Description
Taylor & Francis Ltd Structural Bioinformatics 2008 Edition by Forbes J. Burkowski
The Beauty of Protein Structures and the Mathematics behind Structural BioinformaticsProviding 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 BiologyFollowing 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 ScienceConnecting 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). PrefaceThe Study of Structural Bioinformatics MotivationSmall BeginningsStructural Bioinformatics and the Scientific MethodA More Detailed Problem Analysis: Force FieldsModeling IssuesSources of ErrorSummaryIntroduction to Macromolecular Structure Motivation Overview of Protein StructureOverview of RNA StructureData Sources, Formats, and Applications Motivation Sources of Structural DataPDB File FormatVisualization of Molecular DataSoftware for Structural BioinformaticsDynamic Programming Motivation IntroductionA DP Example: The Al Gore Rhythm for Giving TalksA Recipe for Dynamic ProgrammingLongest Common SubsequenceRNA Secondary Structure PredictionMotivation Introduction to the ProblemThe Nussinov Dynamic ProgrammingThe MFOLD Algorithm: TerminologyProtein Sequence Alignment Protein HomologyVariations in the Global Alignment AlgorithmThe Significance of a Global AlignmentLocal AlignmentProtein GeometryIntroductionCalculations Related to Protein GeometryRamachandran PlotsInertial AxesCoordinate TransformationsMotivation IntroductionTranslation TransformationsRotation TransformationsIsometric TransformationsStructure Comparison, Alignment, and SuperpositionMotivation IntroductionTechniques for Structural ComparisonScoring Similarities and Optimizing ScoresSuperposition AlgorithmsAlgorithms Comparing Relationships within a Protein Machine LearningMotivation Issues of ComplexityPrediction via Machine LearningData Used during Training and TestingObjectives of the Learning Algorithm Linear RegressionRidge RegressionPreamble for Kernel MethodsKernel FunctionsClassificationHeuristics for ClassificationNearest Neighbor ClassificationSupport Vector MachinesLinearly Nonseparable DataSupport Vector Machines and KernelsExpected Test ErrorTransparencyOverview of the AppendicesIndex