Drug design and discovery

Recent Development In
Drug Design and Discovery
SHIKHA D. POPALI
HARSHPAL SINGH WAHI
DEPARTMENT OF PHARMACEUTICAL
CHEMISTRY
GURUNANAK COLLEGE OF PHARMACY,
NAGPUR
2019-2020
1

Today's Job
• Introduction to Protein Structures
• Methods for Protein structure Determination.
• Known Protein & Unknown Proteins in drug design and
discovery
• Homology Modeling, BLAST,Types of BLAST.
2

INTRODUCTION
• Proteins are an important class of
biological macromolecules which
are the polymers of amino acids.
• Biochemists have distinguished
several levels of structural
organization of proteins. They
are:
• Primary structure
• Secondary structure
• Tertiary structure
• Quaternary structure
3

PRIMARY STRUCTURE
• The primary structure of protein refers to the sequence of amino
acids present in the polypeptide chain.
• Amino acids are covalently linked by peptide bonds.
• Each component amino acid in a polypeptide is called a “residue”
or “moiety”
• By convention, the 10 structure of a protein starts from the amino-
terminal (N) end and ends in the carboxyl-terminal (C) end.
4

IMPORTANCE OF PRIMARY STRUCTURE
• To predict 20 and 30 structures from sequence homologies with
related proteins. (Structure prediction)
• Many genetic diseases result from abnormal amino acid sequences.
• To understand the molecular mechanism of action of proteins.
• To trace evolutionary paths.
• End group analysis – Edman degradation.
• Gene sequencing method.
5
METHODS OF AMINO ACID SEQUENCE
DETERMINATION

SECONDARY STRUCTURE
• Localized arrangement of adjacent amino acids formed as the polypeptide
chain folds.
• It consists of
• Linus Pauling proposed some essential features of peptide units and
polypeptide backbone. They are:
• The amide group is rigid and planar as a result of resonance. So rotation
about C-N bond is not feasible.
• Rotation can take place only about N- Cα and Cα – C bonds.
• Trans configuration is more stable than cis for R grps at Cα
• From these conclusions Pauling postulated 2 ordered structures α helix and
β sheet 6
α-helix
β-pleated sheet
β-bends
Non repetitive structures
Super secondary structures

POLYPEPTIDE
CHAIN CONFORMATIONS
• The only reasonably free movements
are rotations around the C α-N bond
(measured as ϕ ) and the C α-C bond
(measured as Ѱ).
• The conformation of the backbone can
therefore be described by the torsion
angles (also called dihedral angles or
rotation angles)
7

8
Animation showing Phi angle rotation at Psi = 0.

9
Animations showing Psi angle rotation at Phi = 0.

• White regions : Sterically
disallowed for all amino acids
except glycine.
• Red regions : allowed regions
namely the a-helical and b-sheet
conformations.
• Yellow areas : outer limit
10
A Ramachandran plot (also known as a Ramachandran diagram or
a [φ,ψ] plot), originally developed in 1963 by G. N. Ramachandran.
RAMACHANDRAN PLOT

ALPHA HELIX
• Spiral structure
• Tightly packed, coiled polypeptide
backbone core.
• Side chain extend outwards
• Stabilized by H bonding b/w
carbonyl oxygen and amide
hydrogen.
• Amino acids per turn – 3.6
• Pitch is 5.4 A
• Alpha helical segments are found in
many globular proteins like
myoglobins, troponin- C etc.
11
H bonding

BETA PLEATED SHEET
• Formed when 2 or more polypeptides
line up side by side.
• Individual polypeptide - β strand
• Each β strand is fully extended.
• They are stabilized by H bond b/w N-H
and carbonyl groups of adjacent chains.
12
2 types
Parallel Anti -Parallel
N C N
N NC
C
C

EXAMPLES
14
The collagen triple helix.
Silk fibroin beta sheet.

BETA BENDS
• Permits the change of direction of the
peptide chain to get a folded structure.
• It gives a protein globularity rather than
linearity.
• H bond stabilizes the beta bend
structure.
• Proline and Glycine are frequently found
in beta turns.
• Beta turns often promote the formation
of antiparallel beta sheets.
• Occur at protein surfaces.
• Involve four successive aminoacid
residues
15

NON REPETITIVE STRUCTURES
• A significant portion of globular
protein’s structure may be irregular
or unique.
• They include coils and loops.
• Segments of polypeptide chains
whose successive residues do not
have similar ϕ and Ѱ values are
called coils.
• Almost all proteins with more than
60 residues contain one or more
loops of 6 to 16 residues, called Ω
loops. 16
Space-filling model of an Ω loop

SUPER SECONDARY STRUCTURES
(MOTIFS)
17
Beta barrelβ-meander motif
beta-alpha-beta motif Greek key motif
Certain groupings of secondary structural elements are
called motifs.

