Homology Modeling: Methods and Protocols

Preface
Contents
Contributors
Chapter 1: Homology Modeling in the Twilight Zone: Improved Accuracy by Sequence Space Analysis
1 Introduction
2 Materials
2.1 Databases
2.2 Sequence Analysis
2.3 Template Mining
2.4 Secondary Structure Prediction
2.5 Automated Structure Prediction
2.6 Customized Structure Prediction
2.7 Model Validation
2.8 Graphical Analysis
3 Methods
3.1 Our Case Study
3.2 Template Search by PDB Mining
3.3 Template Search with LOMETS
3.4 Sequence Space Investigation
3.5 Template Analysis
3.6 Sequence Alignment
3.7 Loop Modeling
3.8 Model Building
3.9 Concluding Remarks
4 Notes
References
Chapter 2: Illuminating the ``Twilight Zone´´: Advances in Difficult Protein Modeling
1 Introduction
1.1 Protein Structure Determination
1.2 Prediction from First Principles
2 Template-Based Prediction
3 Advances in Low-Homology Modeling
3.1 Applications of Machine Learning
3.2 Inter-Residue Distance Predictions
3.3 Recent Improvements
4 Scoring Functions
4.1 Categories of Scoring Functions
4.2 Overview of Available Tools
4.3 The Most Recent Advances
4.4 Limitations
5 Conclusions
References
Chapter 3: Contact-Assisted Threading in Low-Homology Protein Modeling
1 Introduction
2 Materials
2.1 Template Library
2.2 Query and Template Feature Set
2.2.1 Sequence Profiles
2.2.2 Secondary Structures
2.2.3 Solvent Accessibility
2.2.4 Backbone Dihedral Angles
2.2.5 Additional Features
2.3 Threading Performance Measure
3 Methods
3.1 Overview of Protein Threading
3.1.1 Threading Scoring Function
3.1.2 Template Selection
3.1.3 Optimal Query-Template Alignment
3.2 Contact-Assisted Protein Threading
3.2.1 Residue-Residue Contact Map
3.2.2 Contact Map Alignment
3.3 Overview of Existing Contact-Assisted Threading Methods
3.3.1 Threading Methods That Implicitly Use Contact Information via Pairwise Contact Potential
3.3.2 Threading Methods That Explicitly Use Contact Information via Predicted Residue-Residue Contacts
3.4 Significance of Contact Maps Quality in Threading
3.5 Growth of Protein Sequence Databases and Its Implication in Threading
3.6 Discussion
4 Notes
References
Chapter 4: Omics and Remote Homology Integration to Decipher Protein Functionality
