Tag: Protein

ProteoVision – Advanced Visualization of (Ribosomal) Proteins

ProteoVision

:: DESCRIPTION

ProteoVision is a web server designed to explore protein structure and evolution through simultaneous visualization of multiple sequence alignments, topology diagrams and 3D structures.

::DEVELOPER

ProteoVision team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Penev PI, McCann HM, Meade CD, Alvarez-Carreño C, Maddala A, Bernier CR, Chivukula VL, Ahmad M, Gulen B, Sharma A, Williams LD, Petrov AS.
ProteoVision: web server for advanced visualization of ribosomal proteins.
Nucleic Acids Res. 2021 Jul 2;49(W1):W578-W588. doi: 10.1093/nar/gkab351. PMID: 33999189; PMCID: PMC8265156.

BERT-Kcr – Prediction of Protein Lysine Crotonylation sites

BERT-Kcr

:: DESCRIPTION

BERT-Kcr is a novel predictor for protein Kcr sites prediction, which was developed by using a transfer learning method with pre-trained bidirectional encoder representations from transformers (BERT) models.

::DEVELOPER

Zhu Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

BERT-Kcr

:: MORE INFORMATION

Citation

Qiao Y, Zhu X, Gong H.
BERT-Kcr: Prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.
Bioinformatics. 2021 Oct 13:btab712. doi: 10.1093/bioinformatics/btab712. Epub ahead of print. PMID: 34643684.

JabberDock – Protein Docking using a density-based descriptor for Atoms Charge and Dynamics

JabberDock

:: DESCRIPTION

JabberDock tackles the problem of protein-protein docking while accommodating for rearrangements upon binding including side chain reorientations and backbone flexibility.

::DEVELOPER

Degiacomi Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

JabberDock

:: MORE INFORMATION

Citation:

Rudden LSP, Degiacomi MT.
Transmembrane Protein Docking with JabberDock.
J Chem Inf Model. 2021 Mar 22;61(3):1493-1499. doi: 10.1021/acs.jcim.0c01315. Epub 2021 Feb 26. PMID: 33635637; PMCID: PMC8041277.

PASTA 2.0 – Protein Aggregation Prediction

PASTA 2.0

:: DESCRIPTION

PASTA (Prediction of amyloid structure aggregation) is a web server for the analysis of amino acid sequences. It predicts which portions of a given input sequence are more likely to stabilize the cross-beta core of fibrillar aggregates.

::DEVELOPER

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PASTA

:: MORE INFORMATION

Citation:

PASTA 2.0: an improved server for protein aggregation prediction.
Walsh I, Seno F, Tosatto SC, Trovato A.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W301-7. doi: 10.1093/nar/gku399.

Antonio Trovato, Flavio Seno and Silvio C.E. Tosatto.
The PASTA server for protein aggregation prediction
Protein Engineering Design & Selection, 20(10):521-523. (2007)

FiltRest3D – Filtering Protein Models by Fuzzy Restraints

FiltRest3D

:: DESCRIPTION

FiltRest3D is a software for scoring and ranking of models according to their agreement with user-defined restraints.Automatic methods for protein structure prediction (fold-recognition, de novo folding, and docking programs) produce large sets of alternative models. These large model sets often include many native-like structures, which are scored as high as false positives. Such native-like models can be more easily identified based on data from experimental analyses used as structural restraints (e.g. identification of nearby residues by crosslinking, chemical modification, site-directed mutagenesis, deuterium exchange coupled with mass spectrometry etc.)

FiltRest3D Web Server

::DEVELOPER

Bujnicki Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

FiltRest3D

:: MORE INFORMATION

Citation

Michal J. Gajda,Irina Tuszynska1, Marta Kaczor, Anastasia Yu. Bakulina and Janusz M. Bujnicki
FILTREST3D: discrimination of structural models using restraints from experimental data
Bioinformatics (2010) 26 (23): 2986-2987

Raster3D 3.0-7 – Generate High Quality Raster Images of Proteins or other Molecules

Raster3D 3.0-7

:: DESCRIPTION

Raster3D is a set of tools for generating high quality raster images of proteins or other molecules. The core program renders spheres, triangles, cylinders, and quadric surfaces with specular highlighting, Phong shading, and shadowing. It uses an efficient software Z-buffer algorithm which is independent of any graphics hardware. Ancillary programs process atomic coordinates from PDB files into rendering descriptions for pictures composed of ribbons, space-filling atoms, bonds, ball+stick, etc. Raster3D can also be used to render pictures composed in other programs such as Molscript in glorious 3D with highlights, shadowing, etc. Output is to pixel image files with 24 bits of color information per pixel.

::DEVELOPER

Raster3D Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • MacOsx/Linux/Windows

:: DOWNLOAD

  Raster3D

:: MORE INFORMATION

Citation

Merritt, Ethan A. and Bacon, David J. (1997).
“Raster3D: Photorealistic Molecular Graphics”
Methods in Enzymology 277, 505-524.

Shiftcor 1.3 – Compares, Identifies, Corrects and Re-referencs Protein Chemical Shifts

Shiftcor 1.3

:: DESCRIPTION

Shiftcor compares, identifies, corrects and re-referencs 1H, 13C and 15N backbone chemical shifts of peptides and proteins by comparing the observed chemical shifts with the predicted chemical shifts derived from the 3D structure (PDB corrdinates) of the protein(s)of interest.

::DEVELOPER

the Wishart Research Group, University of Alberta

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Haiyan Zhang, Stephen Neal and David Wishart (2003)
“RefDB: A database of uniformly referenced protein chemical shifts”
Journal of Biomolecular NMR, 25: 173-195

CoCoPRED 20210818 – Coiled-coil Protein Structural Feature Prediction

CoCoPRED 20210818

:: DESCRIPTION

CoCoPRED is a method of coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

CoCoPRED

:: MORE INFORMATION

Citation

Feng SH, Xia CQ, Shen HB.
CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.
Bioinformatics. 2021 Oct 30:btab744. doi: 10.1093/bioinformatics/btab744. Epub ahead of print. PMID: 34718416.

BRS-nonint – Balanced Random Sampling of Non-interactions between Proteins

BRS-nonint

:: DESCRIPTION

BRS-nonint (Balanced Random Sampling of Non-interactions) is a method of sampling non-interactions randomly from the complement graph of protein-protein interactions, while the degree of each protein in the ‘positive’ dataset (interactions) equals to that in the ‘negative’ dataset (non-interactions).

::DEVELOPER

University of Leeds Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Oct 15;26(20):2610-4. Epub 2010 Aug 27.
Simple sequence-based kernels do not predict protein-protein interactions.
Yu J, Guo M, Needham CJ, Huang Y, Cai L, Westhead DR.

Peptides 2.6 – Peptide and Protein Physico-chemical Properties

Peptides 2.6

:: DESCRIPTION

Peptides is a program for calculation of peptide/protein parameters from a given sequence. It calculates number of residues, molecular mass, the molecular formula, volume, pI, amino acid content and a pH-charge correlation (‘titration curve’).

::DEVELOPER

Hofmann Laboratory, Eskitis Institute

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 Peptides

 :: MORE INFORMATION

Citation

Hofmann, A., Wlodawer, A. (2002)
PCSB – a programme collection for structural biology and biophysical chemistry.
Bioinformatics 18, 209-210.

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