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TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

Overview of attention for article published in Perspectives in Drug Discovery and Design, September 2017
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Title
TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers
Published in
Perspectives in Drug Discovery and Design, September 2017
DOI 10.1007/s10822-017-0047-0
Pubmed ID
Authors

Han Cao, Marcus C. K. Ng, Siti Azma Jusoh, Hio Kuan Tai, Shirley W. I. Siu

Abstract

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 33%
Student > Bachelor 1 17%
Student > Doctoral Student 1 17%
Unknown 2 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 17%
Computer Science 1 17%
Agricultural and Biological Sciences 1 17%
Social Sciences 1 17%
Unknown 2 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 December 2017.
All research outputs
#20,726,252
of 25,461,852 outputs
Outputs from Perspectives in Drug Discovery and Design
#817
of 949 outputs
Outputs of similar age
#251,690
of 324,678 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#10
of 16 outputs
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