PPTs logo

Table Of Contents

PyParaTools FeaturesΒΆ

PyParaTools is Python based software used to analyse paramagnetic Nuclear Magnetic Resonance (NMR) data. PyParaTools has been developed to facilitate fast, flexible, simple or complex analysis of Pseudocontact Shift (PCS), Paramagnetic Relaxation Enhancement (PRE) and, or, Residual Dipolar Coupling (RDC) data.

Existing software packages that support paramagnetic NMR data suffer from limitations
  • they predominantly support only a single paramagnetic effect,
  • often they use different or non-standard conventions when reporting parameters that define the paramagnetic effect,
  • the format of input files can vary considerably,
  • those that are GUI based, do facilitate ease-of-use, however, they become inflexible and complex analysis can be challenging and time consuming, and
  • they have not usually designed to be extended or modified.

The PyParaTools software unifies PCS, PRE and RDC data in simple, inuitive, yet powerful manner.

Some key features of PyParaTools include
  • it understands PCS, PRE and RDC data, described in an simple, single input format,
  • it reports parameters in a consistent, unique manner, removing ambiguity from reported solutions,
  • it is driven by Python scripts, allowing for fast, complex analysis to be performed with a minimum of fuss,
  • it is distributed with pre-tested example protocols so that the new users can begin analysing their data quickly,
  • it is distributed with extensive user and developer documentation with additional support provided by a mailing list,
  • with minimal overhead, it can be incorporated into other biomolecular software packages
PyParaTools comes with extensive optimization routines
  • it can optimize for a single parameter set given a single structural model
  • it can optimize for a single parameter set for multiple multiple structural models
  • it can optimize for multiple parameter sets given a single dataset
  • it can unify PCS, PRE and RDC datasets, by optimizing the common parameters of each effect in a simultaneous manner

PyParaTools has extensive analysis tools