Analysis of protein structures
Outline
 Residue solvent accessibility
 Protein solubility
 Molecular interactions
 Functional sites
 Binding sites
 Transport pathways
Analysis of protein structures 2
Residue solvent accessibility
 Solvent accessible surface area
Residue solvent accessibility 3
What is it?
Why do we care?
Residue solvent accessibility
 Solvent accessible surface area (ASA, SASA or SAS, in Å2)
 It quantifies the extent to which a residue in a protein
structure is accessible to the solvent
 Typically calculated by rolling a spherical probe of a
particular radius over a protein surface and summing the
area that can be accessed by this probe on each residue
Residue solvent accessibility 4
=
Residue solvent accessibility
 Solvent accessible surface area (ASA, SASA or SAS, in Å2)
 Solvent excluded surface (SES) – also known as molecular
surface, or Connolly surface area
Water radius  1.4 Å
5Residue solvent accessibility
VdW
Ai
exp = exposed area
VdW = Van der Waals radius
Residue solvent accessibility
 Solvent accessible surface area (ASA, SASA or SAS, in Å2)
 Solvent excluded surface (SES) – also known as molecular
surface, or Connolly surface area – usually represented in
“surface” visualization
6Residue solvent accessibility
SASA SES
Residue solvent accessibility
 Relative accessible surface area (rASA)
 Ratio of the actual accessible area of a given residue
rASA = ASA / ASAMAX
 Enables comparison of accessibility of different amino acids (e.g.,
long extended vs. spherical amino acids)
 Simplified two state description
 Buried vs. exposed residues
 Threshold for differentiating surface residues vs. buried is not well
defined (usually rASA = 15–25 %)
 rASA < threshold => buried
rASA ≥ threshold => exposed
7Residue solvent accessibility
Residue solvent accessibility – programs
 POLYVIEW-2D (PDB) / SABLE (sequence)
 https://polyview.cchmc.org/ ; https://sable.cchmc.org/
 Visualization tool for structural and functional annotations of
proteins, including solvent accessibility
 Residue SASA calculated by DSSP and transformed to rASA
9Residue solvent accessibility
Protein solubility
 Concentration of protein in saturated solution that is in
equilibrium with solid phase
 For proteins expressed in the lab, it depends on
 Hydrophilic/hydrophobic balance of the solvent-exposed residues
 Aggregation-prone regions (APRs) – mainly hydrophobic residues
prone to form beta-structures
 Protein expressibility in the cells
Protein solubility 10
Cross-beta spines of amyloid fibrils
Protein solubility
 SoluProt
 https://loschmidt.chemi.muni.cz/soluprot/
 Soluble expression of protein sequences in E.coli
 Based on machine learning
Input Output
11Protein solubility
Protein solubility
 Aggrescan3D
 http://biocomp.chem.uw.edu.pl/A3D2/
 Predicts the aggregation propensities by identifying APRs
 Can introduce mutations and the predict impact on stability and
aggregation-propensity
 Can account for protein flexibility (“dynamic mode”)
Mutations
12Protein solubility
Molecular interactions
 Intra-molecular – within the same protein structure
 Inter-molecular – between proteins in an assembly
 Essential to understand the molecular basis for function
and stability of proteins and their complexes
13Molecular interactions
Which types?
Types of interactions
 Charge-charge (ionic) interactions
 Present in charged residues; ex. salt bridges
 Hydrogen bonds (H-bonds)
 Donor and acceptor atoms sharing hydrogen
 Aromatic (π-π) interactions
 Attractive interaction between aromatic rings
 Van der Waals (vdW) interactions
 Between any two atoms; more important for non-polar residues
 Hydrophobic interactions
 Entropic origin; important for non-polar/hydrophobic residues
Molecular interactions 14
Types of interactions
 Disulfide bonds (cysteine bridges)
 Cation-π interactions
 Electrostatic interaction of a positively charged residue (Lys or Arg)
with an aromatic residue (Phe, Trp, or Tyr)
Lys
Trp
15Molecular interactions
Aromatic ring
Cation +
2 Cys
Molecular interactions – how to identify?
