SlideShare une entreprise Scribd logo
1  sur  30
Protein docking
Saramita De Chakravarti
12/14/2012
Outline
 Physical background of protein
 Protein docking
Quantum Mechanism (QM)
 Electrons
 Nuclei
 Wave functions instead of Newton Eq.
 High computation cost
 Ab initio N4
N is # of base functions
 Semi-empirical N3
 Molecule of a couple of hundreds atoms
 Pico-second(10-12
s) behavior
Molecular Mechanism (MM)
 Atoms
 Newton equations
 Parameters from experiment and QM
 force field
 System with 100,000 atoms
 µs (10-6
s) behavior
Force field
+−+−= ∑∑ 22
)()( eq
angle
eq
bonds
b krrkU θθθ
])[()]cos(1[
2 612
ij
ji
ij
ij
i ij ij
ij
dihedral n
n
r
qq
r
B
r
A
n
V
⋅
+−+−+ ∑∑∑ ∑ > ε
γφ
kb – bond parameter
kө --- angle parameter
Vn --- dihedral energy barrier
Van Der Waal radii
Partial charge set
Solvent effect
 Oil in water
 Hydrophobic interactions
 Alcohol in water
 Hydrophilic interactions
 Explicit water
 Solvent Accessible Surface
 Surface Complementarity
Twenty Amino Acids
Sidechains
The amino acids vary in their side chains (indicated in blue in the diagram).
The eight amino acids in the orange area are nonpolar and hydrophobic.
The other amino acids are polar and hydrophilic ("water loving").
The two amino acids in the magenta box are acidic ("carboxy" group in the side chain).
The three amino acids in the light blue box are basic ("amine" group in the side chain).
Peptide bonds
Proteins
 Primary structure
 Secondary structure
 Tertiary structure
 Quaternary structure
Important forces
 Weak forces
 Delicate balance
 Electrostatic (hydrogen bonds, salt
bridges)
 Hydrophobic (sidechain packing)
Protein docking
Contents
 Why Is Docking Important?
 Why Is Docking Hard?
 Docking Scoring Criteria
 Docking Search Strategies
 CAPRI Participants & Algorithms
 Lessons Learnt From CAPRI
 Selected Docking References
http://www.csd.abdn.ac.uk/~dritchie/
Why Is Docking Important?
 Better understand the Machinery of Life
 Enzyme-inhibitor class
 Antibody-antigen class
 Others
 Engineered Protein Enzymes
 Protein Therapies
 Drug targets
Why Is Docking A Hard Problem?
 Large Search Space !
 Rigid Body Docking: 6D
 Flexible Docking: 3N (N normal modes)
 Structure Space: Continuous
Criteria for Good Docking
Orientations
 Low Free Energy
 Low Pseudo-Energy Based On force field
 Large Surface Burial
 Small van der Waals Overlaps
 Good H-Bonding
 Good Charge Complementarity
 Polar/Polar Contacts Favoured
 Polar/Non-Polar Contacts Disfavoured
Docking Search Strategies
 Pseudo Random
 Simulated Annealing / Monte Carlo
 Genetic Algorithms
 Directed Search
 Geometric Hashing
 Spherical Harmonic Surface Triangles
Docking Search Strategies
 Brute-Force Search
 Explicit Grid Correlations
 Fast Fourier Transform (FFT) Correlations
 Refinement Phase
 Classical or Soft Potentials (+/- Electrostatics)
 Desolvation, Solvent Dipoles...
 Visual Inspection!!
CAPRI
 Critical Assessment of PRedicted
Interactions
 http://capri.ebi.ac.uk/
 targets available (unbounded/bounded)
 Bounded (rigid)
 Unbounded (non-rigid)
 International groups participated
Cartesian Grid Correlations
Basic Principles
d
PNAS 1992 89 pp. 2195-2199
Electrostatic complementarity
JMB 1997 272, pp 106-120
The electric field by protein A
Correlation function
Binary filter to remove false positive geometries
Lessons Learnt From CAPRI
 Antibodies Often Bind Near Antigen's
Active Site
 Expect Large Conformational Change
in Enzyme/Inhibitor Docking
 Develop Better Models of Flexibility
Future Challenges For
Docking
 Better Scoring Functions
 High-Throughput Screening
 Tractable Models of Flexibility
Reference
 Reviews
Halperin et al.; Proteins, 47 409-443 (2002)
Smith & Sternberg; COSB, 12 28-35 (2002)
 CAPRI
http://capri.ebi.ac.uk/
 Algorithms
Chen & Weng; Proteins, 47 281-294 (2002)
Fernandez-Recio et al.; Prot Sci, 11 280-291 (2002)
Gardiner et al.; Proteins, 44 44-56 (2001)
Camacho et al.; Proteins, 40 525-537 (2000)
Palma et al.; Proteins, 39 372-384 (2000)
Ritchie & Kemp; Proteins, 39 178-194 (2000)
Gabb et al.; J Mol Biol 272 106-120 (1997)
Vakser; Proteins, S1 226-230 (1997)
Abagyan et al.; J Comp Chem, 15 488-506 (1994)
Norel et al.; Prot Eng 7 39-46 (1994)
Katchalski-Katzir et al.; PNAS, 89 2195-2199 (1992)

