Computational Methods for Estimating the Kinetic Parameters of Biological Systems

Preface
Contents
Contributors
Chapter 1: Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models
1 Introduction
2 Modeling Approaches for Drug-Target Binding Kinetics
3 Traditional Pharmacodynamic Models
4 Mechanistic Pharmacodynamic Models that Include Drug-Target Binding
4.1 Factors that Influence Binding Kinetics
4.1.1 Concentration Gradient Between Intracellular and Extracellular Space
4.1.2 Nonspecific Binding
4.1.3 Endogenous Competition
4.1.4 Target Turnover
4.2 Drug Efficacy as a Function of Target Occupancy
4.2.1 Linear Function
4.2.2 Sigmoidal Function
4.2.3 Signal Transduction Network
4.3 Advantages of Mechanistic PD Models
4.3.1 Population Heterogeneity
4.3.2 Prolonged Drug Action
4.3.3 Synergistic and Antagonistic Action of Drugs
5 Methodology
5.1 How to Select a Suitable Model
5.1.1 Selecting Traditional or Mechanistic Models to Describe the Process
5.1.2 Selecting the Mathematical Modeling Approaches to Represent the Chosen Model
5.2 Estimation of Uncertain Parameters
5.3 Simulation Tools
6 Challenges of Current Mechanistic PD Models
References
Chapter 2: An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe Dru...
1 Introduction
1.1 Target-Mediated Drug Disposition (TMDD)
1.2 The Basic TMDD Model and Its Equations
2 Characterization of TMDD Disposition After an iv Bolus Administration of Ligand
2.1 Phase A
2.2 Phase B
2.3 Phase C
2.4 Phase D
2.5 Conclusions
3 Characterization of TMDD Disposition After an iv Constant-Rate Infusion of Ligand
4 Equilibrium Relationships
4.1 L, R, and RL vs. the Ligand Dosing Rate
4.2 Rss and RLss Versus the Ligand Concentration Lss
4.3 The In Vivo Potency EC50
5 Clearance of Ligand L
6 Reduced Models
6.1 The Parallel Linear and Michaelis-Menten Elimination Model
6.2 The Constant Target Pool Hypothesis
6.3 Quasi-Equilibrium (QE) and Quasi-Steady State (QSS) hypotheses
6.4 QE or QSS and Constant Target Pool Hypothesis Combined
7 Discussion
References
Chapter 3: Beyond the Michaelis-Menten: Bayesian Inference for Enzyme Kinetic Analysis
1 Introduction
2 Method
2.1 Overview of the Software: Installation
2.2 Step-by-Step Instructions for Computational Package for the Bayesian Inference
3 Outputs of the Computational Package
3.1 Summary Statistics
3.2 Diagnostic Plots
4 Comparisons of Enzyme Kinetics Based on the sQSSA and the tQSSA Models
4.1 Estimation of kcat
4.2 Estimation of KM
4.3 Estimation of kcat and KM
4.4 An Optimal Progress Curve Assay
5 Notes
References
Chapter 4: Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation
1 Introduction
2 Materials
2.1 Getting the Model in Proper Form
2.2 Software
3 Methods
3.1 Define the Multi-Objective Problem. Objectives
3.2 Define the Multi-Objective Problem. Decision Variables
3.3 Multi-Objective Optimization
3.4 Multi-Criteria Decision-Making
4 Notes
References
Chapter 5: Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven No...
