Targeted metabolic engineering guided by computational analysis of single-nucleotide polymorphisms (SNPs)

D B R K Gupta Udatha; Simon Rasmussen; Thomas Sicheritz-Pontén; Gianni Panagiotou

doi:10.1007/978-1-62703-299-5_20

Targeted metabolic engineering guided by computational analysis of single-nucleotide polymorphisms (SNPs)

D B R K Gupta Udatha, Simon Rasmussen, Thomas Sicheritz-Pontén, Gianni Panagiotou

Research output: Contribution to journal › Journal article › Research › peer-review

1 Citation (Scopus)

Abstract

The non-synonymous SNPs, the so-called non-silent SNPs, which are single-nucleotide variations in the coding regions that give "birth" to amino acid mutations, are often involved in the modulation of protein function. Understanding the effect of individual amino acid mutations on a protein/enzyme function or stability is useful for altering its properties for a wide variety of engineering studies. Since measuring the effects of amino acid mutations experimentally is a laborious process, a variety of computational methods have been discussed here that aid to extract direct genotype to phenotype information.

Original language	English
Journal	Methods in molecular biology (Clifton, N.J.)
Volume	985
Pages (from-to)	409-28
Number of pages	20
ISSN	1064-3745
DOIs	https://doi.org/10.1007/978-1-62703-299-5_20
Publication status	Published - 2013
Externally published	Yes

Keywords

Amino Acid Substitution
Computational Biology
Genome
Genotyping Techniques
Metabolic Engineering
Models, Biological
Polymorphism, Single Nucleotide
Protein Stability
Saccharomyces cerevisiae/genetics
Saccharomyces cerevisiae Proteins/chemistry
Sequence Alignment
Sequence Analysis, DNA

Access to Document

10.1007/978-1-62703-299-5_20

Cite this

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title = "Targeted metabolic engineering guided by computational analysis of single-nucleotide polymorphisms (SNPs)",

abstract = "The non-synonymous SNPs, the so-called non-silent SNPs, which are single-nucleotide variations in the coding regions that give {"}birth{"} to amino acid mutations, are often involved in the modulation of protein function. Understanding the effect of individual amino acid mutations on a protein/enzyme function or stability is useful for altering its properties for a wide variety of engineering studies. Since measuring the effects of amino acid mutations experimentally is a laborious process, a variety of computational methods have been discussed here that aid to extract direct genotype to phenotype information.",

keywords = "Amino Acid Substitution, Computational Biology, Genome, Genotyping Techniques, Metabolic Engineering, Models, Biological, Polymorphism, Single Nucleotide, Protein Stability, Saccharomyces cerevisiae/genetics, Saccharomyces cerevisiae Proteins/chemistry, Sequence Alignment, Sequence Analysis, DNA",

author = "Udatha, {D B R K Gupta} and Simon Rasmussen and Thomas Sicheritz-Pont{\'e}n and Gianni Panagiotou",

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language = "English",

volume = "985",

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journal = "Methods in Molecular Biology",

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T1 - Targeted metabolic engineering guided by computational analysis of single-nucleotide polymorphisms (SNPs)

AU - Udatha, D B R K Gupta

AU - Rasmussen, Simon

AU - Sicheritz-Pontén, Thomas

AU - Panagiotou, Gianni

PY - 2013

Y1 - 2013

N2 - The non-synonymous SNPs, the so-called non-silent SNPs, which are single-nucleotide variations in the coding regions that give "birth" to amino acid mutations, are often involved in the modulation of protein function. Understanding the effect of individual amino acid mutations on a protein/enzyme function or stability is useful for altering its properties for a wide variety of engineering studies. Since measuring the effects of amino acid mutations experimentally is a laborious process, a variety of computational methods have been discussed here that aid to extract direct genotype to phenotype information.

AB - The non-synonymous SNPs, the so-called non-silent SNPs, which are single-nucleotide variations in the coding regions that give "birth" to amino acid mutations, are often involved in the modulation of protein function. Understanding the effect of individual amino acid mutations on a protein/enzyme function or stability is useful for altering its properties for a wide variety of engineering studies. Since measuring the effects of amino acid mutations experimentally is a laborious process, a variety of computational methods have been discussed here that aid to extract direct genotype to phenotype information.

KW - Amino Acid Substitution

KW - Computational Biology

KW - Genome

KW - Genotyping Techniques

KW - Metabolic Engineering

KW - Models, Biological

KW - Polymorphism, Single Nucleotide

KW - Protein Stability

KW - Saccharomyces cerevisiae/genetics

KW - Saccharomyces cerevisiae Proteins/chemistry

KW - Sequence Alignment

KW - Sequence Analysis, DNA

U2 - 10.1007/978-1-62703-299-5_20

DO - 10.1007/978-1-62703-299-5_20

M3 - Journal article

C2 - 23417815

VL - 985

SP - 409

EP - 428

JO - Methods in Molecular Biology

JF - Methods in Molecular Biology

SN - 1064-3745

ER -