Contenu connexe

Tendances

Introduction to proteomics
Introduction to proteomicsIntroduction to proteomics
Introduction to proteomics
Shryli Shreekar
 
Gene identification and discovery
Gene identification and discoveryGene identification and discovery
Gene identification and discovery
Amit Ruchi Yadav
 

Tendances (20)

methods for protein structure prediction
methods for protein structure predictionmethods for protein structure prediction
methods for protein structure prediction
 
Sage
SageSage
Sage
 
Threading modeling methods
Threading modeling methodsThreading modeling methods
Threading modeling methods
 
Protein 3 d structure prediction
Protein 3 d structure predictionProtein 3 d structure prediction
Protein 3 d structure prediction
 
Sts
StsSts
Sts
 
Basics Of Molecular Docking
Basics Of Molecular DockingBasics Of Molecular Docking
Basics Of Molecular Docking
 
Validation of homology modeling
Validation of homology modelingValidation of homology modeling
Validation of homology modeling
 
Introduction to proteomics
Introduction to proteomicsIntroduction to proteomics
Introduction to proteomics
 
Functional annotation
Functional annotationFunctional annotation
Functional annotation
 
Role of bioinformatics in drug designing
Role of bioinformatics in drug designingRole of bioinformatics in drug designing
Role of bioinformatics in drug designing
 
Gene identification and discovery
Gene identification and discoveryGene identification and discovery
Gene identification and discovery
 
Molecular Docking using Autodock 4.2.6
Molecular Docking using Autodock 4.2.6Molecular Docking using Autodock 4.2.6
Molecular Docking using Autodock 4.2.6
 
Genomics and proteomics in drug discovery and development
Genomics and proteomics in drug discovery and developmentGenomics and proteomics in drug discovery and development
Genomics and proteomics in drug discovery and development
 
Protein structure prediction with a focus on Rosetta
Protein structure prediction with a focus on RosettaProtein structure prediction with a focus on Rosetta
Protein structure prediction with a focus on Rosetta
 
Omics era
Omics eraOmics era
Omics era
 
MD Simulation
MD SimulationMD Simulation
MD Simulation
 
Gene prediction methods vijay
Gene prediction methods  vijayGene prediction methods  vijay
Gene prediction methods vijay
 
Molecular Docking Using Autodock
Molecular Docking Using AutodockMolecular Docking Using Autodock
Molecular Docking Using Autodock
 
Gene prediction and expression
Gene prediction and expressionGene prediction and expression
Gene prediction and expression
 
Molecular Docking Using Autodock Tools
Molecular Docking Using Autodock ToolsMolecular Docking Using Autodock Tools
Molecular Docking Using Autodock Tools
 

En vedette

Molecular docking
Molecular dockingMolecular docking
Molecular docking
palliyath91
 
Lab assignment 1 revised 2
Lab assignment 1 revised 2Lab assignment 1 revised 2
Lab assignment 1 revised 2
juancarlosrise
 
2. better control, better life dr. ko ko
2. better control, better life   dr. ko ko2. better control, better life   dr. ko ko
2. better control, better life dr. ko ko
ko ko
 
Incretin Therapy
Incretin TherapyIncretin Therapy
Incretin Therapy
ko ko
 
Drug Target Identification
Drug Target IdentificationDrug Target Identification
Drug Target Identification
Arvind306
 

En vedette (20)

Protein-ligand docking
Protein-ligand dockingProtein-ligand docking
Protein-ligand docking
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
Notes for SQLite3 Usage
Notes for SQLite3 UsageNotes for SQLite3 Usage
Notes for SQLite3 Usage
 
