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E-raamat: Virtual Screening in Drug Discovery

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  • Formaat: 496 pages
  • Ilmumisaeg: 24-Mar-2005
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781040204917
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  • Formaat: 496 pages
  • Ilmumisaeg: 24-Mar-2005
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781040204917

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Virtual screening can reduce costs and increase hit rates for lead discovery by eliminating the need for robotics, reagent acquisition or production, and compound storage facilities. The increased robustness of computational algorithms and scoring functions, the availability of affordable computational power, and the potential for timely structural determination of target molecules, have provided new opportunities for virtual screening, and made it more practical. Why then, isnt everyone using virtual screening? Examining the scope and limitations of this method, Virtual Screening in Drug Discovery explores the algorithms involved and how to actually use them.

Part I offers perspectives on both ligand-based and docking-based virtual screens. The authors of these chapters frame many of the challenges currently facing the field. Part II considers the choice of compounds that are best suited as drug leads. Part III discusses ligand-based approaches, including descriptor-based similarity, traditional pharmacophore searching, and similarity based 3D-pharmacophore fingerprints. The final two sections are devoted to molecular docking. Part IV outlines some important and practical considerations relating to the energetics of protein-ligand binding and target-site topography, whereas specific docking algorithms and strategies are discussed in Part V.

Notwithstanding this list of subjects, the book does not overwhelm you with more information than you needmany of the strategies outlined will transcend the specifics of any given method. Nor does the book purport to offer single best ways to use the programs. What it does is provide a snapshot of virtual screening that gives you easy access to strategies and techniques for lead discovery.

Daniel E. Levy, editor of the Drug Discovery Series, is the founder of DEL BioPharma, a consulting service for drug di
PART I: Perspectives on Virtual Screening
1(86)
Virtual Screening: Scope and Limitations
Gerhard Klebe
Introduction
3(1)
Strategies to Virtual Screening
4(1)
Development of a Reliable Pharmacophore Hypothesis
5(1)
Series of Consecutive Hierarchical Filters to Match with the Pharmacophore Hypothesis
6(2)
Docking, Scoring, and Visual Inspection
8(1)
Virtual Screening: Matured as a Routine Tool?
9(1)
Peptide Bond Flip and Interstitial Water Molecules
10(2)
Changes of Protonation States Induced upon Ligand Binding
12(4)
Water, the Nasty, Frequently Ignored Binding Factor
16(1)
Protein Plasticity or ``How to Hit a Mobile Target?''
17(2)
Conclusions and Outlook
19(6)
Acknowledgments
19(1)
References
20(5)
Addressing the Virtual Screening Challenge: The Flex Approach
Marcus Gastreich
Christian Lemmen
Hans Briem
Matthias Rarey
Introduction
25(1)
Elementary Models for Docking and Structural Alignment Calculations
26(4)
Conformations
26(1)
Molecular Interactions
27(2)
Scoring
29(1)
Basic Algorithmic Concepts
30(2)
Placing Molecular Fragments
30(1)
The Incremental Construction Phase
31(1)
Bringing It All Together: Base Selection---Base Placement---Incremental Construction -- Postoptimization
31(1)
Advanced Concepts for VS Tools
32(3)
Combinatorial Libraries
32(1)
Docking under Pharmacophore Constraints
33(1)
Protein Flexibility
33(1)
Multimolecule Alignment
34(1)
Details to Be Taken into Account
34(1)
Covalent Binders
34(1)
Flexible Ring Systems
34(1)
Water Molecules and Ions
35(1)
Stereoisomerism
35(1)
Supporting the Workflow---From an Algorithmic Engine to a VS Machine
35(2)
Scripting the Screening Process
35(1)
Parallel VS
36(1)
A Database of VS Results
36(1)
Mining VS Results
36(1)
A Practical Example---VS on a Cyclin-Dependent Kinase Target