 Criteria for recognizing various types of interactions
 Geometric rules (distances, angles)
 Atom types
 Energetics (physicochemical rules)
 Contact surface area between atoms
16Molecular interactions
If SASATotal < SASAA + SASAB
 Interaction
Molecular interactions – programs
 CMView
 https://www.bioinformatics.org/cmview/
 Represents residue-residue contacts within a protein or between
proteins in a complex in the form of a contact map
 3D visualization using PyMol
17Molecular interactions
Molecular interactions – programs
 PIC (Protein Interactions Calculator)
 http://pic.mbu.iisc.ernet.in/
 Identifies various interactions – hydrophobic interactions, ionic
(charge-charge) interactions, hydrogen bonds, aromatic–aromatic,
aromatic–sulfur, cation–π interactions, and disulfide bonds, within
a protein or between proteins in a complex
 Uses standard criteria (atom types and geometry)
18Molecular interactions
Molecular interactions – programs
 PIC (Protein Interactions Calculator)
 http://pic.mbu.iisc.ernet.in/
19Molecular interactions
Functional sites
Functional sites 21
Examples?
Why are they
important?
Functional sites
 Binding sites
 Binding sites for small molecules
 Binding sites for macromolecules
 Transport pathways
 Voids
 Tunnels
 Channels
Functional sites 22
Binding sites
 Sites on the protein that provides the complementarity for
the bound molecule (ligand)
 Binding site – its function is molecular recognition
 Active/catalytic site – special case of the binding site – its function is
to promote chemical catalysis (break/formation of covalent bonds)
 Binding involves the formation of non-covalent interactions
between the protein and the bound molecule
 Bound molecule – small molecule or macromolecule
 Binding is usually very specific – complementarity in shape
and charge distribution between the site and bound molecule
Functional sites → binding sites 23
Binding sites
Complementarity in shape and charge distribution
between the active site and substrate
24Functional sites → binding sites
Binding sites for small molecules
 Usually: internal cavities, surface pockets or clefts
 Concave regions
 Provide microenvironment different from that of the bulk solvent
(e.g., many residues with negative charge → very strong
electrostatic field enabling binding of highly charged ligands)
 Often identifiable by a simple examination of the protein structure
 Highly conserved by evolution
 Low desolvation energy
 Characteristic physicochemical properties
Functional sites → binding sites → binding sites for small molecules 25
Binding sites for small molecules
Active site
pocket on the
protein surface
Active site
cavity buried
inside the
protein core
Functional sites → binding sites → binding sites for small molecules 26
 residues influencing binding of ligands, transition-state
stabilization or product release
LigandActive site pocket
Binding sites for small molecules
27Functional sites → binding sites → binding sites for small molecules
 Can be very ligand-specific
 residues influencing binding of ligands, transition-state
stabilization or product release
Residue to be mutated
Binding sites for small molecules
28Functional sites → binding sites → binding sites for small molecules
 Can be very ligand-specific
 residues influencing binding of ligands, transition-state
stabilization or product release
Binding sites for small molecules
Before mutation
29Functional sites → binding sites → binding sites for small molecules
 Can be very ligand-specific
 residues influencing binding of ligands, transition-state
stabilization or product release
Binding sites for small molecules
30Functional sites → binding sites → binding sites for small molecules
 Can be very ligand-specific
Before mutation
 residues influencing binding of ligands, transition-state
stabilization or product release
After mutation
Binding sites for small molecules
31Functional sites → binding sites → binding sites for small molecules
 Can be very ligand-specific
 residues influencing binding of ligands, transition-state
stabilization or product release
No longer a good fit!
