Computer-Aided Antibody Design

Preface
Contents
Contributors
Part I: Information of Antibody Sequences and Structures
Chapter 1: Antibody Sequence and Structure Analyses Using IMGT: 30 Years of Immunoinformatics
1 Introduction
2 IMGT-ONTOLOGY and IMGT Scientific Chart Rules
3 IMGT, the International ImMunoGeneTics Information System
3.1 IMGT Overview
3.2 IMGT Databases
3.2.1 IMGT/LIGM-DB
3.2.2 IMGT/GENE-DB
3.2.3 IMGT/2Dstructure-DB and IMGT/3Dstructure-DB
3.2.4 IMGT/mAb-DB
3.3 Nucleotide Sequence Analysis Tools
3.3.1 IMGT/V-QUEST and IMGT/JunctionAnalysis
3.3.2 IMGT/HighV-QUEST
3.3.3 IMGT Reference Directories
3.3.4 Gene Analysis Tool
3.3.5 Amino Acid and Structure Analysis Tools
4 IMGT Gene and Allele Nomenclature
5 IMGT Unique Numbering and IMGT Colliers de Perles
5.1 IMGT Unique Numbering for V and C Domains
5.2 IMGT Collier de Perles
6 IMGT/DomainGapAlign
7 IMGT/2Dstructure-DB
8 IMGT/3Dstructure-DB
8.1 IMGT/3Dstructure-DB Card
8.2 IMGT/3Dstructure-DB Contact Analysis
8.3 IMGT Paratope and Epitope
8.4 Contact Analysis of TR-Mimic Antibodies and TR Targeting Peptide-HLA
9 Allotypes of the Human IG Chains
10 IGHG Engineered Variants and Effector Properties
11 Availability and Citation
12 Notes
References
Chapter 2: Structural Classification of CDR-H3 in Single-Domain VHH Antibodies
1 Introduction
1.1 Architectures of Antigen-Binding Sites
1.2 Short History of CDR Classifications
2 Current Knowledge of Sequence-Structure-Function Correlations of VHH Antibodies
2.1 Diversity of FRs and CDRs
2.2 Contribution of FRs and CDRs to Antigen Binding
3 Structural Classification of CDR-H3
3.1 Single-Domain VHH Antibodies in the PDB
3.2 Visual Inspection of CDR-H3 Identified Three Structural Classes and Eight Subclasses
3.3 Conformational Changes of CDR-H3 upon Antigen Binding
4 Sequence-Structure Correlations of CDR-H3
4.1 H3-Rules Do Not Hold for CDR-H3 in VHH Antibodies
4.2 Sequence Features of Each Structural Class of Single-Domain Antibodies
5 Conclusions and Perspectives
5.1 Implications for Antibody Modeling
5.2 Implications for Antibody Engineering and Design
6 Notes
References
Part II: Modeling Antibody Structures and Dynamics
Chapter 3: Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops)
1 Introduction
2 TCR and Antibody (CDR3 Loop) Modeling Protocol
3 Notes
3.1 Note 3.1-LYRA
3.2 Note 3.2-Sphinx
3.3 Note 3.3-Additional Software for Modeling
3.4 Note 3.4-Expected Performance
3.5 Note 3.5-List of CDR Numbering Tools
References
Chapter 4: Molecular Dynamics Simulation for Investigating Antigen-Antibody Interaction
1 Introduction
2 Materials
3 Methods
3.1 MD Simulation
3.2 Structural Analyses
3.3 US/mTMD
References
Chapter 5: Molecular Dynamics Methods for Antibody Design
1 Introduction
2 Materials
3 Methods
3.1 Protocol A: Conventional MD Simulation
3.2 Protocol B: Engineering Antibody-Antigen Interactions
3.3 Protocol C: Engineering Antibody Stability for Harsh Environments
3.4 Protocol D: Engineering Quaternary and Higher-Order Structures
4 Notes
References
Chapter 6: Probing Conformational Dynamics of Antibodies with Geometric Simulations
1 Introduction
2 Materials and Methods
2.1 FRODAN-Geometric Dynamics Simulations
2.2 Geometric Simulations and Search Algorithms
2.3 FRODAN Simulation Procedure
3 Applications-Antibody Dynamics
4 Notes
References