37(10)
An Initial Test---Reproducing and Cross-Docking Crystal Structures
37(2)
VS for CDK2 Inhibitors
39(1)
Results
40(1)
Alignment-Based Screening for CDK2 Inhibitors
41(1)
Acknowledgments
42(1)
Notes
43(1)
References
43(4)
An Analysis of Critical Factors Affecting Docking and Scoring
Emanuele Perola
W. Patrick Walters
Paul S. Charifson
Introduction
47(2)
The Importance of Test Set Selection
49(5)
Complex Selection
50(1)
Composition of the Test Set
51(1)
Complex Preparation
51(3)
Evaluation of Highly Regarded Docking Programs
54(10)
ICM (MolSoft LLC)
55(1)
Glide (Schrodinger, LLC)
56(1)
Gold (Cambridge Crystallographic Data Centre)
57(1)
Results and Discussion
57(6)
Conclusions
63(1)
Scoring Function Analysis
64(9)
Piecewise Linear Potential (PLP)
64(1)
ChemScore
65(1)
GlideScore
65(1)
Potential of Mean Force (PMF)
65(1)
PMF612
65(1)
MMFF
66(1)
OPLS--AA
66(1)
Correlations between Scoring Functions
66(1)
Scoring Function Accuracy
67(1)
The Effects of Training Set Selection
68(4)
Conclusions
72(1)
Evaluation of Docking/Scoring Combinations for Virtual Screening
73(6)
Results and Discussion
75(3)
Conclusions
78(1)
Overall Conclusions and Perspectives
79(8)
References
81(6)
PART II: Compound and Hit Suitability for Virtual Screening
87(38)
Compound Selection for Virtual Screening
Tudor I. Oprea
Cristian Bologa
Marius Olah
The Role of Leads in Drug Discovery
89(3)
The Leadlike Concept
92(5)
Compound Processing Prior to Virtual Screening
97(6)
Assembling the Virtual Compound Collection
97(1)
Cleaning Up the VCC
98(4)
Filter for Leadlikeness
102(1)
Similarity Search if Known Active Molecules Are Available
103(1)
3D Structure Generation
103(1)
Conclusions
103(4)
Acknowledgment
104(1)
Notes
104(1)
References
104(3)
Experimental Identification of Promiscuous, Aggregate-Forming Screening Hits
Susan L. McGovern
Introduction
107(1)
Overview
108(2)
Conceptual Advances
110(1)
Protocols
111(11)
Kinetic Assays
112(1)
Sensitivity to Detergent
113(2)
Inhibition of Dissimilar Enzymes
115(1)
Sensitivity to Enzyme Concentration
116(1)
Incubation Effect
117(1)
Other Kinetic Properties
118(1)
Light Scattering
119(1)
Overview
119(1)
DLS Instruments
119(1)
Methods
120(1)
Data Analysis
120(2)
Problems and Troubleshooting
122(1)
Conclusions
122(3)
Acknowledgments
123(1)
References
123(2)
PART III: Ligand-Based Virtual Screening Approaches
125(102)
Data Mining Approaches for Enhancement of Knowledge-Based Content of De Novo Chemical Libraries
Nikolay P. Savchuk
Konstantin V. Balakin
Introduction
127(1)
Classification OSAR in Virtual Screening
128(2)
Data Analysis and Visualization
130(1)
Preparatory Stages
131(1)
Knowledge Database
131(2)
Molecular Descriptors
133(2)
Enhancement of Target-Specific Informational Content of Virtual Compound Selections
135(5)
Sammon Mapping
140(3)
Modeling CYP450-Mediated Drug Metabolism
143(3)
Prediction of Toxicity for Human Fibroblasts
146(2)
Other Filters
148(2)
De Novo Design of Chemical Libraries
150(1)
Conclusions
151(6)
Acknowledgments
151(1)
References
152(5)
Pharmacophore-Based Virtual Screening: A Practical Perspective
John H. van Drie
Introduction
157(1)
Motivations Driving the Early Evolution: Historical Developments
157(3)
Concepts and Definition of Terms
160(8)
3D Database
160(1)
Virtual Screening, in silico Screening, 3D Database Searching
160(1)
Hits, Hit List
161(1)
Pharmacophore
161(1)
Structure--Activity Relationship
162(1)
Search Query
162(1)
Feature, Mapping, Multiple Mappings
162(1)
Dyads, Triads, Tetrads
162(1)
Overlay
163(1)
Scaffold
163(1)
Selectivity
163(1)
Success Rate
164(1)
Enrichment Ratio
164(1)
Geometric Object, Geometric Constraint
164(1)
Projected Point, Receptor Point, Dummy Atoms, Outriggers
165(1)
Steric Constraint, Forbidden Region, Shape-Enhanced Pharmacophores
165(1)
Rigid Match, Rigid Search vs. Flexible Match, Flexible Searching
166(1)
Pharmacophore Discovery
167(1)
2D, 1D
167(1)
Similarity, Similarity Searching
168(1)
Cluster, Clustering
168(1)
Smiles, Mol Files, SD Files
168(1)
Step 1 --- Constructing 3D Databases
168(5)
Where Do these Lists of Molecules Come from?
