Note: Intro -- Contents -- Part I Introduction and Historical Background -- 1 Overall Introduction and Rationale, with View from Computational Biology -- 1.1 Introduction -- 1.2 Analysis of Genome Information to Obtain Structural Information -- 1.3 Integration of Various Methods to Build the Atomic or Semi-atomic Resolution Models -- 1.4 Analysis of Dynamic Natures of Complex Structures -- 1.5 Conclusion -- References -- 2 Integrative/Hybrid Methods Structural Biology: Role of Macromolecular Crystallography -- 2.1 Introduction -- 2.2 MX Structure Data for I/H Methods -- 2.3 Accessing Public-Domain Experimental and Computational Structure Data for I/H Methods -- References -- 3 View from Nuclear Magnetic Resonance Spectroscopy -- References -- Part II New Experimental Tools Enabling Hybrid Methods -- 4 Complementary Use of Electron Cryomicroscopy and X-Ray Crystallography: Structural Studies of Actin and Actomyosin Filaments -- 4.1 Introduction -- 4.2 Power of cryoEM Image Analysis in the Past and Present -- 4.3 Structural Study of F-Actin -- 4.4 Structural Study of Skeletal Muscle Actomyosin Rigor Complex -- References -- 5 Current Solution NMR Techniques for Structure-Function Studies of Proteins and RNA Molecules -- 5.1 Introduction -- 5.2 Sample Preparation and Isotope Labeling -- 5.3 NMR Data Collection -- 5.4 NMR Observables Used in Structure Determination -- 5.5 Software for Data Analysis and Assignment -- 5.6 Structure Determination -- 5.7 Hybrid Approaches with NMR -- 5.8 Validation of NMR Data -- 5.9 Use of NMR for Dynamics and Functional Studies -- 5.10 Data Handling and Deposition -- References -- 6 The PA Tag: A Versatile Peptide Tagging System in the Era of Integrative Structural Biology -- 6.1 Introduction -- 6.2 Protein Purification and Biochemical Analyses -- 6.2.1 Overview of Tag-Based Affinity Purification Systems. , 6.2.2 Development of the PA Tag System -- 6.2.3 Crystal Structure of PA Peptide Bound to NZ-1 Fab -- 6.2.4 PA Tag as a "Mobile Epitope" -- 6.2.5 Monitioring and Controling Conformational Change -- 6.3 Protein Labeling in EM Studies -- 6.3.1 Demands for EM Labeling Technologies -- 6.3.2 Presently Available EM Tags and Labeling Strategies -- 6.3.3 PA Tag as an EM Label -- 6.4 Concluding Remarks -- References -- 7 Small Angle Scattering and Structural Biology: Data Quality and Model Validation -- 7.1 Introduction -- 7.2 Current State of the Art -- 7.2.1 Sample Preparation and Instrumentation -- 7.2.2 Data Reduction and Error Propagation -- 7.2.3 Data Analysis and Validation -- 7.2.4 3D Structural Modelling and Model Validation -- 7.3 Areas for Further Research -- 7.4 Standards and Publication Guidelines -- 7.4.1 Establishing Guidelines Through Community Engagement -- 7.4.2 Archiving SAS Data and Hybrid Models -- 7.5 Conclusion -- References -- 8 Structural Investigation of Proteins and Protein Complexes by Chemical Cross-Linking/Mass Spectrometry -- 8.1 Introduction -- 8.2 The Cross-Linking/MS Strategy -- 8.3 Experimental Design of the Cross-Linking/MS Workflow -- 8.3.1 Cross-Linker Design and Reactivity -- 8.3.2 Identification of Cross-Linked Peptides -- 8.3.3 Facilitated Analysis of Cross-Linked Products -- 8.4 Structural Investigation of Purified Proteins and Large Protein Assemblies -- 8.5 Identification of Protein-Protein Interaction Networks -- 8.6 Conclusion -- References -- 9 Prediction of Structures and Interactions from GenomeInformation -- 9.1 Introduction -- 9.2 Statistical Methods to Extract Causative Interactions Between Sites -- 9.2.1 Direct Coupling Analysis for Amino Acid Covariations Between Sites in a Multiple Sequence Alignment -- 9.2.1.1 Maximum Entropy Model for the Distribution of Protein Sequences. , 9.2.1.2 Log-Likelihood and Log-Posterior-Probability -- 9.2.1.3 Inverse Potts Model -- 9.2.2 Partial Correlation of Amino Acid Cosubstitutions Between Sites at Each Branch of a Phylogenetic Tree -- 9.3 Machine Learning Methods to Augment the Contact Prediction Accuracy Based on Amino Acid Coevolution -- 9.4 Performance of