## Structural bioinformatics: an algorithmic approachThe Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approachshows 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). |

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### Contents

The Study of Structural Bioinformatics | 1 |

REFERENCES | 36 |

REFERENCES | 80 |

Copyright | |

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algorithm alpha carbon alpha helices amino acids analysis assume atoms axis backbone base pair beta sheet beta strands binding Biology calculations cell Chapter classification color insert following computation configuration conformation consider coordinates define derive described diagonal diagonalizable dihedral angle distance domain dot plot dynamic programming eigenvalues eigenvectors entries equation evaluation evolutionary example feature space Figure folding following page 72 fragment function given hairpin helices helix hydrogen bonds hydrophobic hypothesis inner product interactions kernel learning algorithm linear mathematical model minimize molecular molecule multiple myoglobin natural process Note nucleotides observed optimal global alignment orthogonal parameters PDB code PDB file prediction predictor primary sequence problem protein structure pseudoknots Ramachandran plot recursion represent residues RNA secondary structure rotation matrix Science score secondary structure sequence alignment similar specified stem strategy structural alignment structural bioinformatics structure comparison subproblem support vectors symmetric tertiary structure Theorem tion trace-back training set typically values various