Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Computational prediction and analysis of the DR6–NAPP interaction

View through CrossRef
AbstractAlzheimer's disease (AD) is a progressive neurodegenerative disorder that involves a devastating clinical course and that lacks an effective treatment. A biochemical model for neuronal development, recently proposed by Nikolaev et al., that may also have implications for AD, hinges on a novel protein–protein interaction between the death cell receptor 6 (DR6) ectodomain and an N‐terminal fragment of amyloid precursor protein (NAPP), specifically, the growth factor‐like domain of NAPP (GFD NAPP). Given all of this, we used a pure computational work‐flow to dock a binding competent homology model of the DR6 ectodomain to a binding competent crystal structure of GFD NAPP. The DR6 homology model was built according to a template supplied by the neurotrophin p75 receptor. The best docked model was selected according to an empirical estimate of the binding affinity and represents a high quality model of probable structural accuracy, especially with respect to the residue‐level contribution of GFD NAPP. The final model was tested and verified against a variety of biophysical and theoretical data sets. Particularly, worth noting is the excellent observed agreement between the theoretically calculated DR6–GFD NAPP binding free energy and the experimental quantity. The model is used to provide a satisfying structural and energetic interpretation of DR6–GFD NAPP binding and to suggest the possibility of and a mechanism for spontaneous apoptosis. The evidence suggests that the DR6–NAPP model proposed here is of probable accuracy and that it will prove useful in future studies, modeling work, and structure‐based AD drug design. Proteins 2011. © 2010 Wiley‐Liss, Inc.
Title: Computational prediction and analysis of the DR6–NAPP interaction
Description:
AbstractAlzheimer's disease (AD) is a progressive neurodegenerative disorder that involves a devastating clinical course and that lacks an effective treatment.
A biochemical model for neuronal development, recently proposed by Nikolaev et al.
, that may also have implications for AD, hinges on a novel protein–protein interaction between the death cell receptor 6 (DR6) ectodomain and an N‐terminal fragment of amyloid precursor protein (NAPP), specifically, the growth factor‐like domain of NAPP (GFD NAPP).
Given all of this, we used a pure computational work‐flow to dock a binding competent homology model of the DR6 ectodomain to a binding competent crystal structure of GFD NAPP.
The DR6 homology model was built according to a template supplied by the neurotrophin p75 receptor.
The best docked model was selected according to an empirical estimate of the binding affinity and represents a high quality model of probable structural accuracy, especially with respect to the residue‐level contribution of GFD NAPP.
The final model was tested and verified against a variety of biophysical and theoretical data sets.
Particularly, worth noting is the excellent observed agreement between the theoretically calculated DR6–GFD NAPP binding free energy and the experimental quantity.
The model is used to provide a satisfying structural and energetic interpretation of DR6–GFD NAPP binding and to suggest the possibility of and a mechanism for spontaneous apoptosis.
The evidence suggests that the DR6–NAPP model proposed here is of probable accuracy and that it will prove useful in future studies, modeling work, and structure‐based AD drug design.
Proteins 2011.
© 2010 Wiley‐Liss, Inc.

Related Results

Computational Prediction and Analysis of the NAPP - DR6 Interaction: Implications for Alzheimer's Research
Computational Prediction and Analysis of the NAPP - DR6 Interaction: Implications for Alzheimer's Research
AbstractAlzheimer's disease (AD) is a progressive neurodegenerative disorder that involves a devastating clinical course and lacks an effective treatment. A biochemical model for n...
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo
Abstract For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis c...
Improved Genomic Prediction Performance with Ensembles of Diverse Models
Improved Genomic Prediction Performance with Ensembles of Diverse Models
Abstract The improvement of selection accuracy of genomic prediction is a key factor in accelerating genetic gain for crop breeding. Traditionally, efforts have foc...
Identifikasi Batuan Pembentuk Air Asam Tambang Pada Pit Mayang Menggunakan Uji Statik di PT Menara Cipta Mulia
Identifikasi Batuan Pembentuk Air Asam Tambang Pada Pit Mayang Menggunakan Uji Statik di PT Menara Cipta Mulia
Mine Acid Water is one of the impacts of the mining industry that must be handled seriously because it can cause environmental quality degradation. The factor for the formation of ...
Computational Social Welfare: Applying Data Science in Social Work
Computational Social Welfare: Applying Data Science in Social Work
Computational social welfare, a powerful new science, combines a focal commitment to social justice and equity with adoption of computational modeling as an epistemological paradig...
Water position prediction with SE(3)-Graph Neural Network
Water position prediction with SE(3)-Graph Neural Network
Abstract Most protein molecules exist in a water medium and interact with numerous water molecules. Consideration of interactions between protein...
Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks
Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks
The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistas...
Apriori Algorithm-Based Three-Dimensional Mineral Prospectivity Mapping—An Example from Meiling South Area, Xinjiang, China
Apriori Algorithm-Based Three-Dimensional Mineral Prospectivity Mapping—An Example from Meiling South Area, Xinjiang, China
Mineral Prospectivity Mapping (MPM) is shifting toward intelligent deep mineralization searches in the era of big data and the increasing difficulties of surface deposit detection....

Back to Top