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SecStAnT [1] 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. The possibility of managing different resolutions and the primary/secondary structure selectivity allow addressing the mapping-backmapping of atomistic to CG representation and study the secondary to primary structure relations [2] [3] . Furthermore, the sleek and intuitive interface allows to easily and systematically compute data-set analysis of two and three body correlation.

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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:

  • 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)
  • two variables correlations (including the PHI-PSI Ramachandran map and its equivalent in the Ca based representation)
  • three variables correlations

Publications.

  1. Giuseppe Maccari, Giulia L. B. Spampinato, Valentina Tozzini - SecStAnT: secondary structure analysis tool for data selection, statistics and models building Bioinformatics 5:668-674 (2014)
  2. Fabio Trovato, Valentina Tozzini - Minimalist models for biopolymers: Open problems, latest advances and perspectives AIP Conference Proceedings :187-200 (2012)
  3. Valentina Tozzini, Walter Rocchia, J. Andrew McCammon - Mapping All-Atom Models onto One-Bead Coarse-Grained Models:  General Properties and Applications to a Minimal Polypeptide Model Journal of Chemical Theory and Computation 3:667-673 (2006)