Protein-ligand complexes
 Biological relevance
 Molecular recognition
 Structure of complexes
 Protein druggability
 Small molecules
 Molecular docking
 Evaluation of complexes
 Transport of small molecules
Outline
2Protein-ligand complexes
Protein-ligand complexes
3Biological relevance
Why do we care?
Examples?
 Cell signaling & regulation
 Binding of small molecules to receptors
 Molecular function of ligands/receptors
 Selectivity of receptors
 Signaling pathways
 Transport mechanisms
 Homeostasis of the cell
 …
Biological relevance
4Biological relevance
 Metabolism
 Binding of small molecules to enzymes
 Molecular function of enzymes
 Activation of enzymes and molecular pathways
 Bioactivation and clearance of drugs and xenobiotics (P450s,…)
 Enzymatic cascades
 Metabolic interferences (competing pathways)
 …
5Biological relevance
Biological relevance
 Drug discovery
 Binding of small molecules to macromolecules
 Identification of targets (enzymes, receptors, ...)
 Identification of potential target inhibitors/activators
 Optimization of target modulators
 Repurposing of drugs – finding new receptors
 Adverse side-effects due to binding
to off-targets
 …
6Biological relevance
Biological relevance
 Binding
 Specific binding governed by complementarity
 Geometry and shape
 Physicochemical properties (interactions)
Biophysical aspects
Histone octamer
Molecular recognition – biological roles 7
 Catalysis
 Chemical reactions can be accelerated up to 17 orders of
magnitude
 Binding to active site decreases the energy barrier of the reaction
 Stabilization of Transition State
Hammerhead ribozyme
Molecular recognition – biological roles
Biophysical aspects
8
 Signaling
 Conformational changes in response to
 Ligand binding
 Properties of surrounding environment (pH, forces… )
 Different conformations recognized by different proteins in
signaling pathways  control of cellular processes
Guanine riboswitch
Molecular recognition – biological roles
Biophysical aspects
9
 Formation of complex structures
 Structural elements of complex systems
 Governed by specific association of protein subunits
 With themselves
 Other proteins, carbohydrates, lipids, …
Molecular recognition – biological roles
Biophysical aspects
10
 Molecular recognition refers to the specific interactions
between two or more molecules through non-covalent bonding
 Different biological roles
 Specific binding
 Catalysis
 Signaling
 Several models to explain molecular recognition
Molecular recognition
Molecular recognition 11
 E. Fisher – 1894
Lock-and-key model
Molecular recognition – mechanisms 12
What is it?
 E. Fisher – 1894
 Complementarity between receptor’s binding site
and the ligand
 Size & shape
 Physicochemical properties
 Both ligand and receptor are considered rigid
 Not sufficient to explain allostery, non-competitive inhibition,
or catalysis
  Model dismissed, only used for educational purposes
Lock-and-key model
Molecular recognition – mechanisms 13
 D. E. Koshland – 1956
Induced-fit model
Molecular recognition – mechanisms 14
What is it?
 D. E. Koshland – 1956
 Only partial complementarity necessary
 Both ligand and receptor can undergo conformational
adjustments upon complexation
 Conformation of the bound receptor does not exist in its free state
Induced-fit model
Molecular recognition – mechanisms 15
 B. F. Straub – 1964
 This model is also called: conformational selection, fluctuation-fit
or population selection
 Receptor and ligand flexible  considered as ensembles
 Complex is formed in a lock-and-key fashion when two
complementary configurations occur
 Conformation of the bound receptor exists also in its free state
Selected-fit model
Molecular recognition – mechanisms 16
 Z. Prokop – 2012
 When the receptor has a buried active site and tunnels
 Complementarity with the ligand is needed for both the
active site and tunnel
 Explains the extra selectivity filter provided by the tunnel
Keyhole-lock-key model
Molecular recognition – mechanisms 17
 Enzymes increase the speed of chemical reactions by
decreasing the activation barrier
Biocatalysis
Molecular recognition – biocatalysis
▪ Kinetic rate:
(Arrhenius equation)
▪ Lower Ea  higher k
(faster reaction)
Ea
Ea
H
with
enzyme
without
enzyme
G‡
reactants
enzyme-substrate
complex enzyme-products
complex
products
transition
state
𝑘 = 𝐴𝑒
−𝐸 𝑎
𝑅𝑇
18
 Enzymes increase the speed of chemical reactions by
decreasing the activation barrier
 Provide environments that stabilize the transition state(s)
Biocatalysis
Molecular recognition – biocatalysis
Ea
Ea
H
with
enzyme
without
enzyme
G‡
reactants
enzyme-substrate
complex enzyme-products
complex
products
transition
state
19
Structure of complexes
20Structure of complexes
 Complexes in RSCB PDB
 Databases of complexes
 PDBbind
 BindingDB
 ChEMBL
 …
 Experimentally determined complexes!
