Quantifying the relationship of protein burying depth and sequence

Abstract

Protein burying depth (BD) is a structural descriptor that is exploited not only to find whether a residue is exposed or buried, but also to determine how deep a residue is buried. The widely used solvent accessible surface area is mainly focusing on the study of protein surface residues, while protein BD can provide more detailed information about the arrangement of buried residues, which may be used to study protein deep level structure and the formation of protein folding nucleus. In this work, we analyse the relationship of protein BD and sequences, and describe it by nonlinear functions estimated by support vector machines. We examine the functions by crossvalidation tests and find strong correlation between residue BD and local sequence environment. By further taking account the size of the molecule where a residue is located, we find that the correlation coefficient between predicted and observed depths improves from 0.60 to 0.65. Moreover, nearly half of the deepest 10% residues in a protein sequence can be correctly predicted. Our study suggests that a residue's burying extent is able to be predicted, to some degree, by itself and its local neighbouring residues. The methods used to estimate the sequence-depth functions are expected to become more useful in the investigation of protein structures and folding mechanism.

Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions

“…The depth values are usually normalized using mean and standard deviation of the depth values in a large, pre-defined protein dataset as follows [62,63] normalized_depth = (depth -mean_depth) / standard_deviation_of_depth Details concerning calculation of various residue depth definitions are given in [63].…”

Prediction of One‐Dimensional Structural Properties Of Proteins by Integrated Neural Networks

“…Commonly used one -dimensional global structural properties are parameters that measure the solvent exposure of a residue, including normalized solvent accessible surface area (solvent accessibility) [181] , residue depth (the distance of a residue from the nearest solvent molecule) [182] , residue coordination or contact number (the number of residues within a cutoff distance) [183] , half -sphere exposure (orientation -dependent contact numbers) [184] , and recursive convex hull class [185] . While the methods for predicting residue depth [186,187] , coordination (or contact) numbers [9,188 -191] , halfsphere exposure [192] , and recursive convex hull class [185] emerged recently, solvent accessibility prediction has a relatively long history.…”

Protein Depth Calculation and the Use for Improving Accuracy of Protein Fold Recognition

“…The accuracy of the method is compromised since the calculated depth value depends on the positions and orientations of the water molecules. One can also calculate the depth by first generating the explicit solvent accessibility surface (e.g., by EDTSurf or Michel Sanner's Molecular Surface (MSMS) (Sanner et al, 1996)) and then identifying the vertex on the triangulated surface which is the closest one to the atom (Yuan and Wang, 2008;Zhang et al, 2008). However, the computation of this kind of method is quite time-consuming since all the atoms in the protein need to be searched against the huge number of vertices on the mesh surface.…”