Binding sites for small molecules
32Functional sites → binding sites → binding sites for small molecules
 Can be very ligand-specific
After mutation
Binding sites for small molecules
 Approaches to identify binding sites:
 Evolutionary conservation
 Physical detection of “pockets”
 Geometry based methods
 Energy based methods
 Binding site similarity
 Template-based methods
 Microenvironment-based methods
33Functional sites → binding sites → binding sites for small molecules
Evolutionary conservation
 Residues important for protein function or stability tend to
be highly conserved over evolution
 Residue conservation in a set of related proteins can be
derived from a multiple sequence alignment (MSA)
 Mapping of conservation on structure can reveal patches of
conserved surface residues – potential binding sites
 Protein interior usually more conserved than surface – not
suitable for prediction of buried cavities
 Not very specific – better to combine with other features
34Functional sites → binding sites → binding sites for small molecules
Evolutionary conservation
Map of evolutionary data
on the structure
Phylogenetic analysis
Conservation scoring
35Functional sites → binding sites → binding sites for small molecules
Evolutionary conservation
 ConSurf
 http://consurf.tau.ac.il/
 Estimates the level of evolutionary conservation of individual
positions in protein and maps this information onto its 3D structure
 Conservation score is derived based on the site-specific evolutionary
rates calculated for each position by Rate4Site software
 ConSurfDB – pre-calculated conservation scores for all structures
from wwPDB
36Functional sites → binding sites → binding sites for small molecules
Evolutionary conservation
 ConSurf
37Functional sites → binding sites → binding sites for small molecules
Physical detection of “pockets”
 Analyze the protein surface for pockets (clefts, cavities)
 Geometry-based methods
 Define favorable cleft regions based on steric assessments
 Energy-based methods
 Define favorable cleft regions based on energetic evaluations
38Functional sites → binding sites → binding sites for small molecules
Geometry-based methods
 Computed Atlas of Surface Topography of proteins (CASTp)
 http://sts.bioe.uic.edu/castp
 Uses computational geometry methods including Delaunay
triangulation, alpha shape and discrete flow theory
 Measures the volume and surface area of each pocket and cavity
using the ASA model and molecular surface (Connolly) model
Delaunay
triangulation
Alpha shape
Voronoi
diagram
39Functional sites → binding sites → binding sites for small molecules
Energy-based methods
 Pockets are defined by energetic criteria
 Evaluate the interaction energy between the protein and a
molecular fragment – probe (e.g., a methyl, hydroxyl, amine,
etc.) to locate energetically favorable binding sites
 Can be combined with other methods to assess the
ligandability (ability of a cavity to bind ligands)
41Functional sites → binding sites → binding sites for small molecules
Note: druggability is referred to the likelihood of finding
orally bioavailable small molecules that bind to a
particular target in a disease-modifying way.
Ligandability is a requirement but not sufficient condition
for druggability.
Energy-based methods
 Cavity Plus
 http://www.pkumdl.cn/cavityplus
 Applies Cavity program to detect the potential binding sites and
rank them with ligandability and druggability scores
 Extracts pharmacophore features within the cavities
42Functional sites → binding sites → binding sites for small molecules
Energy-based methods
 Cavity Plus
43Functional sites → binding sites → binding sites for small molecules
Binding site similarity
 Prediction of binding sites is based on the similarity with
other (known) binding sites
 Template-based methods
 Binding sites are represented by 3D templates
 Based on similarity with homologous proteins
 Microenvironment-based methods
 Based on description of local environment,
such as type of residues, their distances, solvent
accessibility and physicochemical properties
44Functional sites → binding sites → binding sites for small molecules
Template-based methods
 Definition and construction of 3D templates of features
 Local structural motifs, patterns and descriptors that characterize the
binding sites (e.g., functional groups, shape, solvent accessibility, etc.)