1 Evolution of Protein Structure Modeling
2 Comparative Genomics Among Other ``Omics´´
3 Natural Selection
4 Scientific Advances Boosted by Comparative Genomics: Protein Structures Integration
4.1 Insights in Cancer, Longevity, and Immunity Field
4.2 Insights in Mitochondrial Function and Related Diseases
4.3 Insights in Venom Proteins and Drug Design
5 Revolutionary Use of Homology Prediction in Biotechnology: Biosensors and Recombinant Proteins
6 Conclusion
References
Chapter 5: Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates
1 Introduction
2 Conformational Diversity
3 High-Sequence Identity Does Not Guarantee an Accurate Model
3.1 A Single Amino Acid Change (Minimal Sequence Changes) May Produce a Huge Conformational Change
3.2 Candidate Templates with Conformational Diversity
3.3 Structural Divergence Within a Protein Family
4 Does My Query Protein Have Conformational Diversity?
4.1 Modeling Different Conformations of a Protein
4.2 When Do I Need to Model Multiple Conformations?
5 Conclusions
6 Notes
References
Chapter 6: Quality Estimates for 3D Protein Models
1 Introduction
2 Estimates of Model Accuracy (EMA) Are Essential for Template-Based Modeling (TBM) and Template-Free Modeling (FM)
3 Methods for Estimates of Model Accuracy
4 Observed Model Accuracy Scoring
5 EMA Classification
6 ModFOLD: A Leading EMA Web Server
6.1 ModFOLD History
6.1.1 The Initial Construction of ModFOLD
6.1.2 ModFOLDclustQ for Speed, Accuracy, and Consistency
6.1.3 The Quasi-Single-Model Approach
6.2 Latest Versions of ModFOLD
7 EMA in Community-Wide Experiments
8 Recent Advances in EMA Methods
References
Chapter 7: Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy
1 Introduction
1.1 The Local Quality Estimation of 3D Models
1.2 The Refinement of the Predicted 3D Models
1.3 The ReFOLD Server
2 Materials
3 Methods
3.1 The Performance of the Local Quality Assessment Guided MD-Based Protocol
3.1.1 ModFOLD6 in the Refinement Pipeline
4 Notes
5 Addendum
References
Chapter 8: Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling
1 Introduction
2 Materials
3 Methods
3.1 Template Search and Identification
3.2 Sequence and Structural Alignments
3.3 Multi-Template Homology Modeling with RosettaCM
3.4 High-Resolution Refinement
3.5 Preparation of the Ligand
3.6 Ligand Docking Using RosettaLigand
4 Notes
References
Chapter 9: Homology Modeling of the G Protein-Coupled Receptors
1 Introduction
1.1 G Protein-Coupled Receptors
1.2 Homology Modeling
1.3 Sequence Alignment
2 Methods. Where to Start?
2.1 Template Coverage
2.2 Where to Get the Template from?
2.3 Model Refinement
3 Conclusions
4 Notes
References
Chapter 10: Modeling of Olfactory Receptors
1 Introduction
2 Materials
2.1 Sequence Comparison and Alignment
2.2 3D Structure Building
2.3 Ligand Docking
2.4 Membrane Embedding
2.5 Molecular Dynamics
3 Methods
3.1 Collection of the Templates
3.2 Structure-Based Sequences Alignment
3.3 Construction of the Model
3.4 3D Model Analysis and Validation
3.5 Building the Complexes
3.6 Membrane Embedding
3.7 Molecular Dynamics Simulation
4 Notes
References
Chapter 11: Analyses of Mutation Displacements from Homology Models
1 Introduction
1.1 Which Mutations Are We Observing?
2 Effects of Mutations
2.1 Effect on the Function
2.2 Global Structural Effect
2.3 Side Chain and Stability Effect
2.4 Backbone Effect
3 Methods
3.1 The Three Mutants
3.2 Homology Model of the Wild Type
3.3 FoldX and Missense3D Models and Analyses
3.4 Dynamut and Rosetta Backrub Analyses
4 Conclusion
References
Chapter 12: Persistent Homology for RNA Data Analysis
1 Introduction
2 Method
2.1 Topological Representations for RNA
2.1.1 Simplicial Complex
2.1.2 Hypergraph
2.2 Persistent Homology for RNA Data Analysis
2.2.1 Persistent Homology
Homology
Vietoris-Rips Complex and Filtration
Persistent Homology
2.2.2 Persistent Homology Based Models and Functions
Persistent Betti Number
Persistent Entropy
Persistent Similarity
2.2.3 Weighted Persistent Homology for RNA Representation
Physics-Aware WPH Models
Element-Specific WPH
2.3 Persistent Spectral Theory for RNA Data Analysis
2.3.1 Spectral Graph
2.3.2 Spectral Simplicial Complex
2.3.3 RNA-Based Persistent Spectral Models
2.4 Persistent Models Based Machine Learning Models for RNA Data Analysis
3 Notes
References
Chapter 13: Computational Methods to Predict Intrinsically Disordered Regions and Functional Regions in Them
1 Introduction
2 IDR Prediction Methods
2.1 Scoring Function Methods
2.1.1 Uversky Plot and FoldIndex
2.1.2 IUpred
2.2 Machine Learning Methods
2.2.1 DISOPRED
2.2.2 SPOT-Disorder
2.2.3 PONDR
2.3 Consensus Methods
2.3.1 MobiDB-lite
2.3.2 MetaDisorder
3 Case Study
3.1 NeProc
3.2 Prediction by NeProc
References
Chapter 14: Homology Modeling of Transporter Proteins
1 Introduction
2 Materials
3 Methods
3.1 Template Identification and Selection
3.2 Target-Template Alignments
3.3 Model Building and Refinements
3.4 Model Validation
4 Notes
References
Chapter 15: Modeling of SARS-CoV-2 Virus Proteins: Implications on Its Proteome
Abbreviations
1 Introduction
2 Protein Modeling
2.1 Template-Based Structure Prediction
2.2 Ab Initio Structure Prediction
3 The SARS-CoV-2 Proteome
3.1 Nonstructural Proteins (Nsp)
3.2 Structural Proteins
3.3 Accessory Proteins
4 Models of Important SARS-CoV-2 Viral Proteins
4.1 Homology Modeling of Proteins Where High-Resolution Experimental Structures Are Available: Comparison of Homology Models w...