1 Introduction
2 Materials
2.1 PEPSSBI Architecture
2.2 Running PEPSSBI
3 Methods
3.1 Data Normalization
3.2 DNS Objective Functions
3.3 The PEPSSBI Input Language
3.3.1 Algorithmically Supported Data Normalization
3.3.2 Parameter Definitions
3.3.3 Multi-condition and Multi-model
3.3.4 Interpolated Data as Model Input
3.3.5 Parameter Estimation
3.3.6 Simulations and Optimal Parameters Plots
3.4 Example of Algorithmically Supported Data Normalization and DNS
3.5 Parameter Estimation of Synthetic Gene Circuits
3.6 Applying PEPSSBI to an Existing Model
3.7 PEPSSBI and Systems Biology Benchmarks
3.8 Conclusions
4 Notes
References
Chapter 6: Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models
1 Introduction
1.1 Least Squares Method
1.2 Nonlinear Least Squares Method for Parameter Estimation
1.3 Optimal Control Problem for Microalgae
1.4 Numerical Methods for Biomass Concentration of Microalgae
2 Materials
3 Methods
3.1 Monod Model
3.2 Gauss-Newton Method
3.3 Levenberg-Marquardt Method
Coding of Parameter Estimation of Monod Model by Gauss-Newton Method in Matlab Software
3.4 Photobioreactor Model
Coding of Parameter Estimation of Monod Model by Levenberg-Marquardt Method in Matlab Software
3.5 Optimal Input Design Method
3.5.1 State Equations of Monod Model
3.5.2 Cost Function
3.5.3 Co-state Equations for Monod Model
3.5.4 Stationary Equations for Monod Model
3.6 Taylor Series Method
Coding of Optimal Input Design Method in Mathematica Software
4 Notes
Coding of Euler Method in Matlab Software
Coding of Second-Order Taylor Series Method in Matlab Software
References
Chapter 7: Automatic Assembly and Calibration of Models of Enzymatic Reactions Based on Ordinary Differential Equations
1 Introduction
2 Comparisons Between Experimental Initial Rate Data and Complete Progress Curves
2.1 The Essence of Progress Curve Analysis Using Numerical Approaches
3 Exemplifying the Power of Numerical Analysis of Progress Curve Data
4 Notes
References
Chapter 8: Data Processing to Probe the Cellular Hydrogen Peroxide Landscape
1 Introduction
2 Data Processing
2.1 Theoretical Model
2.2 Data Handling
2.2.1 Determination of kox and krd
2.2.2 Analysis of Hydrogen Peroxide Landscape Based on Redox Genetic Probes
2.2.3 Determining Rate Constants for the Oxidation of Redox Target with Unknown Reactivity Toward Hydrogen Peroxide
3 Discussion
References
Chapter 9: Computational Methods for Structure-Based Drug Design Through System Biology
1 Introduction
2 System Biology and Drug Target Identification
2.1 System-Based Drug Discovery
2.2 Disease as a State of the Network
2.3 Modulating Network Dynamics
3 Allosteric Binding Sites as Drug Targets
4 Modulating Drug Target Gene Expression
5 The Influence of the Kinetics of the Drug Binding Process
6 New Opportunities for SBDD Approaches in the System Biology Era
6.1 De Novo Drug Design
6.2 Multi-Target Drug Design
7 Repositioning of Drug Analogs
8 Design Drugs to Target Specific Protein-Protein Interactions (PPIs)
9 Conclusions
References
Chapter 10: Model Setup and Procedures for Prediction of Enzyme Reaction Kinetics with QM-Only and QM:MM Approaches
1 Introduction
2 Parametrization of Nonstandard Residues
2.1 Parametrization of an Organic Molecule
2.2 Parametrization of Nonstandard Amino Acid
2.3 Parameterization of the Metal Center
2.3.1 Protocol in Brief to Parametrize the Metal Center by the Seminario Method
2.3.2 Protocol in Detail
3 MD Simulation
3.1 Protein Preparation
3.2 Geometry Minimization
3.3 Heating, Density Equilibration and Production
3.3.1 After Minimization, Heat the System from 0 to 300 K (3.MD-Simulation/2.3. MD)
3.3.2 Density Equilibration
3.3.3 Production Run 5 ns
3.4 Analysis of MD Simulation
3.4.1 RMSD Analysis
3.4.2 Cluster Analysis
4 Cluster-Based QM Calculations
4.1 Construction of the Cluster Model
4.2 Investigation of the Reaction Pathway with the Cluster Model
4.3 Analysis of Different Reaction Pathways
5 Correlation of Modeling with Kinetic Experiment
6 QM:MM Modeling
6.1 Preparation of the ONIOM Model for QM:MM Calculations
6.1.1 Selection of the ONIOM Model and Optimization Zone
6.1.2 Preparation of the *.atq file (5.QMMM/2_step_prepare_atq_file)
6.1.3 Preparation of the ONIOM Input File (5.QMMM/3_step_prepare_oniom_input)
6.2 How to Analyze the Log File and Recompute Atomic Charges for the QM Subsystem
7 QMMM MD Simulation
7.1 Preparation of QMMM MD Calculations
7.2 Scanning Atom Transfer with QMMM MD Simulation
7.3 Analysis of QMMM MD Simulation
8 Prediction of Kinetic Isotope Effects
9 Notes
References
Chapter 11: The Role of Ligand Rebinding and Facilitated Dissociation on the Characterization of Dissociation Rates by Surface...