Complexes in RSCB PDB
21Structure of complexes
 Limited number of available complexes
 >180,000 protein structures
 >101,000 structures with ligands
 Limited information on conformation of bound ligand
 Ligands often quite mobile -> uncertainties -> need to be verified
Databases of complexes
22Structure of complexes
 PDBbind
 http://www.pdbbind.org.cn
 Binding affinity data and structural information on >16,500
complexes
 >13,500 protein-ligand
 >120 nucleic acid-ligand
 >800 protein-nucleic acid
 >2,000 protein-protein complexes
 Data collected from >29,000 original references
 Provides also a "refined set" and "core set" compiled as high-quality
data sets of protein-ligand complexes for docking/scoring studies
Databases of complexes
23Structure of complexes
 PDBbind
Databases of complexes
24Structure of complexes
 PDBbind
Databases of complexes
25Structure of complexes
 PDBbind
Databases of complexes
26Structure of complexes
 PDBbind
Databases of complexes
27Structure of complexes
 BindingDB
 www.bindingdb.org
 Focus on the interactions of proteins considered to be drug-targets
with drug-like molecules
 Contains about 1,500,000 entries of binding data
 >7,000 protein targets
 >650,000 small molecules
 Crystal structures of complexes with measured affinity
 >2,500 – for proteins with 100% sequence identity
 >6,000 – for proteins up to 85% sequence identity
Databases of complexes
28Structure of complexes
 BindingDB
Databases of complexes
29Structure of complexes
 BindingDB
Databases of complexes
30Structure of complexes
 ChEMBL
 https://www.ebi.ac.uk/chembldb/
 Is a manually curated database of bioactive molecules with
drug-like properties
 Database of binding, functional and ADME (Absorption,
Distribution, Metabolism, and Excretion) and toxic. information
 Contains >15,000,000 activity data
 >12,000 protein targets
 >1,700,000 distinct small molecules
 Data collected from >67,000 original publications
 Smart clustering of relevant information
Databases of complexes
31Structure of complexes
 ChEMBL
Databases of complexes
32Structure of complexes
 ChEMBL
Databases of complexes
33Structure of complexes
 ChEMBL
 Druggability
 Likelihood of a particular protein to be modulated or targeted by a
drug-like molecule in a way that leads to a therapeutic effect
 It means bind with high affinity to selective, bioavailable,
low-molecular weight molecules
 Lipinski’s rule of 5 (for orally-active drugs)
 MW < 500 Da
 < 5 H-bond donors (NH, OH); < 10 H-bond acceptors (F, O, N)
 Partition coefficient (log Po/w) < 5
 Usually 1 violation is acceptable
Protein druggability
34Protein druggability
 Druggability
Protein druggability
35Protein druggability
 Prediction of protein druggability
 By similarity to known target
 Sequence of binding domain
 Structural features of binding sites
 From databases of known targets
 Predictive tools: PockDrug Server, DoGSiteScorer, …
 Important in target identification phase of drug discovery
 Unfortunately, many resources are only private or
commercial
Protein druggability
36Protein druggability
 PockDrug-Server
 http://pockdrug.rpbs.univ-paris-diderot.fr/
 Automatic tool combining pocket detection, characterization and
druggability prediction
 Based on:
 Physicochemical features
 Geometry, volume, shape
 Druggability probability for
one pocket or to compare two
pockets
Protein druggability server
37Protein druggability
 Proteins Plus
 https://proteins.plus/
 Meta-server providing global support for the initial steps in
analysing protein structures
 Structure search, quality assessment, protein pocket detection,
protein-ligand and protein-protein
interactions
 Predicts binding sites and estimates
their druggability (using DoGSiteScorer)
Protein-ligand interactions server
38Protein druggability
 Representation of small molecules
 Databases of small molecule
 Cambridge Structural Database
 PUBCHEM database
 ZINC database
 Preparation of small molecule structure
Small molecules
39Small molecules
 1D – atom based (empirical formula)
 C2H5Cl
 2D – chemical structure diagram -> connection
 Topology or SMILES (Simplified Molecular Line Entry System)
 3D – atomic coordinates
 Usually: PDB, SDF or MOL2 files