168(1)
What Types of Data-Cleaning Must One Perform on the Given Structures?
169(1)
How Does One Handle Chirality, in Particular Chiral Centers with Unspecified Chirality?
170(1)
How Does One Generate the 3D Information? How Does One Handle Conformational Flexibility?
171(2)
Step 2 --- Pharmacophore Discovery
173(7)
How Should the Dataset Be Selected?
175(1)
How Should the Conformational Analysis Be Performed?
176(1)
How Can One Detect Candidate Pharmacophores?
176(2)
What Combinations of Features and Constraints Should Be Used?
178(1)
How Should One Sift through the Candidate Pharmacophores?
178(1)
Are Steric Constraints Important? How Should One Construct Them?
179(1)
Which Computational Approaches Work Best? How Do They Compare?
179(1)
Step 3 --- What Does One Do with the Output of a Virtual Screen?
180(2)
Refine Query --- See if Data that Tests Hypothesis Exists
180(1)
Computational Postprocessing of Hits
181(1)
Biological Testing
181(1)
Case Studies
182(12)
D1 Agonists (Abbott)
182(2)
Fibrinogen Antagonists (Merck)
184(1)
Protein Kinase C Agonists (NCI)
185(2)
HIV Integrase Inhibitors (NCI)
187(1)
Muscarinic M3 Antagonists (Astra)
188(3)
α4β1 Antagonists (Biogen)
191(2)
Estrogen ERα Receptor (Organon)
193(1)
What Are the Components of the Art of Pharmacophore-Based Virtual Screening that Are Crucial for Success?
194(2)
Open Issues and Future Directions
196(1)
Conclusions
197(1)
Postscript
198(9)
Acknowledgments
201(1)
References
202(5)
Using Pharmacophore Multiplet Fingerprints for Virtual High Throughput Screening
Robert D. Clark
Peter C. Fox
Edmond J. Abrahamian
Introduction
207(2)
Overview
209(1)
Conceptual Advances
210(3)
Bitsets vs. Bitmaps
210(1)
Multiplet Mapping
210(1)
Accommodating a Range
211(1)
Bitmap Size
212(1)
Stochastic Cosine
212(1)
Application
213(14)
Preparing the Dataset
213(1)
Specifying Tuplet Generation Parameters
214(1)
Creating Multiplet Fingerprints
215(1)
Examination of Tuplets for a Class of Active Compounds
216(1)
Clustering Actives By Pharmacophoric Similarity
217(2)
Creating Tuplet Hypotheses and Screening a Database
219(3)
Acknowledgments
222(2)
Notes
224(1)
References
224(3)
PART IV: Important Considerations Impacting Molecular Docking
227(74)
Potential Functions for Virtual Screening and Ligand Binding Calculations: Some Theoretical Considerations
Kim A. Sharp
Introduction
229(1)
Types of Scoring/Binding Potentials
230(1)
Basic Theory of Absolute and Relative Binding Affinity
231(5)
Absolute Binding Affinity
231(4)
Relative Binding Affinity
235(1)
Screening Potential or Free Energy Calculation?
236(2)
Separability of Binding Free Energy Terms
238(2)
What to Put into Free Energy-Based Scoring Functions
240(1)
Linear Interaction Energy Methods
241(1)
Statistical Potentials
241(2)
Internal Conformational Changes
243(3)
Effect of Multiple Unbound Ligand Conformations
243(1)
General Effect of Internal Coordinate Changes
244(2)
Conclusions
246(3)
Acknowledgment
246(1)
References
246(3)
Solvation-Based Scoring for High Throughput Docking
Thomas S. Rush III
Eric S. Manas
Gregory J. Tawa
Juan C. Alvarez
Introduction and Scope of the Problem
249(1)
Evaluating the Free Energy of Binding between Small Molecules and Proteins
250(2)
Scoring Functions for High Throughput Docking
252(9)
Force-Field-Based Schemes
253(2)
Empirically-Based Schemes
255(3)
Knowledge-Based Schemes
258(1)
Continuum-Based Schemes
259(2)
Full Solvation-Based Scoring for High Throughput Docking
261(8)
The Benefits of Full Solvation-Based Scoring
262(4)
A Full Solvation-Based Scoring Function for High Throughput Docking
266(3)
Implementing Solvation-Based Scoring in a Tiered High Throughput Docking Scheme
269(3)
Conclusions and Future Directions