 Capture the essence of the binding sites in protein
 Usually apply constraints on atom types and occasionally sequential
relationships
 Search a database for structures using template as a query
 Identification of structures with a given binding site
 Compare the query structure against a 3D template database
 Identification of potential binding sites in the query structure
45Functional sites → binding sites → binding sites for small molecules
Template-based methods
 PINTS
 http://www.russelllab.org/cgi-bin/tools/pints.pl
 To compare a protein structure against a database of 3D patterns
(templates), as well as 3D templates against a database of protein
structures
 Additionally allows comparison of two structures
 The 3D template database includes ligand-binding sites and SITE
annotations from PDB files
Functional sites → binding sites → binding sites for small molecules 46
Template-based methods
 ProFunc
 http://www.ebi.ac.uk/thornton-srv/databases/profunc/
 Aims to identify the most likely function of a protein from its 3D
structure
 Uses several methods, including fold matching, residue
conservation, surface cleft analysis, and functional 3D templates
(templates for enzyme active sites, ligand-binding templates, DNAbinding
templates, reverse template comparison vs. structures in
wwPDB)
Functional sites → binding sites → binding sites for small molecules 47
Template-based methods
 Mechanism and Catalytic Site Atlas
 https://www.ebi.ac.uk/thornton-srv/m-csa/
 Database that provides information about the active sites, catalytic
residues and reaction mechanisms in enzymes with experimentally
determined 3D structure
 Defines catalytic residues as the residues directly involved in some
aspect of the enzymatic reaction
 Provides 3D templates for catalytic sites in the database
48Functional sites → binding sites → binding sites for small molecules
Binding sites for macromolecules
Functional sites → binding sites → binding sites for macromolecules 49
What’s different?
Binding sites for macromolecules
 Typically protruding loops, large surface clefts but also flat
binding sites – flatter than binding sites for small molecules
 Recognition of a macromolecule involves interactions over a large
continuous surface area or several discrete binding regions
 Difficult to identify by a simple examination of the protein structure
 High evolutionary conservation
 Low desolvation energy
 Characteristic physicochemical properties
 DNA binding sites have characteristic motifs and positive
charged electrostatic patches
Functional sites → binding sites → binding sites for macromolecules 50
Binding sites for macromolecules
protein-protein
complex
protein-DNA
complex
51Functional sites → binding sites → binding sites for macromolecules
Binding sites for macromolecules
 Approaches to identify binding sites
 Evolutionary conservation
 Knowledge-based
 Meta-servers (tools that combine several methods)
52Functional sites → binding sites → binding sites for macromolecules
Evolutionary conservation methods
 Same principles as for binding sites of small molecules
(see above)
 WHISCY
 https://wenmr.science.uu.nl/whiscy/
 Predicts protein-protein interface using conservation and structural
information (interface propensities for each residue at the surface
are used to adjust the score)
53Functional sites → binding sites → binding sites for macromolecules
Knowledge-based methods
 Combine multiple interface features
 Conservation
 Residue propensity for being at protein-protein interfaces
 Physicochemical properties
 Structural properties
 Use known binding sites for parameterization or training →
empirical scoring functions and machine learning methods
54Functional sites → binding sites → binding sites for macromolecules
Knowledge-based methods
 CONS-PPISP (consensus Protein-Protein Interaction Site Predictor)
 http://pipe.scs.fsu.edu/ppisp.html
 Utilizes machine learning to predict protein binding sites
 Trained on position-specific sequence profiles and solvent
accessibilities of each residue and its spatial neighbors
 Patch Finder Plus
 http://pfp.technion.ac.il/
 Utilizes machine learning primarily to find DNA binding regions
 Identifies the largest positive electrostatic patch on a protein surface
– combination of residue frequency, composition and conservation,
surface concavity, accessible area and H-bond potential
55Functional sites → binding sites → binding sites for macromolecules
Meta-servers
 Combine multiple methods to improve prediction accuracy
 META-PPISP (Protein Protein Interaction Site Predictor)
 http://pipe.scs.fsu.edu/meta-ppisp.html
 Combines cons-PPISP, ProMate and PINUP
 PI2PE (Protein Interface/Interior Prediction Engine)
 http://pipe.scs.fsu.edu/
 Pipeline to use five different predictors including cons-PPISP, metaPPISP
and DISPLAR
56Functional sites → binding sites → binding sites for macromolecules
Transport pathways
Functional sites → transport pathways 57
What are these?
Examples?
