  Flexibility is an intrinsic essential feature of protein structures, directly
linked to their functions. To this day, most of the prediction methods use the
crystallographic data (namely B-factors) as the only indicator of protein's
inner flexibility and predicts them as rigid or flexible.PredyFlexy stands
differently from other approaches as it relies on the definition of protein
flexibility (i) not only taken from crystallographic data, but also (ii) from
Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics
simulations. It also uses a specific representation of protein structures,
named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix,
the 120 LSPs are predicted with a good accuracy and directly used to predict
(i) the protein flexibility in three categories (flexible, intermediate and
rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a
confidence index. Prediction accuracy among these three classes is equivalent
to the best two class prediction methods, while the normalized B-factors and
normalized RMSFs have a good correlation with experimental and in silico
values. Thus, PredyFlexy is a unique approach, which is of major utility for
the scientific community. It support parallelization features and can be run on
a local cluster using multiple cores.The entire project is available under an
open-source license at
http://www.dsimb.inserm.fr/~debrevern/TOOLS/predyflexy_1.3/index.php.
