In silico prediction of protein flexibility with local structure approach

TL;DR

PredyFlexy is an innovative in silico method that predicts protein flexibility by integrating crystallographic data and molecular dynamics simulations, utilizing Long Structural Prototypes for accurate, multi-category predictions and confidence assessment.

Contribution

It introduces a novel approach combining structural prototypes and dynamic data for flexible protein prediction, surpassing existing methods in accuracy and providing open-source tools.

Findings

Prediction accuracy matches top two-class methods.

Normalized B-factors and RMSFs correlate well with experimental data.

Supports parallel processing for large-scale analysis.

Abstract

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.