BookPandjassarame Kangueane.
Summary: Bioinformation Discovery illustrates the power of biological data in knowledge discovery. It describes biological data types and representations with examples for creating a workflow in bioinformation discovery. Concepts are illustrated using line diagrams.
Contents:
Intro; Dedication; Preface; Acknowledgments; Abbreviations; Contents; List of Figures; List of Tables; About the Author;
Chapter 1: Bioinformatics for Bioinformation; 1.1 Bioinformatics; 1.2 Bioinformatics-Related Terms; 1.3 Some Journals Supporting Bioinformatics; 1.4 Bioinformatics in Drug Discovery; 1.5 Skills for Bioinformatics; 1.5.1 UNIX Commands for Bioinformation Discovery; 1.5.2 Mathematics of Bioinformatics; 1.6 Bioinformatics Warehousing in Drug Discovery; 1.7 Bioinformatics Components; 1.8 Bioinformation; 1.9 Bioinformatics Variables; 1.10 Cell Constituents; 1.10.1 Nucleic Acids. 1.10.2 Proteins1.10.3 Classification of Amino Acids; 1.11 Codon and Codon Usage Table; 1.12 Bioinformation Discovery; 1.13 Bioinformatics Principle; 1.14 Bioinformatics Challenges; 1.15 Biological Data; 1.16 Data Explosion; 1.17 Sequence Data; 1.18 Structure Data; 1.19 Small Molecules; 1.20 Macromolecules; 1.21 SCOP Dataset; 1.22 CATH Dataset; 1.23 Functional Data; 1.24 Pathway Data; 1.25 Bioinformatics Developments; 1.26 Discovery Environment; 1.27 Sequence, Structure Alignment, and Evolutionary Inferences; 1.27.1 Sequence Alignment; 1.28 Molecular Modeling; 1.28.1 Protein Modeling. 1.28.2 Methods of Protein Modeling1.28.3 Popular Force Fields for Molecular Mechanics; 1.28.4 Prediction of Protein Structure; 1.28.5 Caveats on Homology Modeling; 1.29 Molecular Docking; 1.30 Phylogenetic Analysis; 1.31 Exercises;
Chapter 2: Creating Datasets for Bioinformation; 2.1 Datasets; 2.2 HLA Binding Peptide Dataset; 2.3 MHC-Peptide Structural Dataset; 2.4 Grouping of MHC-Peptide Structures; 2.5 PDB Chain Identifier; 2.6 Information Redundancy in Dataset; 2.7 Information from MHC-Peptide Data; 2.8 Structural Parameters for MHC-Peptide Dataset Analysis. 2.9 Creation of Heterodimer and Homodimer Dataset2.10 Homodimer Folding Dataset; 2.11 Intronless Genes Dataset; 2.12 Human Single Exon Gene (SEG) Dataset; 2.13 Intron Containing Genes Dataset; 2.14 Fusion Protein Dataset; 2.15 Cholera Toxin Dataset; 2.16 HIV-1 GP160 (GP120/GP40) Structures; 2.17 Biological Data to Knowledge; 2.18 Exercises; References;
Chapter 3: Tools and Techniques; 3.1 ALIGN; 3.2 BIMAS; 3.3 BLAST; 3.4 CLUSTALW; 3.5 DeCypher; 3.6 DEEP VIEW; 3.7 FASTA; 3.8 INSIGHT II; 3.9 GENSCAN; 3.10 GROMOS; 3.11 HBPLUS; 3.12 LALIGN/PLALIGN; 3.13 LIGPLOT; 3.14 LOOK; 3.15 MODELLER. 3.16 NACCESS3.17 PHYLIP; 3.18 PROTPARAM; 3.19 PROTORP; 3.20 PSAP; 3.21 InterPro; 3.22 PYMOL; 3.23 RASMOL; 3.24 ROSETTA Design; 3.25 SURFNET; 3.26 SYBYL; 3.27 T-EPITOPE DESIGNER; 3.28 Exercises; References;
Chapter 4: Protein-Protein Interaction; 4.1 Protein Subunit Interaction; 4.2 Protein Dimer Datasets in Literature; 4.3 Parameters in Subunit Interaction; 4.3.1 Hydrophobic Effect; 4.3.2 Interface Size; 4.3.3 Interface Residues; 4.3.4 Interface H-Bonds; 4.3.5 Interface Electrostatics; 4.3.6 Interface Sidechain-Sidechain Interaction; 4.3.7 Interface Hot Spots; 4.4 Conclusion; 4.5 Exercise.