Part III: Prediction and Optimization of Biological and Biophysical Properties of Antibodies
Chapter 7: PITHA: A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies
1 Introduction
2 Webserver
3 Methods and Materials
3.1 Dataset
3.2 Features
3.3 Training and Prediction Procedure
4 Notes
References
Chapter 8: Thermal Stability Estimation of Single Domain Antibodies Using Molecular Dynamics Simulations
1 Introduction
2 Materials
2.1 Protein Structures and Tm Data
3 Methods
3.1 System Preparation
3.2 MD Execution to Study the Thermal Stability
3.3 MD Analyses to Study the Thermal Stability
3.4 MD Execution and Analysis to Identify Potentially Stabilizing Mutants
3.5 Results Summary
4 Notes
References
Chapter 9: Assessing and Engineering Antibody Stability Using Experimental and Computational Methods
1 Introduction
2 Materials and Methods
2.1 Using RosettaCM to Obtain a Starting Structure Model
2.1.1 Alignment
2.1.2 Fragment Files
2.1.3 Prepare the Alignment in Grishin Format
2.1.4 Thread the Target Sequence over the Template Sequence
2.1.5 Obtain the Weights´ Files
2.1.6 Define Hybridize Script: rosetta_cm.xml
2.1.7 Define Options: rosetta_cm.options
2.1.8 Run RosettaCM Hybridize
2.1.9 Post-treatment
2.2 Using Rosetta relax to Refine the Local Conformational Space
2.2.1 Define Disulfide Bond: Fab.disulfide
2.2.2 Define Options: relax.options
2.2.3 Run relax
2.3 Molecular Dynamics in Gromacs
2.3.1 Determine the Protonation States
2.3.2 Load the PDB Structure into Gromacs
2.3.3 Define the Solvent Box
2.3.4 Solvate the Box with Water
2.3.5 Add Ions to Neutralize the Box
2.3.6 Energy Minimization, NVT, NPT, grompp for Production Run
2.3.7 Production Run
2.3.8 Analyze the Simulation
2.4 B-Factor Analysis
2.4.1 Obtain the Raw B-Factors from Homologous PDBs
2.4.2 Convert the Atomic B-Factors to Residue-Level B-Factors
2.5 Prediction of Mutational ΔΔG by Rosetta cartesian_ddg
2.5.1 Perform the In Silico Prediction of Mutational ΔΔG
2.5.2 Analysis of the ΔΔG Prediction
2.6 Selection of Mutations for Stability Improvement
2.6.1 Correlation Between RMSF and B-Factor
2.6.2 Most Flexible Residues Based on RMSF and B-Factor
2.6.3 Stable Mutant Candidates
2.6.4 Unstable Mutant Candidates
2.6.5 Production of the Variants
2.7 Formulating the Fab
2.7.1 Filtering
2.7.2 Concentrating
2.7.3 Buffer Selection
2.7.4 Formulating the Fab into the Desired Buffer
2.8 Thermal Stability Analysis
2.8.1 Sample Preparation
2.8.2 Measurement Parameters
2.8.3 Fitting the Thermal Measurement Result
2.8.4 Analysis of the Thermal Stability
2.9 Aggregation Kinetics
2.9.1 Sample Preparation
2.9.2 Thermal Incubation
2.9.3 Sampling
2.9.4 HPLC Analysis for Monomer Retention
2.9.5 Fitting the Monomer Retention Kinetics
2.9.6 Analysis of the Aggregation Kinetics
2.9.7 Correlation Between ΔΔG, Tm, and ln(v)
3 Notes
References
Chapter 10: In Silico Prediction Method for Protein Asparagine Deamidation
1 Introduction
2 Materials
2.1 Data Set Construction
2.2 Software
2.3 Machine Learning Algorithms
3 Methods
3.1 Training and Test Set Preparation
3.2 Descriptor Development (the Python Script for Calculating Descriptors Is Available in Supporting Information File S2)
3.3 Model Building, Cross-Validation, and Blind Test (the R Script for Training and Test Is Available in Supporting Informatio...
