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.

INTREPED 2.0 – DNA Repair Protein Prediction server

INTREPED 2.0

:: DESCRIPTION

The INTREPED (INTeractive dna REPair prEDiction) web server is a collection of services for predicting properties relating to DNA repair. DNA repair is thought to exist in all organisms that have an active metabolism, and repair systems are crucial for repairing many types of DNA damage. External damage includes (but is not limited to) ionizing (UV) radiation, tobacco smoke, and chemical alteration. Examples of internal damage are DNA copying errors, oxygen byproducts resulting from metabolism, and hydrolysis.

::DEVELOPER

Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2009 Jan 20;10:25. doi: 10.1186/1471-2105-10-25.
Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology.
Brown JB, Akutsu T.

PRORATE – Prediction of Protein Folding Rates

PRORATE

:: DESCRIPTION

PRORATE is a novel approach to predict protein folding rates for two-state and multi-state protein folding kinetics, which combines a variety of structural topology and complex network properties that are calculated from protein three-dimensional structures.

::DEVELOPER

Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Song J, Takemoto K, Shen H, Tan H, Gromiha MM, Akutsu T.
Prediction of protein folding rates from structural topology and complex network properties.
IPSJ Transactions on Bioinformatics 3, 40-53, 2010-05-12

HSEpred – Prediction of Half-Sphere Exposure from Protein Sequences

HSEpred

:: DESCRIPTION

HSEpred is a web server to predict the HSE(Half-Sphere Exposure) measures and infer residue contact numbers using the predicted HSE values, based on a well-prepared non-homologous protein structure dataset.

::DEVELOPER

Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Web Server

:: DOWNLOAD

 HSEpred

:: MORE INFORMATION

Citation:

Bioinformatics. 2008 Jul 1;24(13):1489-97. doi: 10.1093/bioinformatics/btn222.
HSEpred: predict half-sphere exposure from protein sequences.
Song J, Tan H, Takemoto K, Akutsu T.

Exit mobile version