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Targeting Neuroblastoma Cell Surface Proteins: Recommendations for Homology Modeling of hNET, ALK, and TrkB

Overview of attention for article published in Frontiers in Molecular Neuroscience, January 2017
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Title
Targeting Neuroblastoma Cell Surface Proteins: Recommendations for Homology Modeling of hNET, ALK, and TrkB
Published in
Frontiers in Molecular Neuroscience, January 2017
DOI 10.3389/fnmol.2017.00007
Pubmed ID
Authors

Yazan Haddad, Zbyněk Heger, Vojtech Adam

Abstract

Targeted therapy is a promising approach for treatment of neuroblastoma as evident from the large number of targeting agents employed in clinical practice today. In the absence of known crystal structures, researchers rely on homology modeling to construct template-based theoretical structures for drug design and testing. Here, we discuss three candidate cell surface proteins that are suitable for homology modeling: human norepinephrine transporter (hNET), anaplastic lymphoma kinase (ALK), and neurotrophic tyrosine kinase receptor 2 (NTRK2 or TrkB). When choosing templates, both sequence identity and structure quality are important for homology modeling and pose the first of many challenges in the modeling process. Homology modeling of hNET can be improved using template models of dopamine and serotonin transporters instead of the leucine transporter (LeuT). The extracellular domains of ALK and TrkB are yet to be exploited by homology modeling. There are several idiosyncrasies that require direct attention throughout the process of model construction, evaluation and refinement. Shifts/gaps in the alignment between the template and target, backbone outliers and side-chain rotamer outliers are among the main sources of physical errors in the structures. Low-conserved regions can be refined with loop modeling method. Residue hydrophobicity, accessibility to bound metals or glycosylation can aid in model refinement. We recommend resolving these idiosyncrasies as part of "good modeling practice" to obtain highest quality model. Decreasing physical errors in protein structures plays major role in the development of targeting agents and understanding of chemical interactions at the molecular level.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 20%
Researcher 4 20%
Student > Ph. D. Student 4 20%
Other 2 10%
Lecturer > Senior Lecturer 1 5%
Other 2 10%
Unknown 3 15%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 4 20%
Biochemistry, Genetics and Molecular Biology 3 15%
Chemistry 3 15%
Agricultural and Biological Sciences 2 10%
Computer Science 1 5%
Other 4 20%
Unknown 3 15%
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 21 January 2017.
All research outputs
#22,751,836
of 25,375,376 outputs
Outputs from Frontiers in Molecular Neuroscience
#2,845
of 3,329 outputs
Outputs of similar age
#367,082
of 429,791 outputs
Outputs of similar age from Frontiers in Molecular Neuroscience
#73
of 88 outputs
Altmetric has tracked 25,375,376 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,329 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 88 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.