 Beware: may have different protonation states
Representation of small molecules
40Small molecules
CCCl C1=CC=C(C=C1)CN
Databases of small molecule
 Cambridge Structural Database
 http://www.ccdc.cam.ac.uk/products/csd/
 The world largest repository of crystal structures of small molecules
 >900,000 structures with 3D coordinates available
 CSD is distributed commercially
 Free interactive demo for educational purposes
(only ~750 structures)
 https://www.ccdc.cam.ac.uk/Community/educationalresources/
teaching-database/
Small molecules 41
Databases of small molecule
 Cambridge Structural Database
Small molecules 42
Databases of small molecule
 PubChem
 http://pubchem.ncbi.nlm.nih.gov/
 World largest open repository of experimental data identifying the
biological activities of small molecules
 Substances: >270 M chemical entities
 Compound: >111 M unique chemical structures. Compounds may
be searched by chemical properties and are pre-clustered by
structure comparison into identity and similarity groups
 BioAssays: >1.4 M biological experiments
 Bioactivities: >300 M biological activity data points
Small molecules 43
Databases of small molecule
 ZINC database
 http://zinc.docking.org/
 Free public resource for ligand discovery
 3D coordinates in ready-to-dock formats (ex: added hydrogens,
partial atomic charges, … )
 Molecules in biologically relevant protonation and tautomeric forms
 About 37 billion unique molecules grouped by classes
 >750,000,000 – commercially available molecules
 >10,000,000 – drug-like molecules
 > 5,000 – FDA-approved drugs
 …
Small molecules 44
Preparation of small molecule structure
 AVOGARO
 https://avogadro.cc/
 Free, open-source molecule editor and visualizer
 Intuitive & easy to use
 Useful to convert file formats
 Embedded molecular minimization and molecular mechanics
 Interface to quantum chemistry packages
Small molecules 45
Preparation of small molecule structure
 AVOGARO
Small molecules 46
Preparation of small molecule structure
 PyMOL
 https://pymol.org/
 Powerful molecular visualizer and editor
Small molecules 47
Preparation of small molecule structure
 Open Babel
 https://openbabel.org/
 Free, open-source
 Widely used molecule format converter
 Command line and graphical interface
Small molecules 48
Molecular docking
49Molecular docking
What is it?
 Useful when experimental data is not available
or for virtual screening
Molecular docking
50Molecular docking
Crystal
(experimental)
Docking attempts
Score
RMSD
 Several components/steps
 Receptor representation
 Ligand representation
 Search of binding modes
 Scoring
Molecular docking
51Molecular docking
Receptor Ligand Complex
 Receptor represented only by relevant binding site
 Descriptor representation – derived from geometry
and interaction abilities of binding site
(H-bond donor/acceptor, hydrophobic contacts, …)
 Grid representation – entire searched region
is covered by orthogonal equidistant
points carrying information about
interactions of probe atom at this point
with receptor atoms
Receptor representation
52Molecular docking – receptor
 Receptor flexibility
 Fully rigid approximation
 Soft docking – employs tolerant “soft” scoring functions to simulate
plasticity of otherwise rigid receptor
 Explicit side-chain flexibility – optimization of residues by rotating
part of their structure or rotation of whole side-chains using
predefined rotamer libraries
 Docking to molecular ensemble of protein structure – obtained
from multiple crystal structures, from NMR structure determination
or from a trajectory produced by MD simulation
Receptor representation
53Molecular docking – receptor
 Ligands represented by all atoms or just some
 Non-polar hydrogens can be united with their respective parent
carbon atoms to reduce number of atoms in calculation
 Ligand flexibility
 Only rotation about single bonds
 Docking of a library of pre-generated ligand conformations –
applicable only to quite rigid ligands due to exponential increase in
number of possible conformers with number of rotatable bonds
 Direct sampling of ligand conformational space during searching
 Fragment-based techniques – ligand is cut into several fragments
