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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Structural modelling pipelines in next generation sequencing projects.

Jonathan G L Mullins¹

¹Genome and Structural Bioinformatics, Institute of Life Science, College of Medicine, Swansea University, Singleton Park, Swansea, Wales, UK. j.g.l.mullins@swansea.ac.uk

Advances in Protein Chemistry and Structural Biology

|October 11, 2012

Summary

This summary is machine-generated.

Predicting protein structure from sequence is improving with new methods and data. This aids in assessing the impact of genetic variations (nsSNPs) on protein function and disease.

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Area of Science:

* Computational Biology
* Structural Bioinformatics
* Genomics

Background:

* Advances in structural genomics and modeling methodologies are enhancing protein structure prediction from amino acid sequences.
* Template-based (homology) and de novo modeling techniques are increasingly integrated to improve coverage and accuracy.
* Automated structural modeling pipelines are crucial for analyzing large-scale protein sequence datasets.

Purpose of the Study:

* To discuss the challenges and applications of predicting protein structure and the functional impact of nonsynonymous single nucleotide polymorphisms (nsSNPs).
* To explore the differential challenges in assessing nsSNP impact across various protein functional classes.
* To highlight the potential of mapping predicted SNP impacts onto protein-protein interaction networks for disease research.

Main Methods:

* Integration of template-based (homology) and de novo protein modeling approaches.
* Application of automated structural modeling pipelines to large protein sequence datasets.
* Analysis of protein structure prediction challenges across diverse protein functional classes (globular proteins, membrane proteins, ion channels).

Main Results:

* Improved protein structure prediction accuracy enables routine assessment of nsSNP functional impact.
* Predictive approaches for nsSNPs are being developed for next-generation sequencing data.
* Challenges in assessing nsSNP impact vary significantly depending on protein type.

Conclusions:

* Accurate protein structure prediction is vital for understanding genetic variation effects.
* Mapping predicted SNP impacts onto interaction networks offers new avenues for studying complex polygenic disorders.
* This research facilitates a deeper understanding of disease predisposition through structural and functional analysis of genetic variations.