Structural Bioinformatics

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A01=Forbes J. Burkowski
ACA snoRNP
alpha
Alpha Carbon
Alpha Carbon Atoms
Alpha Helices
angle
atoms
Author_Forbes J. Burkowski
Base Pairing
beta
Beta Sheet
Beta Strand
Bone Morphogenetic Protein Receptor Type-2
carbon
Category=PS
Category=UY
coordinate transformation algorithms
dihedral
Dihedral Angle
Dot Plot
dynamic
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Execution Time
Global Alignment
Hairpin
Hairpin Loop
Linear Regression
Longest Common Subsequence
machine learning in drug design
molecular geometry analysis
Optimal Global Alignment
PDB Code
programming
protein
protein superposition methods
Ramachandran Plot
RNA Secondary Structure
RNA Secondary Structure Prediction
RNA structure prediction
Rotation Matrix
sequence alignment techniques
Smile String
strand
Structural Bioinformatics
UCSF Chimera
undergraduate bioinformatics course guide

Product details

  • ISBN 9781584886839
  • Weight: 771g
  • Dimensions: 152 x 229mm
  • Publication Date: 30 Oct 2008
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics
Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure.

Helps Students Go Further in Their Study of Structural Biology
Following some introductory material in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design.

At the Crossroads of Biology, Mathematics, and Computer Science
Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).

University of Waterloo, Ontario, Canada

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