3.4 Prediction Results
3.5 Feature Selection and Understanding Protein Deamidation on Structure Level
3.6 MD Simulation to Investigate Deamidation Process
4 Application to Aspartic Acid Isomerization
5 Notes
5.1 Investigation Deamidation Prediction in NG Motif
5.2 Additional Metrics to Evaluate Performance with Unbalanced Data Sets
6 Summary
References
Chapter 11: Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for ...
1 Introduction
2 In Silico Predictions of Biophysical Attributes of Biotherapeutic Drug Candidates
2.1 Molecular Surface Hydrophobicity of Antibody Drug Candidates
2.2 Electrostatic Properties of Antibody Drug Candidates
2.3 PTM and Chemical Degradation Motifs in Antibody-Based Drug Candidates
3 Practical Considerations and Utility of Developability Assessments
4 Considerations in Antibody Homology Modeling
5 A Worked Example of Antibody Modeling and Developability Assessment
6 Conclusions and Future Directions
References
Part IV: Prediction of Antibody-Antigen Interactions
Chapter 12: B-Cell Epitope Predictions Using Computational Methods
1 Introduction
2 Webservers
3 Methods and Materials
3.1 Training and Test Datasets
3.2 Features
3.3 SVM Package and Model Parameters
4 Notes
References
Chapter 13: Computational Epitope Prediction and Design for Antibody Development and Detection
1 Introduction
2 Protocol
2.1 System Preparation
2.2 Minimization
2.3 MM/GBSA
2.4 Energy Decomposition
2.5 Matrix of Local Coupling Energies (MLCE)
2.6 Patch Identification
3 Use Cases
3.1 Single Frame Analysis
3.2 BEPPE Analysis
3.3 Multiframe/Cluster Analysis
References
Chapter 14: Information-Driven Antibody-Antigen Modelling with HADDOCK
1 Introduction
2 Overview
2.1 HADDOCK Docking Protocol
2.2 Clustering
3 Method
3.1 Installation
3.2 Identification of the Hypervariable Loops
3.3 Preparation of the Input Files
3.4 Antibody-Antigen Docking
4 Notes
References
Chapter 15: Structural Modeling of Adaptive Immune Responses to Infection
1 Introduction
2 Materials
2.1 Collection of Immune Repertoire Sequencing Data
2.2 Annotation of Sequence
2.3 Sequence Cleaning and Filtration
3 Modeling Methods
3.1 High-Throughput Antibody or TCR Modeling with Repertoire Builder
3.2 Antibody-Antigen Docking with AbAdapt
3.3 TCR-pMHC Docking with ImmuneScape
4 Notes
References
Chapter 16: Protein-Protein Interaction Modelling with the Fragment Molecular Orbital Method
1 Introduction
2 Materials
2.1 Structure Preparation
2.2 Molecular Dynamics Simulation
3 Methods
4 Notes
References
Part V: Computer-Aided Antibody Affinity Maturation and Beyond
Chapter 17: Structural Considerations in Affinity Maturation of Antibody-Based Biotherapeutic Candidates
1 Introduction
2 Structure-Based Affinity Maturation: A Worked Example
3 Analyses of Results
4 Library Generation Using In Silico Calculations
5 Conclusions and Future Directions
References
Chapter 18: Structure-Based Affinity Maturation of Antibody Based on Double-Point Mutations
1 Introduction
2 Methods
2.1 Preparation of the Structure of the Target Antibody-Antigen Complex
2.2 Selection of Candidate Residues to Be Mutated
2.3 Preparation of Model Structure
2.4 Benchmark of the Modeling Software Used
2.5 Structure Generation of Mutated Antibody-Antigen Complex
2.6 First Screening by Energy-Based Function
2.7 Second Screening by Rotamer Frequency
2.8 Third Screening by Interaction Analysis
3 Notes
References
Chapter 19: Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimiz...