272(7)
Acknowledgments
273(1)
References
273(6)
Classification of Ligand-Receptor Complexes Based on Receptor Binding Site Characteristics
Marguerita S.L. Lim-Wilby
Teresa A. Lyons
Michael Dooley
Anne Goupil-Lamy
Sunil Patel
Christoph Schneider
Remy Hoffmann
Hugues-Olivier Bertrand
Osman F. Guner
Introduction
279(1)
Considerations in Binding Site Definitions
280(1)
Conceptual Advances in Using Binding Site Information
281(7)
Class I --- Small, Well-Defined Binding Sites
284(1)
Class II --- Large, Well-Defined Binding Sites
285(1)
Class III --- Open Binding Site, with Well-Defined Subsites
286(1)
Class IV --- Large Binding Site, with No Well-Defined Subsites
286(1)
Class V --- Shallow and Superficial Binding Site
287(1)
Class VI --- Ill-Defined Binding Site at Hinge Region
287(1)
Protocols for Addressing Binding Sites According to Classes
288(8)
Check that There Are No Obvious Problems with Ligand-Receptor Interactions
289(1)
Check X-ray Pose of Ligand
289(1)
Prepare Ligand for Native Docking
289(1)
Prepare Receptor Structure for Docking
289(2)
Define Binding Site
291(2)
Select Level of Site Partitioning
293(1)
Find Best Docking Parameters
293(1)
Number of Conformational Trials per Ligand
294(1)
Option to Perform Minimization with Molecular Mechanics on Saved Poses
295(1)
Select Scoring Functions
295(1)
Conclusions
296(5)
Acknowledgments
297(1)
References
297(4)
PART V: Docking Strategies and Algorithms
301(152)
A Practical Guide to Dock 5
Demetri T. Moustakas
Scott C.H. Pegg
Irwin D. Kuntz
Introduction
303(1)
Target Preparation
304(2)
X-ray Structures
304(1)
Structures from Other Sources
304(1)
Generating Matching Points
304(1)
Grid Construction
305(1)
Ligand Preparation
306(1)
Conformation
306(1)
Electrostatics
306(1)
Docking
307(4)
Dock Scoring Functions
307(1)
Bump Filtering
308(1)
Contact Scoring
308(1)
Energy Scoring
309(1)
Solvation Scoring
310(1)
Dock Score Optimization
310(1)
Rigid Docking
311(6)
Generation of Orientations
312(2)
Scoring Ligand Orientations
314(1)
Bump Filtering
315(1)
Contact Scoring
315(1)
Energy Scoring
315(1)
Solvation Scoring
316(1)
Optimizing Ligand Orientations
316(1)
Flexible Docking
317(4)
Ligand Anchor Perception
317(2)
Rigid Docking of the Anchor
319(1)
Flexible Growth
319(1)
Conformational Sampling
320(1)
Scoring and Optimization
321(1)
Refinement of Results
321(1)
Clustering
321(1)
Solvation Rescoring
321(1)
Evaluation of Docking Results
322(1)
Reproduction of Crystal Structures
322(1)
Types of Failures
322(1)
Ranking of Docked Molecules
323(1)
Dock Resources
323(4)
Acknowledgments
324(1)
References
324(3)
Pharmacophore-Based Molecular Docking: A Practical Guide
Diane Joseph-McCarthy
Iain J. McFadyen
Jinming Zou
Gary Walker
Juan C. Alvarez
Introduction
327(1)
Molecular Docking
328(3)
Ligand Flexibility
328(1)
Target Flexibility
329(1)
PhDock and MCSS2SPTS
329(2)
PhDock Database Generation
331(3)
Physical Property Filtering
331(1)
Conformer Generation
332(1)
3D Pharmacophore Generation and Overlays
332(2)
Database Statistics
334(1)
The Docking Process
334(4)
Protein Preparation
334(2)
Site Point Selection
336(1)
Scoring Functions Available
336(1)
Docking Run Parameters
336(1)
Postprocessing Docking Output
337(1)
HIV-1 Protease Test Case (A Cross-Docking Example)
338(1)
DHFR Enrichment Studies
339(4)
DHFR Actives Database for Enrichment-Factor Calculations
339(1)
Decoy Database
339(1)
Dock 4.0 vs. PhDock Enrichment Factors
340(1)
DHFR Actives Seeded into Entire ACD Database
340(1)
Evaluation of Various Scoring Schemes
340(3)
Conclusions
343(6)
Acknowledgments
344(1)
References
344(5)
Fragment-Based High Throughput Docking
Peter Kolb
Marco Cecchini
Danzhi Huang
Amedeo Caflisch
