Predicting protein flexibility through the prediction of local structures

Abstract

Protein structures are valuable tools for understanding protein function. However, protein dynamics is also considered a key element in protein function. Therefore, in addition to structural analysis, fully understanding protein function at the molecular level now requires accounting for flexibility. However, experimental techniques that produce both types of information simultaneously are still limited. Prediction approaches are useful alternative tools for obtaining otherwise unavailable data. It has been shown that protein structure can be described by a limited set of recurring local structures. In this context, we previously established a library composed of 120 overlapping long structural prototypes (LSPs) representing fragments of 11 residues in length and covering all known local protein structures. On the basis of the close sequence-structure relationship observed in LSPs, we developed a novel prediction method that proposes structural candidates in terms of LSPs along a given sequence. The prediction accuracy rate was high given the number of structural classes. In this study, we use this methodology to predict protein flexibility. We first examine flexibility according to two different descriptors, the B-factor and root mean square fluctuations from molecular dynamics simulations. We then show the relevance of using both descriptors together. We define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. The prediction rate reaches 49.6%. This method competes rather efficiently with the most recent, cutting-edge methods based on true flexibility data learning with sophisticated algorithms. Accordingly, flexibility information should be taken into account in structural prediction assessments.

Figure 1. Normalised B-factor values according to normalized RMSF values as determined from molecular dynamics simulations

The two diagonal lines delimit the three flexibility classes defined by the quadruplet (τ_B1, τ_F1, τ_B2, τ_F2) = (−1.5, −0.5, 2.2, 1.1)

Figure 2. Relationship between the mean B-factor_Norm m_B and the mean RMSF_Norm m_F per LSP class

Dot colour represents the secondary structure LSP category, with helical, extended core, connection and extended edge LSPs in black, red, green and blue, respectively. The black line is the first bisector. The brown dashed line gives the regression line.

Figure 3. Relationship between observed flexibility and prediction rate for each LSP class

Flexibility was measured by the mean RMSF_Norm m_F. Dot colour represents the secondary structure LSP category, with helical, extended, connection and extended edge LSPs in black, red, green and blue, respectively. The brown dashed line gives regression line.

Figure 4. Flexibility prediction on the rat intestinal fatty acid-binding protein

(PDB code 1IFC, 131 residues). Top: observed and predicted B-factor_Norm values, bottom: observed and predicted RMSF_Norm values. Black dotted lines indicated observed values and red lines indicate predicted values. Outlier values are symbolised by triangles.

Figure 5. Observed and predicted flexibility descriptors mapped on the rat intestinal fatty acid-binding protein structure

(PDB code 1IFC). A. Normalized B-factors, B. Predicted B-factors, C. Normalized RMSF from molecular dynamics simulations, D. Predicted RMSF.

Figure 6. Flexibility and prediction rate according to the local structure prediction confidence index (CI)

Flexibility descriptors are given on the left y-axis. RMSF_Norm and B-factor_Norm averages according to CI categories are indicated by blue and green lines, respectively. Prediction rates are given on the right y-axis. Local structure prediction rates according to CI are indicated in black and flexibility prediction rates obtained with the quadruplet defined on the whole dataset are in red.