SecStAnT

a Secondary Structure Analysis Tool, for data selection, statistics and models building

Welcome to SecStAnT Homepage!

SecStAnT is a tool for the automatic creation of data-sets of structures from Protein Data Bank (PDB) with user-defined structural composition, and for the calculation of their internal variables distributions.

Download latest version (1.0.3.6) (Documentation):

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Coarse graining of the structures
CUBE display of a three variables correlation map

Dataset Building

Select from PDB data sets of structures based on user specified secondary structures (defined based on internal PDB classification or on DSSP) and/or sequence motives. Data sets can be further refined based on all the additional criteria available on PDB advanced search (experimental determination method, resolution, publication year, structure diversity etc.)

Choose your resolution

With SecStAnT you can choose to build your dataset at different levels of resolution (all atoms, only backbone, only Ca, ...). In this way your dataset will be perfectly fitted to parametrize your Coarse Grain (CG) Model

Evaluate!

Evaluate statistical distributions of internal variables, including:
  1. single variable distributions (including the most relevant in the atomistic representation, e.g. PHI and PSI and a number of those for the Ca based representation)
  2. two variables correlations (including the PHI-PSI Ramachandran map and its equivalent in the Ca based representation)
  3. three variables correlations (see "link" for details)
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Screenshots.

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Publications.

  1. SecStAnT: Secondary Structure Analysis Tool for data selection, statistics and models building G. Maccari, G. L. B. Spampinato, V. Tozzini, Bioinformatics 2013; doi: 10.1093/bioinformatics/btt586
  2. Minimalist models for biopolymers: open problems, latest advances and perspectives. Trovato, F.; Tozzini, V. Journal of Chemical Theory and Computation Volume: 2 Issue: 3 Pages: 667-673 Published: MAY 2006
  3. Mapping all-atom models onto one-bead Coarse Grained Models: general properties and applications to a minimal polypeptide model. Tozzini, V; Rocchia, W; McCammon, JA Journal of Chemical Theory and Computation Volume: 2 Issue: 3 Pages: 667-673 Published: MAY 2006
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About us.

Our Team

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