Evidence map›Paper›PMID 25035865›Full record

ArticleResearch in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- )2013

Fast and Accurate Calculation of Protein Depth by Euclidean Distance Transform.

Dong Xu, Hua Li, Yang Zhang

Abstract read
In one paragraph

Article in Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Discriminating changes in protein structure using tyrosine conjugation.Protein science : a publication of the Protein Society · 2020
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dong XuBioinformatics & Systems Biology Program, Sanford-Burnham Medical Research Institute.
Hua LiDepartment of Computational Medicine and Bioinformatics, University of Michigan.
Yang ZhangIntegration Application Center, Institute of Computing Technology, Chinese Academy of Sciences.

Funding

I-TASSER based atomic-level protein structure predictionR01GM083107 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZHANG, YANG · 2008 to 2019
$3.5M
Atomic-level, large-scale structure prediction of G protein-coupled receptorsR01GM084222 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZHANG, YANG · 2009 to 2012
$1.3M
NIGMS NIH HHS R01 GM083107NIGMS NIH HHS R01 GM084222
6 · The paper itself

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.

Indexed as

Euclidean distance transformfold recognitionmolecular visualizationprotein depthprotein tertiary structuresolvent accessibility

Identifiers

PMID25035865
PMCPMC4098708

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.