and rigidly docked into binding site
Ligand representation
54Molecular docking – ligand
Molecular docking – search
55Molecular docking – search
 Many search algorithms available
 Rigid docking 
 Semi-flexible 
 Fully flexible 
(but demanding)

 Geometry-based and combinatorial algorithms
 Assumes that binding is governed by shape and/or physicochemical
complementarity between the ligand and the receptor
 Assumes that the degree of complementarity is proportional to
the binding energy which is not always true especially for
more polar ligands
 Energy-driven and stochastic algorithms
 Tries to locate directly the global minimum of the binding free
energy corresponding to the experimental structure
 Random basis of these methods requires multiple independent runs
of docking calculations to achieve consistent results
Molecular docking – search
56Molecular docking – search
 Matching algorithms
 Represent a ligand and a receptor binding site by descriptors
derived from their geometry and/or presence of particular
interaction sites
 Try to align/match complementary parts of ligand and binding site
and in this way predict the ligand binding mode
 SW packages
 DOCK – http://dock.compbio.ucsf.edu/
 SLIDE – http://www.kuhnlab.bmb.msu.edu/software/slide/
 …
Geometry-based algorithms
57Molecular docking – search
 Matching algorithms
Geometry-based algorithms
58Molecular docking – search
 Fragment-based algorithms
 Ligand is initially fragmented into rigid parts
 Two approaches to obtain whole docked molecule
 Incremental construction – fragments are incrementally
docked into the receptor until whole ligand is constructed
 Fragment-placing and linking – all fragments are docked
simultaneously and then joined together
 SW packages
 FlexX – http://www.biosolveit.de/FlexX/
 eHITS – http://www.simbiosys.ca/ehits/
 …
Geometry-based algorithms
59Molecular docking – search
 Fragment-based algorithms
Geometry-based algorithms
60Molecular docking – search
 Monte Carlo algorithms
 Explore protein-ligand interactions space by iteratively introducing
random changes into a position, orientation or conformation of the
ligand and evaluating new configuration using acceptance criterion
 New configuration is always accepted if its energy is more favorable
then the energy of previous configuration or accepted with some
probability reflecting energy difference to previous configuration
 SW packages
 Autodock Vina – http://vina.scripps.edu
 Glide – http://www.schrodinger.com/Glide
 …
Stochastic energy-driven algorithms
61Molecular docking – search
 Monte Carlo algorithms
Stochastic energy-driven algorithms
62Molecular docking – search
 Genetic algorithms
 Configurations of the ligand from randomly generated initial
population are encoded in their “genes” which are subject of
random genetic modification (single point mutation, crossover, …)
 Individuals with better fitness (binding energy) have higher
chance to survive and reproduce to next generation
 Overall fitness of population is increasing with each new generation
 SW packages
 AutoDock – http://autodock.scripps.edu
 GOLD – http://www.ccdc.cam.ac.uk/products/life_sciences/gold/
 …
Stochastic energy-driven algorithms
63Molecular docking – search
 Genetic algorithms
Stochastic energy-driven algorithms
64Molecular docking – search
 Scoring function
 Evaluate all the binding modes from the searching algorithms
 Must be computationally efficient and provide accurate description
of protein-ligand interactions
 Application of scoring functions to rank
 Several configurations of one ligand bound to one protein –
essential for prediction of the best binding mode
 Different ligands bound to one protein – determination of
substrate or inhibitor specificity
 One ligand bound to several different proteins – functional
annotation of proteins and study of drug selectivity
Molecular docking – scoring
65Molecular docking – scoring
 Categories of scoring functions
 Empirical
 Knowledge-based
 Force field-based
 Machine learning
Molecular docking – scoring
66Molecular docking – scoring
 Categories of scoring functions
 Empirical
 Derived by fitting of following equation to experimental binding