Introduction
349(1)
Overview
349(6)
Defining the Binding Site
349(1)
Generating a Pose
350(1)
Generation of Ligand Conformations
350(1)
Defining Ligand Positions
351(1)
Incremental Methods
352(1)
Ranking the Poses
353(1)
Objective Function
353(1)
Binding Energy Function and Postprocessing
353(1)
Solvation
354(1)
Protein Flexibility
354(1)
Technical Improvements
355(9)
Current Limitations
355(2)
Search Strategy
357(3)
Scoring Function
360(3)
Multiple-Step Docking
363(1)
Protocols
364(15)
Our Docking Approach
364(2)
Preparation of the Library of Compounds
366(1)
Fragment Choice
367(1)
Protein Preparation
368(1)
First Checks
368(1)
Charged Residues
369(1)
Adding Hydrogens
370(1)
Binding Site Definition
370(1)
Conserved Water Molecules
371(1)
Reference Structure
371(1)
Running SEED
372(1)
Running FFLD
372(1)
Acknowledgments
373(1)
References
374(5)
Protein--Ligand Docking and Virtual Screening with Gold
Jason C. Cole
J. Willem M. Nissink
Robin Taylor
Introduction
379(1)
Scoring Functions
379(9)
GoldScore
380(1)
External H-Bond Energy
380(4)
External vdW Energy
384(1)
Internal Strain and H-Bond Energy
384(1)
Other Aspects of GoldScore
384(2)
ChemScore
386(1)
Gaussian Smoothing
386(1)
Metal Coordination Parameterization
387(1)
Modification of ChemScore for Docking
388(1)
Search Strategy
388(5)
Search Space; Ligand and Protein Flexibility
388(1)
Genetic Algorithm Parameters
388(1)
Chromosome Composition and Ligand Placement
389(1)
Annealing
390(1)
Tuning the Genetic Algorithm
391(1)
Use of Torsion Distributions
391(2)
Prediction Reliability
393(10)
Validation Sets for Testing Docking Programs
393(2)
Interpreting Validation Results
395(1)
Gold Validation Results
396(1)
Current Validation Results for Alternative Genetic Algorithm Settings
396(2)
Importance of Mediating Water Molecules
398(1)
Performance on Different Classes of Proteins
398(1)
Prediction of Ligand Affinity; Virtual High Throughput Screening
398(4)
Gold in the Literature
402(1)
Program Infrastructure
403(8)
Protein and Ligand Preparation
403(1)
Atom Typing
404(1)
Hydrogen Atom Placement
404(1)
Binding-Site Definition
405(1)
Constraints and Restraints
405(1)
Covalent Constraints
405(1)
Distance Restraints
405(1)
Similarity-Based (Pharmacophore) Restraints
406(1)
H-Bonding Restraints
407(1)
Dealing with Large Numbers of Ligands
408(1)
Parallel Processing
408(1)
Selection of the Best Results
409(1)
Customization
410(1)
Future Work
411(6)
Acknowledgments
411(1)
References
411(6)
A Brief History of Glide: A New Paradigm for Docking and Scoring in Virtual Screening
Thomas A. Halgren
Robert B. Murphy
Richard A. Friesner
Introduction
417(1)
Computational Methodology
417(1)
Protein and Ligand Preparation
418(1)
Scoring in Glide
419(2)
Docking Accuracy
421(2)
Accuracy in Virtual Screening
423(3)
Extra-Precision Glide
426(9)
Thymidine Kinase (1kim)
429(1)
CDK-2 (1dm2; 1aq1)
429(2)
p38 Map Kinase (1a9u; 1b17; 1kv2)
431(4)
Optimizing Glide's Performance
435(14)
Preparing the Protein Correctly for Glide
435(1)
Choosing the Protein Site or Sites
435(1)
Preparing the Site or Sites
436(1)
Making Sure the Site Properly Accommodates the Cocrystallized Ligand
436(1)
Choosing the Enclosing Box
437(1)
Preparing the Ligands Correctly for Glide
438(1)
Optimizing the vdW Scale Factors
439(3)
Using Glide to Screen Large Databases
442(1)
Dividing the Screen Over Multiple Processors
442(1)
Using glide_sort to Work Up the Results
443(1)
Dealing with a Subjob that Fails
444(1)
Using Glide XP to Improve Pose Quality or Enhance Early Enrichment
445(1)
Using Multiple Receptor Sites to Deal with Receptor Flexibility
446(3)
Conclusions
449(4)
Note
450(1)
References
450(3)
Index 453


Juan Alvarez, Brian Shoichet