InSilico DB the collaborative genomics hub https://insilicodb.com
InSilico DB the collaborative genomics hub https://insilicodb.comInSilico DB the collaborative genomics hub https://insilicodb.com
InSilico DB the collaborative genomics hub https://insilicodb.com
 
Lab assignment 1 revised 2
Lab assignment 1 revised 2Lab assignment 1 revised 2
Lab assignment 1 revised 2
 
P pt rise insilico 2
P pt rise insilico 2P pt rise insilico 2
P pt rise insilico 2
 
2. better control, better life dr. ko ko
2. better control, better life   dr. ko ko2. better control, better life   dr. ko ko
2. better control, better life dr. ko ko
 
Insilico design and docking of Novel chemical compounds
Insilico design and docking of Novel chemical compoundsInsilico design and docking of Novel chemical compounds
Insilico design and docking of Novel chemical compounds
 
Design of an hexapeptide database for proteomics studies
Design of an hexapeptide database for proteomics studiesDesign of an hexapeptide database for proteomics studies
Design of an hexapeptide database for proteomics studies
 
Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists
Glucagon-Like Peptide-1 (GLP-1) Receptor AgonistsGlucagon-Like Peptide-1 (GLP-1) Receptor Agonists
Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists
 
Insilico Analysis towards Infuenza Virus- A Homology modelling and molecular ...
Insilico Analysis towards Infuenza Virus- A Homology modelling and molecular ...Insilico Analysis towards Infuenza Virus- A Homology modelling and molecular ...
Insilico Analysis towards Infuenza Virus- A Homology modelling and molecular ...
 
Drug discovery Using Bioinformatic
Drug discovery Using BioinformaticDrug discovery Using Bioinformatic
Drug discovery Using Bioinformatic
 
Incretin Therapy
Incretin TherapyIncretin Therapy
Incretin Therapy
 
Homology modeling and molecular docking
Homology modeling and molecular dockingHomology modeling and molecular docking
Homology modeling and molecular docking
 
Drug Target Identification
Drug Target IdentificationDrug Target Identification
Drug Target Identification
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
Standarization in Proteomics: From raw data to metadata files
Standarization in Proteomics: From raw data to metadata filesStandarization in Proteomics: From raw data to metadata files
Standarization in Proteomics: From raw data to metadata files
 
Rasa
RasaRasa
Rasa
 
Target identification in drug discovery
Target identification in drug discoveryTarget identification in drug discovery
Target identification in drug discovery
 

Similaire à Protein docking

The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...
The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...
The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...
Lucid Designs
 
PhD defense
PhD defensePhD defense
PhD defense
aamir064
 
poster_portrait
poster_portraitposter_portrait
poster_portrait
RyanMoodie
 
Presentation(11p)
Presentation(11p)Presentation(11p)
Presentation(11p)
Shiqi Zhang
 

Similaire à Protein docking (20)

7926563mocskoff pack method k sampling.ppt
7926563mocskoff pack method k sampling.ppt7926563mocskoff pack method k sampling.ppt
7926563mocskoff pack method k sampling.ppt
 
Quantum chemical molecular dynamics simulations of graphene hydrogenation
Quantum chemical molecular dynamics simulations of graphene hydrogenationQuantum chemical molecular dynamics simulations of graphene hydrogenation
Quantum chemical molecular dynamics simulations of graphene hydrogenation
 
Advanced Molecular Dynamics 2016
Advanced Molecular Dynamics 2016Advanced Molecular Dynamics 2016
Advanced Molecular Dynamics 2016
 
Quantum Computation for Predicting Electron and Phonon Properties of Solids
Quantum Computation for Predicting Electron and Phonon Properties of SolidsQuantum Computation for Predicting Electron and Phonon Properties of Solids
Quantum Computation for Predicting Electron and Phonon Properties of Solids
 
Recent developments for the quantum chemical investigation of molecular syste...
Recent developments for the quantum chemical investigation of molecular syste...Recent developments for the quantum chemical investigation of molecular syste...
Recent developments for the quantum chemical investigation of molecular syste...
 
The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...
The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...
The Combined Role of Thermodynamics and Kinetics in the Growth of Colloidal B...
 
