title: Fast and Accurate Calculation of Protein Depth by Euclidean Distance Transform.
abstract: The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation.
authors.affiliation: Bioinformatics & Systems Biology Program, Sanford-Burnham Medical Research Institute.
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan.
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.affiliation: Integration Application Center, Institute of Computing Technology, Chinese Academy of Sciences.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
keywords: Euclidean distance transform; fold recognition; molecular visualization; protein depth; protein tertiary structure; solvent accessibility