Transport pathways
 Mediate transport of ions and small molecules in proteins –
an essential role in functioning of large variety of proteins
 Channels/pores – transport of substances across membranes
 Tunnels – exchange of ligands between buried active/binding site
cavities and the bulk solvent
 Intramolecular tunnels – transport of reaction intermediates
between two distinct active sites in bifunctional enzymes
 The permeability to different substances depends on their
size (radii), shape (length and curvature), amino acid
composition (physicochemical properties) and dynamics
Functional sites → transport pathways 58
 Bottleneck – the narrowest part of the
tunnel/channel; it has critical importance for
the selectivity
Transport pathways
59Functional sites → transport pathways
Channel
tunnel
Cavity
Pocket/
cleft/groove
Bottleneck
Protein
channel
Enzyme
tunnels
Active site
pocket
Ligand
Transport pathways
Tunnel
60Functional sites → transport pathways
 Dependence on the residues
 residues influencing binding of ligands, transition-state
stabilization or product release
Transport pathways
Tunnel
Active site
pocket
Ligand
Residue on the bottleneck
(Valine)
61Functional sites → transport pathways
 Dependence on the residues
 residues influencing binding of ligands, transition-state
stabilization or product release
Transport pathways
Leucine
Wider tunnel
62Functional sites → transport pathways
 Dependence on the residues
 residues influencing binding of ligands, transition-state
stabilization or product release
Transport pathways
Closed tunnel
Tryptophan
63Functional sites → transport pathways
 Dependence on the residues
 residues influencing binding of ligands, transition-state
stabilization or product release
Transport pathways
64Functional sites → transport pathways
 Dependence on protein dynamics
Time (ns)
Bottleneck
radius(Å)
Prediction of transport pathways
 Identification of overall voids in proteins
 Identification of tunnels
 Identification of channels
65Functional sites → transport pathways
Identification of overall voids
 Methods that aim to accurately represent all types of voids
in a protein structure, including channels, tunnels, surface
clefts, pockets as well as internal cavities
 Usually provide very limited information on tunnel and
channel characteristics – the identified voids have to be
separated from each other
 Geometry-based methods for pocket detection
 HOLLOW – http://hollow.sourceforge.net/
 3V – http://3vee.molmovdb.org/
 fPocket, LIGSITEcsc
, PASS, CASTp, SURFNET, POCASA …
66Functional sites → transport pathways
Identification of tunnels
 Methods that calculate tunnels connecting occluded cavities
with the surrounding bulk solvent
 Identify the pathways from a cavity to the protein surface
 Voronoi diagrams described by the skeleton of voids
between atoms to find all theoretically possible pathways
connecting the starting point with the bulk solvent
 Diagrams of optimal pathways using Dijkstra’s algorithm,
based on criteria defined by a cost function
 The probe size defines the lowest radius threshold
 Tunnel geometry is approximated by a sequence of spheres
67Functional sites → transport pathways
Identification of tunnels
Voronoi diagram Common limitation: the tools identify
two spherical tunnels instead of one
asymmetric tunnel
68Functional sites → transport pathways
Probe size: the minimum radius specified for the
tunnel search
Allowed pathway according to the selected probe
Disallowed pathways
Atom
Tunnel
mouth
Tunnel
origin
Identification of tunnels - programs
 CAVER 3.0
 http://caver.cz/
 Command-line stand-alone and PyMOL plugin
 GUI with CAVER Analyst 2
 For static structures and dynamic ensembles
 CAVER Web
 http://loschmidt.chemi.muni.cz/caverweb/
 Interactive guide-through web server
 Optimized protocol for detection of biologically relevant tunnels
 Based on CAVER 3.0 program
69Functional sites → transport pathways
Identification of tunnels - programs
70Functional sites → transport pathways
Identification of channels
 Methods that calculate channels (or pores) penetrating
throughout the proteins
 Not suitable to identify tunnels leading from occluded
cavities
 Usually analyze just one channel per structure
 Usually need information about approximate position and
direction of the channel (channel axis) – user-provided or
automatically identified
72Functional sites → transport pathways
Identification of channels - programs
 POREWALKER
 http://www.ebi.ac.uk/thornton-srv/software/PoreWalker/
 Identifies channel axis by heuristic iterative approach (based on the
axes of transmembrane secondary structures)
 Protein is divided into equally-spaced slices perpendicular to the
axis; the largest spheres fitting the channel are identified
73Functional sites → transport pathways
Identification of channels - programs
 POREWALKER
74Functional sites → transport pathways
References
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 Xin, F. & Radivojac, P. (2011). Computational methods for identification of functional residues in protein
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