1 Introduction
2 Materials
2.1 Algorithms
2.2 Cloning and Protein Expression
3 Methods
3.1 System Preparation
3.2 In Silico Optimization
3.3 In Silico Screening
3.4 Recombinant Protein Design
3.5 Cloning
3.6 Small-Scale Expression and Purification
4 Notes
References
Chapter 20: Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform
1 Introduction
2 Materials
3 Methods
3.1 Structure Preparations
3.2 Generating and Scoring Mutants
3.3 Selection of Mutants
4 Notes
References
Chapter 21: Using Graph-Based Signatures to Guide Rational Antibody Engineering
1 Introduction
2 Materials
2.1 An Overview of Computational Tools to Guide Rational Antibody Engineering
2.2 Antibody-Antigen Structure
2.3 Mutation Information
3 Methods
3.1 Predicting and Analyzing Effects of Single-Point Mutations on Antibody Binding Affinity Using mCSM-AB and mCSM-AB2
3.2 mCSM-AB and mCSM-AB2 Output
3.3 Predicting and Analyzing Effects of Multiple-Point Mutations on Antibody Binding Affinity Using mmCSM-AB
3.4 mmCSM-AB Output
3.5 Guiding Rational Antibody Engineering Using the mCSM Platform
3.6 Chemical Modification to Improve Antibody Pharmacokinetics
3.6.1 PEGylation of mAbs via Amide Bond Formation
3.6.2 PEGylation of mAbs via Thioether Bond Formation
3.6.3 PEGylation of Fabs
3.6.4 Purification of PEGylated mAbs or Fabs
4 Notes
References
Chapter 22: A Computational Framework for Determining the Breadth of Antibodies Against Highly Mutable Pathogens
1 Introduction
2 Methods
2.1 Select the Panel of Antigens
2.2 Modeling the Antibody-Antigen Interaction
2.2.1 Find Templates for the Atomistic Models
2.2.2 Create the Atomistic Models
2.3 Compute the Binding Score
2.3.1 Binding Breadth
2.3.2 Neutralizing Breadth
2.3.3 Classification Versus Regression Models
2.4 Compute the Breadth
3 Notes
References
Chapter 23: Analytical Method for Experimental Validation of Computer-Designed Antibody
1 Introduction
1.1 Interaction Analysis: Evaluation of Affinity
1.2 Interaction Analysis: Kinetic and Thermodynamic Parameters
2 Analytical Technique for Experimental Validation of Antibody
2.1 ELISA
2.2 FP
2.3 FCM
2.4 ITC
2.5 SPR
2.6 BLI
2.7 MST
3 Concluding Remarks
References
Part VI: Synthetic Antibody Library, Next Generation Sequencing of B-ELL Repertoires and Immunotherapy
Chapter 24: Computational Analysis of Antibody Paratopes for Antibody Sequences in Antibody Libraries
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 25: Bioinformatic Analysis of Natively Paired VH:VL Antibody Repertoires for Antibody Discovery
1 Introduction
2 Materials and Software
3 Methods
3.1 Paired VH:VL Gene Annotation and Clonotype Generation
3.2 Separate HC and LC Gene Annotation
3.3 Generate VH:VL Antibody Consensus Sequences
3.4 Downstream Processing and Validation
4 Notes
References
Chapter 26: Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning
1 Introduction
2 Materials
2.1 Commands to Install Anaconda
2.2 Commands to Install DeepRC
3 Methods
3.1 Training DeepRC on Real-World Data with Implanted Signals
3.2 Training DeepRC on LSTM-Generated Data
3.3 Training DeepRC on Simulated Immunosequencing Data
3.4 Training DeepRC on Real-World Data
3.5 Training DeepRC on Your Own Dataset
4 Notes
References
Chapter 27: A Computational Pipeline for Predicting Cancer Neoepitopes
1 Introduction
2 Methods
2.1 Data Processing WXS/WGS Data
2.1.1 Trimming
2.1.2 FastQC
2.1.3 Alignment
2.1.4 Sort and Mark Duplicates
2.1.5 Indexing BAM Files
2.1.6 Base Quality Score Recalibration
2.2 Variant Calling
2.3 HLA Typing
2.4 Data Processing RNA-Seq Data
2.5 RNA-Seq Expression File
2.6 MuPeXI Web Server
2.7 MuPeXI Local Version
3 Notes
References
Index