affinities of known protein-ligand complexes
 Rapid evaluations
 Arbitrary selection of terms included in the equation  failure
when binding is governed by any excluded type of interaction
 Weights are dependent on the chosen training set
Molecular docking – scoring
67Molecular docking – scoring
.......  rotellipohbbind GGGGG 
 Categories of scoring function
 Knowledge-based
 Capture the knowledge about protein-ligand binding that is
implicitly stored in structural data by statistical analysis
 Atom-pair potentials derived from
distances found for such pair
in training structural data
 Rapid evaluations
 Describe all types of interactions without any preselection
 Problem when structural data do not contain sufficient
information on specific atom-pairs (ex. halogens, metals, …)
Molecular docking – scoring
68Molecular docking – scoring
 Categories of scoring function
 Force field-based
 Use the non-bonded terms of well-established force fields
 Provide precise affinities
 Computationally demanding  employed for rescoring
selected binding modes (not during searching)
Molecular docking – scoring
69Molecular docking – scoring
 Intermolecular interactions
 Binding energies
Evaluation of complexes
70Evaluation of complexes
 Most common types
 Hydrogen bonds
 Hydrophobic
 Aromatic
 Ionic bonds
Intermolecular interactions
71Evaluation of complexes
 Visualization
 Schematic diagrams showing hydrogen bonds and hydrophobic
contacts
 Tools
 LigPlot+
 Stand alone application
 http://www.ebi.ac.uk/thornton-srv/software/LigPlus/
 Pre-calculated for protein-ligand complexes in PDBsum
(pictorial database of PDB structures)
Intermolecular interactions
72Evaluation of complexes
 Binding Affinity Prediction of Protein-Ligand (BAPPL) server
 http://www.scfbio-iitd.res.in/software/drugdesign/bappl.jsp
 Calculates binding free energy
of a protein-ligand complex
using all-atom-energy-based
empirical scoring function
 Only for non-metallo protein-ligand
complexes
Binding energies
73Evaluation of complexes
 Describe trajectory of ligands through tunnels
 Based on geometry or molecular docking
 Fast but low accuracy
 Good for screening purposes
 CaverDock, MoMA-LigPath, SLITHER
 Based on force field
 Run multiple MD simulations
 Accurate but computationally demanding
 Metadynamics, steered MD, adaptive sampling, etc.
Transport of small molecules
74Transport of small molecules
 CaverDock
 https://loschmidt.chemi.muni.cz/caverdock/
 Analysis of tunnels by Caver
 Discretization of identified tunnel into discs
 Molecular docking by AutoDock Vina to every disc
 Caver Web
 https://loschmidt.chemi.muni.cz/caverweb/
 Web interface for Caver and CaverDock
Transport of small molecules
75Transport of small molecules
CAVER
Discretization
CaverDock
76
CaverDock
Transport of small molecules
Active
site
 Results provided:
 Ligand trajectory
 Energy profile
77
CaverDock
Transport of small molecules
-5
-3
-1
1
3
5
7
9
11
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
2.3
2.5
0 5 10 15
Energy(kcal/mol)
Tunnelradius(Å)
Trajectory along the tunnel [Å]
+
Tunnel
bottleneck
78
CaverDock over Caver Web
Transport of small molecules
References I
 Gu, J. & Bourne, P. E. (2009). Structural Bioinformatics, 2nd Edition,
Wiley-Blackwell, Hoboken.
 Pérot, S. et al. (2010). Druggable pockets and binding site centric
chemical space: a paradigm shift in drug discovery. Drug Discovery
Today 15: 656-667.
 Moitessier, N. et al. (2008). Towards the development of universal, fast
and highly accurate docking/scoring methods: a long way to go. British
Journal of Pharmacology 153: S7-S26.
References 79
References II
 Bolton, E. E. et al. (2008). PubChem: Integrated platform of small
molecules and biological activities. Annual Reports in Computational
Chemistry 4: 217-241.
 Gaulton, A. et al. (2012). ChEMBL: a large-scale bioactivity database for
drug discovery. Nucleic Acids Research 40: D1100-D1107.
 Irwin, J. J. et al. (2012). ZINC: A free tool to discover chemistry for
biology. Journal of Chemical Information and Modeling 52: 1757-1768.
 Santos, R. et al. (2017). A comprehensive map of molecular drug targets.
Nature Reviews Drug Discovery. 16: 19-34
References 80