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.

ProteinEvolverABC – Estimation of Recombination and Substitution Rates in Alignments of Protein Sequences

ProteinEvolverABC

:: DESCRIPTION

The package ProteinEvolverABC is a computer framework to estimate recombination and substitution rates in multiple alignments of protein sequences by approximate Bayesian computation.

::DEVELOPER

CME Group

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

ProteinEvolverABC

:: MORE INFORMATION

Citation

Arenas M.
ProteinEvolverABC: Coestimation of Recombination and Substitution Rates in Protein Sequences by approximate Bayesian computation.
Bioinformatics. 2021 Aug 27:btab617. doi: 10.1093/bioinformatics/btab617. Epub ahead of print. PMID: 34450622.

SAPS/SSPA 20110801 – Statistical Analysis of Protein Sequences & Significant Segment Pair Alignment

SAPS/SSPA 20110801

:: DESCRIPTION

SAPS (statistical analysis of protein sequences) calculates all the statistics for any individual protein sequence input.

SSPA is an acronym for Significant Segment Pair Alignment.

::DEVELOPER

The Brendel Group @ Indiana University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

SAPS/SSPA

:: MORE INFORMATION

Citation

SAPS
Brendel, V., Bucher, P., Nourbakhsh, I.R., Blaisdell, B.E. & Karlin, S. (1992)
Methods and algorithms for statistical analysis of protein sequences.
Proc. Natl. Acad. Sci. USA 89, 2002-2006.

SSPA
Karlin, S., Weinstock, G. & Brendel, V. (1995)
Bacterial classifications derived from RecA protein sequence comparisons.
J. Bacteriol. 177, 6881-6893.

ELM / iELM 1.0 – Investigation of Functional Sites in Protein Sequences with Eukaryotic Linear Motif database

ELM / iELM 1.0

:: DESCRIPTION

The ELM (Eukaryotic Linear Motif) resource  provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences.

The iELM (interactions of Eukaryotic Linear Motif) web server provides a resource for predicting the function and positional interface for a subset of interactions mediated by short linear motifs (SLiMs).

::DEVELOPER

Gibson Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Holger Dinkel et al.
ELM — the database of eukaryotic linear motifs
Nucl. Acids Res. (2012) 40 (D1): D242-D251.

iELM – a web server to explore short linear motif-mediated interactions.
Weatheritt RJ, Jehl P, Dinkel H, Gibson TJ. (2012).
Nucleic Acids Res. 2012 Jul

Protein Coverage Summarizer 1.3.7993 – Determine Percent of Residues in Protein Sequence

Protein Coverage Summarizer 1.3.7993

:: DESCRIPTION

Protein Coverage Summarizer can be used to determine the percent of the residues in each protein sequence that have been identified. The program requires two input files: the first should contain the protein names and protein sequences (optionally with protein description) while the second should contain the peptide sequences and optionally also contain the protein name associated with each peptide sequence. A graphical user interface (GUI) is provided to allow the user to select the input files, set the options, and browse the coverage results. The results browser displays the protein sequences, highlighting the residues that were present in the peptide input file, and providing sequence coverage stats for each protein.

::DEVELOPER

Matthew Monroe; Niksa Blonder at Biological MS Data and Software Distribution Center , Pacific Northwest National Laboratory

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Microsoft NET Framework 2.0

:: DOWNLOAD

Protein Coverage Summarizer

:: MORE INFORMATION

TSpred – Predict Substitutions that Transform Query Protein Sequence/structure into Temperature-sensitive Mutant

TSpred

:: DESCRIPTION

TSpred attempts to identify a small set of amino acid residues in a query protein that have a high probability of being buried (side-chain accessibilities less than 5%, expressed in terms of residue depth). The server suggests substitutions at these buried positions that are most likely to result in a temperature sensitive (Ts) phenotype.

::DEVELOPER

Bioinformatics Institute of Singapore.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Nucleic Acids Res. 2014 Apr 29. [Epub ahead of print]
TSpred: a web server for the rational design of temperature-sensitive mutants.
Tan KP1, Khare S, Varadarajan R, Madhusudhan MS.

BioSeq-Analysis 2.0 / BioSeq-BLM 1.0 – Analyzing DNA, RNA, and Protein Sequences based on Biological Language Models

 BioSeq-Analysis 2.0 / BioSeq-BLM 1.0

:: DESCRIPTION

BioSeq-Analysis is an platform for analyzing DNA, RNA, and protein sequences at sequence level and residue level based on machine learning approaches

BioSeq-BLM is a platform for analyzing DNA, RNA, and protein sequences based on biological language models

::DEVELOPER

Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux

:: DOWNLOAD

BioSeq-Analysis / BioSeq-BLM

:: MORE INFORMATION

Citation

Li HL, Pang YH, Liu B.
BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models.
Nucleic Acids Res. 2021 Sep 28:gkab829. doi: 10.1093/nar/gkab829. Epub ahead of print. PMID: 34581805.

Liu B, Gao X, Zhang H.
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.
Nucleic Acids Res. 2019 Nov 18;47(20):e127. doi: 10.1093/nar/gkz740. PMID: 31504851; PMCID: PMC6847461.

ANNIE – Protein Sequence Annotation and Interpretation Environment

ANNIE

:: DESCRIPTION

ANNIE is a comprehensive de novo protein annotation system that integrates a large number of indispensable algorithms used in everyday sequence analytic work.

::DEVELOPER

Bioinformatics Institute (BII), Singapore.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W435-40. doi: 10.1093/nar/gkp254. Epub 2009 Apr 23.
ANNIE: integrated de novo protein sequence annotation.
Ooi HS, Kwo CY, Wildpaner M, Sirota FL, Eisenhaber B, Maurer-Stroh S, Wong WC, Schleiffer A, Eisenhaber F, Schneider G

Pse-in-One 1.0.6 – Generating various modes of Pseudo Components of DNA, RNA and Protein Sequences

Pse-in-One 1.0.6

:: DESCRIPTION

Pse-in-One is a web server for generating various modes of pseudo components of DNA, RNA and protein sequences

::DEVELOPER

Liu Lab, Harbin Institute of Technology Shenzhen Graduate School.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

 Pse-in-One

:: MORE INFORMATION

Citation

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.
Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC.
Nucleic Acids Res. 2015 Jul 1;43(W1):W65-71. doi: 10.1093/nar/gkv458.

RSARF – Prediction of Solvent Accessibility from Protein Sequence using Random Forest Method

RSARF

:: DESCRIPTION

RSARF is a random forest method to predict residue accessible surface area from protein sequence information.

::DEVELOPER

RSARF team

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Windows/ Linux
  • R

:: DOWNLOAD

 RSARF

:: MORE INFORMATION

Citation

Protein Pept Lett. 2012 Jan;19(1):50-6.
RSARF: prediction of residue solvent accessibility from protein sequence using random forest method.
Pugalenthi G1, Kandaswamy KK, Chou KC, Vivekanandan S, Kolatkar P.