4.1.1 Nsp1
4.1.2 3CL-pro (Nsp5)
4.1.3 S Protein
4.1.4 N Protein
4.1.5 RdRp (Nsp12)
4.2 Homology Modeling of Envelope (E) Protein Where High-Resolution Structure Is Unavailable: Comparison Between Different Hom...
4.3 Ab Initio Protein Modeling of Membrane Protein (M) Where No Experimental Structure Is Available
5 Functional Implications of Protein Modeling
5.1 Protein-Protein Interactions
5.2 Understanding the Functionality of Proteins
5.3 Binding Site Predictions
5.4 Molecular Docking
5.4.1 ATP Binding Sites on 3CL-pro
5.4.2 Nsp7-nsp8 Primase Complex
5.5 Insights into Viral Replication Machinery
5.5.1 Nsp7-Nsp8-Nsp12 Replication Machinery
6 Summary of Methods
6.1 Steps of Homology Modeling
6.2 ATPbind Steps
6.3 Molecular Docking of ATP and 3CL-pro
References
Chapter 16: Homology Modeling of Antibody Variable Regions: Methods and Applications
1 Introduction
1.1 Antibody Structure
1.2 Antibody Variable Region
1.3 Homology Modeling of Antibody Variable Region
2 Materials and Methods
2.1 Identification of CDRs and FRs in the Input Sequences
2.2 Identification and Selection of Template Structures for CDRs and FRs
2.3 Optimization of the Initial VL and VH Orientation
2.4 Grafting of CDR Templates, Building CDR H3, and Assembling Initial Model
2.5 Side-Chain Optimization and Final Refinement
3 Applications and Further Developments
4 Notes
References
Chapter 17: 3D-BMPP: 3D Beta-Barrel Membrane Protein Predictor
1 Introduction
2 Materials
2.1 Additional Equipment
2.2 Equipment Setup
3 Methods
4 Conclusion
5 Notes
References
Chapter 18: Protein Homology Modeling for Effective Drug Design
1 Introduction
2 Materials and Methods
2.1 The Template Selection
2.2 The Sequence Alignment and Sources of Inaccuracies in the Models
2.3 Model Building and Quality Checking
2.4 Case of Modeling the Bacterial Enzyme OatA with Shallow Binding Site for Drug Design
3 Notes
References
Chapter 19: Specificities of Protein Homology Modeling for Allosteric Drug Design
1 Introduction
1.1 Allosteric Effects
1.2 Classification of Allosteric Ligands
2 Materials and Methods
2.1 Modeling of N-Terminal Part of CB1 Receptor
2.2 Ligand-Guided Modeling of the Allosteric Site in GABA Receptor
2.3 Homology Modeling of Potassium Channels
3 Notes
References
Chapter 20: Modeling of Protein Complexes
1 Introduction
2 Materials
2.1 Resources for Identification of Structural Homologs and Domain Boundaries
2.2 Resources for Generation of Homology Models
2.3 Resources for Evaluation of Protein Contact Interfaces
2.4 Resources to Compute Docking Models of Protein-Protein Interactions
2.5 Resources to Integrate Multiple Experimental and Computational Data into Molecular Models
2.6 Resources for Model Building, Visualization, and Optimization
2.7 Resources for Model Validation
3 Methods
3.1 Generate Homology Models of Individual Proteins
3.2 Find Reliable Data About Interaction Surfaces
3.3 Combine Homology Models and Contact Interface Information to Generate the Complex Model
3.4 Postprocessing and Validation: Model Adjustment and Final Evaluation
3.5 Final Considerations and Take-Home Messages
4 Notes
References
Index