Lecture: Interatomic Potentials Enabled by Machine Learning
Lecture: Interatomic Potentials Enabled by Machine LearningLecture: Interatomic Potentials Enabled by Machine Learning
Lecture: Interatomic Potentials Enabled by Machine Learning
 
MOLECULAR MODELLING
MOLECULAR MODELLINGMOLECULAR MODELLING
MOLECULAR MODELLING
 
PhD defense
PhD defensePhD defense
PhD defense
 
Materials Modelling: From theory to solar cells (Lecture 1)
Materials Modelling: From theory to solar cells  (Lecture 1)Materials Modelling: From theory to solar cells  (Lecture 1)
Materials Modelling: From theory to solar cells (Lecture 1)
 
CMR_Formal_Presentation2
CMR_Formal_Presentation2CMR_Formal_Presentation2
CMR_Formal_Presentation2
 
Viscosity of Liquid Nickel
Viscosity of Liquid NickelViscosity of Liquid Nickel
Viscosity of Liquid Nickel
 
poster_portrait
poster_portraitposter_portrait
poster_portrait
 
Electronic structure of strongly correlated materials Part II V.Anisimov
Electronic structure of strongly correlated materials Part II V.AnisimovElectronic structure of strongly correlated materials Part II V.Anisimov
Electronic structure of strongly correlated materials Part II V.Anisimov
 
Uv Vis Calculated Of Mv2+ And Mv+
Uv Vis Calculated Of Mv2+ And Mv+Uv Vis Calculated Of Mv2+ And Mv+
Uv Vis Calculated Of Mv2+ And Mv+
 
Multidimensional wave digital filtering network
Multidimensional wave digital filtering networkMultidimensional wave digital filtering network
Multidimensional wave digital filtering network
 
Presentation(11p)
Presentation(11p)Presentation(11p)
Presentation(11p)
 
Density functional theory (DFT) and the concepts of the augmented-plane-wave ...
Density functional theory (DFT) and the concepts of the augmented-plane-wave ...Density functional theory (DFT) and the concepts of the augmented-plane-wave ...
Density functional theory (DFT) and the concepts of the augmented-plane-wave ...
 
Structural systems
Structural systemsStructural systems
Structural systems
 
Adamek_SestoGR18.pdf
Adamek_SestoGR18.pdfAdamek_SestoGR18.pdf
Adamek_SestoGR18.pdf
 

Plus de Saramita De Chakravarti

Plus de Saramita De Chakravarti (7)

Data Retrieval Systems
Data Retrieval SystemsData Retrieval Systems
Data Retrieval Systems
 
Protein Structure, Databases and Structural Alignment
Protein Structure, Databases and Structural AlignmentProtein Structure, Databases and Structural Alignment
Protein Structure, Databases and Structural Alignment
 
QSAR : Activity Relationships Quantitative Structure
QSAR : Activity Relationships Quantitative StructureQSAR : Activity Relationships Quantitative Structure
QSAR : Activity Relationships Quantitative Structure
 
MOLECULAR DOCKING
MOLECULAR DOCKINGMOLECULAR DOCKING
MOLECULAR DOCKING
 
Molecular Markers: Major Applications in Insects
Molecular Markers: Major Applications in InsectsMolecular Markers: Major Applications in Insects
Molecular Markers: Major Applications in Insects
 
OLFACTION
OLFACTIONOLFACTION
OLFACTION
 
Synthesis and Actions of Juvenile Hormones In Insect Development (MS Power…
Synthesis and Actions of Juvenile Hormones In Insect Development (MS Power…Synthesis and Actions of Juvenile Hormones In Insect Development (MS Power…
Synthesis and Actions of Juvenile Hormones In Insect Development (MS Power…
 

Dernier

The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
heathfieldcps1
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
negromaestrong
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
Chris Hunter
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
 

Dernier (20)

Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Asian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptxAsian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptx
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17  How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17  How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIFood Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 