Contact Prediction -- 9.4.1 MSA Dependence of Contact Prediction Accuracy -- 9.5 Contact-Guided de novo Protein Structure Prediction -- 9.5.1 How Many Predicted Contacts Should Be Used to Build 3D Models? -- 9.6 Evolutionary Direct Couplings Between Residues Not Contacting in a Protein 3D Structure -- 9.6.1 Structural Variation Including Conformational Changes -- 9.6.2 Homo-Oligomer Contacts -- 9.6.3 Residue Couplings Mediated by Binding to a Third Agent -- 9.7 Heterogeneous Protein-Protein Contacts -- 9.8 Discussion -- Appendix -- Inverse Potts Model -- A Gauge Employed for hi(ak) and Jij(ak,al) -- Boltzmann Machine -- Gaussian Approximation for P(σ) with a Normal-Inverse-Wishart Prior -- Pseudo-likelihood Approximation -- Symmetric Pseudo-likelihood Maximization -- Asymmetric Pseudo-likelihood Maximization -- ACE (Adaptive Cluster Expansion) of Cross-Entropy for Sparse Markov Random Field -- Scoring Methods for Contact Prediction -- References -- 10 A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary CouplingSequence Data -- 10.1 Introduction -- 10.2 The EC-NMR Algorithm -- 10.3 EC-NMR Results -- 10.4 Sensitivity to Numbers of Sequence Homologs in Multiple Sequence Alignment -- 10.5 Conclusions and Future Prospects -- References -- 11 Harnessing the Combined Power of SAXS and NMR -- References -- 12 2DHybrid Analysis -- 12.1 Introduction -- 12.2 Methodological Overview -- 12.2.1 Overview of the Computation -- 12.2.2 Construction of the Elastic Network Model. , 12.2.3 Deformation of Atomic Models -- 12.2.4 The Simple Projection Model -- 12.2.5 Contacts with Support Film -- 12.2.6 The Negative-Stain Model -- 12.2.7 Strategy for Selecting the Best-Fitting Atomic Model -- 12.2.8 Expression and Purification of Integrins -- 12.2.9 Electron Microscopy and Image Processing -- 12.3 Application of the 2D Hybrid Analysis -- 12.3.1 Electron Microscopyimages of Integrins in Ca2+ Solution -- 12.3.2 Improperly Averaged Electron Microscopyimages -- 12.4 Concluding Remarks -- References -- Part III New Computational Tools Enabling Hybrid Methods -- 13 Hybrid Methods for Macromolecular Modeling by Molecular Mechanics Simulations with Experimental Data -- 13.1 Hybrid Approach for Structure Modeling from Low Resolution, Low Information Experimental Data -- 13.2 Why Flexible Fittings? -- 13.3 Strategy Used for Flexible Fitting -- 13.4 Algorithms to Generate Candidate Structures -- 13.5 Quantification of Structure-Data Agreement -- 13.6 Flexible Fitting Against EM Data Using Elastic Network Normal Mode Analysis -- 13.7 Flexible Fitting with Molecular Dynamics -- 13.8 Dynamics Extraction from 2D Image Set -- 13.9 Flexible Fitting with SAXS Data -- 13.10 Summary and Conclusions -- References -- 14 Rigid-Body Fitting of Atomic Models on 3D Density Maps of Electron Microscopy -- 14.1 Introduction -- 14.2 Statistics of Modeling Software -- 14.3 Type of Fitting Problem -- 14.4 Molecular Shape Representation -- 14.5 Tools and Programs for Rigid Body Fitting -- 14.5.1 UCSF Chimera -- 14.5.2 Situs -- 14.5.3 Integrative Modeling Platform (IMP) -- 14.5.4 Fitting Using a Gaussian Mixture Model -- 14.6 Concluding Remarks -- References -- 15 Hybrid Methods for Modeling Protein Structures Using Molecular Dynamics Simulations and Small-Angle X-Ray Scattering Data -- 15.1 Introduction for Small-Angle X-Ray Scattering. , 15.2 Overview of Computational Methods for Modeling Protein Structures Using Small-Angle X-Ray Scattering Data -- 15.3 Applications of the Hybrid Method of Molecular Dynamics Simulations and Small-Angle X-Ray Scattering -- 15.3.1 Investigation of Intrinsic Dynamics of EcoO109I and Extensions of MD-SAXS Methods -- 15.3.2 Structural Investigation of the Vitamin D Receptor Ligand-Binding Domain -- 15.4 Conclusion -- References -- Part IV Data Validation and Archives for Hybrid Methods -- 16 Archiving of Integrative Structural Models -- 16.1 Introduction -- 16.2 The Structural Biology Federation -- 16.3 Creation of Data Standards -- 16.4 Methods for Data Exchange -- 16.5 Conclusion -- References.