriminative motif finding for predicting protein subcellular localization.
Lin TH, Murphy RF, Bar-Joseph Z.

FastHMM / FastBLAST 1.3 – Analyzing Large Protein Sequence Databases

FastHMM / FastBLAST 1.3

:: DESCRIPTION

FastHMM and FastBLAST are fast heuristics to replace HMM search, InterProScan, and all-versus-all BLAST. FastHMM uses PSI-BLAST to quickly select likely members of the family and then uses HMMer to confirm those hits. FastBLAST relies on alignments of proteins to known families from FastHMM and from rpsblast against COG. FastBLAST uses these alignments to avoid most of the work of all-versus-all BLAST. FastBLAST further reduces the work by clustering similar sequences.

::DEVELOPER

FastBLAST Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • inux
  • Perl
  • C Compiler
  • HMMer
  • NCBI BLAST
  • MUSCLE
  • CD-HIT

:: DOWNLOAD

 FastHMM / FastBLAST

:: MORE INFORMATION

Citation

PLoS One. 2008;3(10):e3589. doi: 10.1371/journal.pone.0003589. Epub 2008 Oct 31.
FastBLAST: homology relationships for millions of proteins.
Price MN, Dehal PS, Arkin AP.

DeepCNF-D 1.00 – Predicting Protein Order / Disorder Regions by Weighted Deep Convolutional Neural Fields

DeepCNF-D 1.00

:: DESCRIPTION

DeepCNF-D is a protein disorder region prediction tool based on weighted Deep Convolutional Neural Fields (DeepCNF).

::DEVELOPER

Sheng Wang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

DeepCNF-D

:: MORE INFORMATION

Citation:

Wang S, Weng S, Ma J, Tang Q.
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields.
Int J Mol Sci. 2015 Jul 29;16(8):17315-30. doi: 10.3390/ijms160817315. PMID: 26230689; PMCID: PMC4581195.

AcconPred 1.00 – Predicting Solvent Accessibility and Contact Number of Protein

AcconPred 1.00

:: DESCRIPTION

AcconPred is a software package that helps predicting solvent accessibility and contact number of a protein simultaneously.

::DEVELOPER

Sheng Wang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

AcconPred

:: MORE INFORMATION

Citation:

Ma J, Wang S.
AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.
Biomed Res Int. 2015;2015:678764. doi: 10.1155/2015/678764. Epub 2015 Aug 3. PMID: 26339631; PMCID: PMC4538422.

IPPI – Inferring Protein-Protein Interactions for YEAST

IPPI

:: DESCRIPTION

IPPI is a web server of inferring protein-protein interactions

::DEVELOPER

Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Bioinformatics. 2003 Oct;19 Suppl 2:ii58-65.
Inferring strengths of protein-protein interactions from experimental data using linear programming.
Hayashida M, Ueda N, Akutsu T.

SLPFA – Subcellular Location Prediction with Frequency and Alignment

SLPFA

:: DESCRIPTION

SLPFA is a predictor for subcellular location prediction of proteins by feature vectors based on amino acid composition (frequency) and sequence alignment. 90.96% of overall accuracy was obtained through fivefold cross validation tests with TargetP plant data sets.

SLPFA is an improved subcellular location predictor of SLP-Local

::DEVELOPER

Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2007 Nov 30;8:466.
Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition.
Tamura T, Akutsu T.