Protein docking

  • 1. Protein docking Saramita De Chakravarti 12/14/2012
  • 2. Outline  Physical background of protein  Protein docking
  • 3. Quantum Mechanism (QM)  Electrons  Nuclei  Wave functions instead of Newton Eq.  High computation cost  Ab initio N4 N is # of base functions  Semi-empirical N3  Molecule of a couple of hundreds atoms  Pico-second(10-12 s) behavior
  • 4. Molecular Mechanism (MM)  Atoms  Newton equations  Parameters from experiment and QM  force field  System with 100,000 atoms  µs (10-6 s) behavior
  • 5. Force field +−+−= ∑∑ 22 )()( eq angle eq bonds b krrkU θθθ ])[()]cos(1[ 2 612 ij ji ij ij i ij ij ij dihedral n n r qq r B r A n V ⋅ +−+−+ ∑∑∑ ∑ > ε γφ kb – bond parameter kө --- angle parameter Vn --- dihedral energy barrier Van Der Waal radii Partial charge set
  • 6. Solvent effect  Oil in water  Hydrophobic interactions  Alcohol in water  Hydrophilic interactions  Explicit water  Solvent Accessible Surface  Surface Complementarity
  • 8. Sidechains The amino acids vary in their side chains (indicated in blue in the diagram). The eight amino acids in the orange area are nonpolar and hydrophobic. The other amino acids are polar and hydrophilic ("water loving"). The two amino acids in the magenta box are acidic ("carboxy" group in the side chain). The three amino acids in the light blue box are basic ("amine" group in the side chain).
  • 10. Proteins  Primary structure  Secondary structure  Tertiary structure  Quaternary structure
  • 11. Important forces  Weak forces  Delicate balance  Electrostatic (hydrogen bonds, salt bridges)  Hydrophobic (sidechain packing)
  • 13. Contents  Why Is Docking Important?  Why Is Docking Hard?  Docking Scoring Criteria  Docking Search Strategies  CAPRI Participants & Algorithms  Lessons Learnt From CAPRI  Selected Docking References http://www.csd.abdn.ac.uk/~dritchie/
  • 14. Why Is Docking Important?  Better understand the Machinery of Life  Enzyme-inhibitor class  Antibody-antigen class  Others  Engineered Protein Enzymes  Protein Therapies  Drug targets
  • 15. Why Is Docking A Hard Problem?  Large Search Space !  Rigid Body Docking: 6D  Flexible Docking: 3N (N normal modes)  Structure Space: Continuous
  • 16. Criteria for Good Docking Orientations  Low Free Energy  Low Pseudo-Energy Based On force field  Large Surface Burial  Small van der Waals Overlaps  Good H-Bonding  Good Charge Complementarity  Polar/Polar Contacts Favoured  Polar/Non-Polar Contacts Disfavoured
  • 17. Docking Search Strategies  Pseudo Random  Simulated Annealing / Monte Carlo  Genetic Algorithms  Directed Search  Geometric Hashing  Spherical Harmonic Surface Triangles
  • 18. Docking Search Strategies  Brute-Force Search  Explicit Grid Correlations  Fast Fourier Transform (FFT) Correlations  Refinement Phase  Classical or Soft Potentials (+/- Electrostatics)  Desolvation, Solvent Dipoles...  Visual Inspection!!
  • 19. CAPRI  Critical Assessment of PRedicted Interactions  http://capri.ebi.ac.uk/  targets available (unbounded/bounded)  Bounded (rigid)  Unbounded (non-rigid)  International groups participated
  • 20.
  • 22.
  • 23.
  • 24. d PNAS 1992 89 pp. 2195-2199
  • 26. The electric field by protein A
  • 27. Correlation function Binary filter to remove false positive geometries
  • 28. Lessons Learnt From CAPRI  Antibodies Often Bind Near Antigen's Active Site  Expect Large Conformational Change in Enzyme/Inhibitor Docking  Develop Better Models of Flexibility
  • 29. Future Challenges For Docking  Better Scoring Functions  High-Throughput Screening  Tractable Models of Flexibility
  • 30. Reference  Reviews Halperin et al.; Proteins, 47 409-443 (2002) Smith & Sternberg; COSB, 12 28-35 (2002)  CAPRI http://capri.ebi.ac.uk/  Algorithms Chen & Weng; Proteins, 47 281-294 (2002) Fernandez-Recio et al.; Prot Sci, 11 280-291 (2002) Gardiner et al.; Proteins, 44 44-56 (2001) Camacho et al.; Proteins, 40 525-537 (2000) Palma et al.; Proteins, 39 372-384 (2000) Ritchie & Kemp; Proteins, 39 178-194 (2000) Gabb et al.; J Mol Biol 272 106-120 (1997) Vakser; Proteins, S1 226-230 (1997) Abagyan et al.; J Comp Chem, 15 488-506 (1994) Norel et al.; Prot Eng 7 39-46 (1994) Katchalski-Katzir et al.; PNAS, 89 2195-2199 (1992)