1 Introduction
2 Methods
2.1 Rebinding of Ligands Due to High Surface Density of Binding Sites
2.2 Facilitated Dissociation (FD) of Ligands by Solution-Phase Ligands
2.3 Deciphering the Key Performance Metrics of Plasmonic Sensing
2.3.1 Surface Functionalization
2.3.2 Antifouling Strategies
3 Final Remarks
4 Notes
References
Chapter 12: Computational Tools for Accurate Binding Free-Energy Prediction
1 Introduction
1.1 General Considerations
1.2 Computational Tools to Predict Binding Free Energies
1.3 Theory
1.3.1 Molecular Dynamics Simulation
1.3.2 Calculation of a Potential of Mean Force-A Pathway Method
1.3.3 Double Decoupling Calculations-An Alchemical Method
2 Methods
2.1 Technical Prerequisites
2.2 Calculation of a Potential of Mean Force
2.2.1 Prior Considerations
2.2.2 Simulations
2.2.3 Analysis
2.2.4 Summary
2.2.5 Example
2.3 Double Decoupling Calculations
2.3.1 Prior Considerations
2.3.2 Simulations
2.3.3 Analysis
2.3.4 Summary
2.3.5 Example
3 Notes
3.1 General Notes
3.2 Notes Concerning Potential of Mean Force Calculations
3.3 Notes Concerning Double Decoupling Calculations
References
Chapter 13: Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease
1 Introduction
2 Methods
2.1 System Setup and Molecular Dynamics Simulations
2.2 GBSA_ASIE Method
3 Results and Discussion
3.1 Dynamic Stabilities of Interacting Residues
3.2 MM/GBSA_ASIE Calculation
3.3 Key Residues on TCRs
3.4 Key Residues on MHCs
3.5 Interaction Between MHCs and TCRs
3.5.1 Interaction Between MHC α Chain and TCR
3.5.2 Interaction Between MHC β Chain and TCR
3.6 Key Residues on Peptides
3.6.1 Interaction Between Peptide and TCRs
3.6.2 Interaction Between Peptide Gliadin-γ1(GPQQSFPEQEA) and TCRs
3.6.3 Interaction Between Peptide Gliadin-α1(EGSFQPSQE) and TCRs
4 Conclusion
References
Chapter 14: Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity
1 Introduction
2 Computational Ligand-Binding Affinity
2.1 Preparing the Protein-Ligand Complex
2.2 Generating Initial Conformation for Umbrella Sampling Simulations Using Steered MD
2.3 Umbrella Sampling Simulations
2.4 Computation of the Free Energy Profile Using the Potential Mean Force Method
3 Notes
References
Chapter 15: Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery
1 Introduction
2 Setup
3 Methods
4 Notes
References
Chapter 16: Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures
1 Introduction
1.1 Protein-Protein Interactions as Basis of Cellular Functions
1.2 Relevance on Nonnative Binding Interface Regions in the Formation of Protein Complexes
1.3 BADOCK: Estimating Binding Affinity from Unbound Proteins
2 Methods
2.1 Predicting the Structure of Protein Complexes by Comparative Modeling (MODPIN)
2.1.1 Background
2.1.2 Protocol
2.2 Predicting the Structure of Protein Complexes by Docking-Based Approaches (V-D2OCK)
2.2.1 Background
2.2.2 Protocol
2.3 Estimation of Binding Affinities from Unbound Proteins: BADOCK
2.3.1 Background
2.3.2 Protocol
3 Notes
4 Examples
4.1 Example to Complement BADOCK with MODPIN
4.2 Example to Complement BADOCK with V-D2OCK
4.3 Example to Complement BADOCK with iFrag
References
Chapter 17: Parameter Optimization for Ion Channel Models: Integrating New Data with Known Channel Properties
1 Introduction
2 Materials
2.1 Computer
3 Methods
3.1 Install the QuB Software
3.2 Set up the Modeling Interface in QuB
3.3 Prepare the Data for Fitting
3.4 Prepare a Kinetic Model
3.5 Define Linear Parameter Constraints
3.6 Formulate Behavioral Constraints
3.7 Calculating the Total Penalty
3.8 Setting Up the Optimization
3.9 Run the Optimization
3.9.1 Overview of the Method
3.9.2 Run I: No Constraints Applied
3.9.3 Run II: All Constraints Applied
3.10 Conclusions
References
Index