TERTIARY STRUCTURE
• Tertiary structure is the three-
dimensional conformation of a
polypeptide.
• The common features of protein tertiary
structure reveal much about the
biological functions of the proteins and
their evolutionary origins.
• The function of a protein depends on its
tertiary structure. If this is disrupted, it
loses its activity.
18

DOMAINS
• Polypeptide chains containing more than ,200 residues usually
fold into two or more globular clusters known as domains.
• Fundamental functional and 3 dimensional structure of
proteins.
• Domains often have a specific function such as the binding of a
small molecule.
• Many domains are structurally independent units that have the
characteristics of small globular proteins.
19
The two-domain protein glyceraldehyde-
3-phosphate dehydrogenase.
NAD+

INTERACTIONS STABILIZING 30
STRUCTURE
• This final shape is
determined by a variety of
bonding interactions
between the "side chains"
on the amino acids.
• Hydrogen bonds
• Ionic Bonds
• Disulphide Bridges
• Hydrophobic Interactions:
20

DETERMINATION OF
TERTIARY STRUCTURE
• The known protein structures have come to light through:
• X-ray crystallographic studies
• Nuclear Magnetic Resonance studies
• The atomic coordinates of most of these structures are deposited
in a database known as the Protein Data Bank (PDB).
• It allows the tertiary structures of a variety of proteins to be
analyzed and compared.
22

QUATERNARY STRUCTURE
• The biological function of some
molecules is determined by multiple
polypeptide chains –
multimeric proteins.
• Arrangement of polypeptide sub unit
is called quaternary structure.
• Sub units are held together by non
covalent interactions.
• Eg: Hemoglobin has the subunit
composition a2b2
23
Quaternary structure of hemoglobin.

Conformational parameters for secondary
structure of a protein
• Dihedral angles: in proteins the A.A joint is specific in its
orientation which determines the conformation of the
protein.
• The conformation of the protein could then be elucidated via
the angles in the parent chain and not the side chain of the
protein
• The angle Phi φ is present at the C alpha to Nitrogen of amino
group in the polypeptide
• The angle Psi ψ is present at the C alpha to carbon of
carboxylic group in the polypeptide
• The angles phi and psi should be considered as 180 degrees
when the polypeptide is in fully extended conformation 28

Ramachandran plot
• There are certain permitted values
for these angles.
• As if the values are not appropriate
there might be steric hindrance and
the conformation might get
distorted.
• The protein might also get non
functional
29

• A Ramachandran plot can be used in two different ways.
• One is to show in theory which values, or conformations, of the
ψ and φ angles are possible for an amino-acid residue in a
protein .
• A second is to show the empirical distribution of data points
observed in a single structure in usage for structure validation,
or else in a database of many structures
• Either case is usually shown against outlines for the
theoretically favored regions.
30

Hydropathy plot
• A hydropathy plot is a quantitative analysis of the degree of
hydrophobicity or hydrophilicity of amino acids of a protein.
• It is used to characterize or identify possible structure or
domains of a protein.
• If more hydrophobic residues are present in a plot this means
that the protein is a trans membrane protein and domain
refers to the inner side of the membrane that spans the
membrane multiple times.
32

PROTEIN STRUCTURE DETERMINATION

• Structure Determination
Various functions of biological system depend upon the
structure and function of proteins.
Determination of structure and functions of proteins assist in
scrutinizing the dynamics of proteins.
To understand the functions of proteins at a molecular level, it
is often necessary to determine their three-dimensional
structure.
Introduction
You
r
Log
o

Introduction
Why Structure Determination ?
helps us in Understanding:
• How proteins interact with other molecules ?
• How they perform catalysis in the case of enzymes ?
• Interaction of protein with other molecules including
protein itself.
• Miscoding and/or misfolding of proteins associated with
diseases.
You
r
Log
o

• What is X-Ray Crystallography?
• A form of very high resolution microscopy.
• Enables us to visualize protein structures at the atomic level
• Enhances our understanding of protein function.
• What is the principle behind X-Ray Crystallography?
• It is based on the fact that X-rays are diffracted by crystals.
X-Ray Crystallography

Why X-Rays? Not Others?
300 nm
10 nm
0.1 nm or 1 Å
Wavelength
Individual cells and
sub-cellular
organelles
Cellular architecture
Shapes of large
protein molecules
Atomic detail of
protein
1. Light
1. Electron
1. X-Rays
VisualizationMicroscopy

Why use X-rays and crystals?
Optical microscopy vs. X-ray diffraction
• X-rays is in the order of atom diameter and bond lengths, allowing these to be
individually resolved.
• No lenses available to focus X-rays. Crystal acts as a magnifier of the scattering of
X-rays.
http://classes.soe.ucsc.edu/bme220/Spring07/NOTES/Xraycryst.IMcNae_MWalkinshaw.pdf

• 1. Protein purification.
• 2. Protein crystallization.
• 3. Data collection.
• 4. Structure Solution (Phasing)
• 5. Structure determination (Model building and refinement)
Steps in Structure Determination
http://www2.uah.es/farmamol/New_Science_Press/nsp-protein-5.pdf

• What is Protein Purification?
• is a series of processes intended to isolate one or a few proteins from a
complex mixture, usually cells, tissues or whole organisms.
• Why Protein Purification?
• Characterization of the function.
• Structure
• Interactions of the protein.
• Requirements
• minimum of 5 to 10 milligrams pure soluble
• protein are required with better than 95% purity
Step1:Protein Purification

• Why Crystallization:
• X-ray scattering from a single unit would be unimaginably weak.
• A crystal arranges a huge number of molecules in the same orientation.
• Scattered waves add up in phase and increase Signal to a level which
can be measured.
• This is often the rate-limiting step in straightforward structure
determinations, especially for membrane proteins
Step2:Protein crystallization
http://xray.bmc.uu.se/~kaspars/xray.ppt

Crystals MUST be:
Small in size:
•Less than 1 millimeter
PERFECT:
•No cracks
•No Inclusions, such as air
bubbles
Improving Crystal Quality
Hanging Drop Method
Hanging Drop Method:
1 to 5μl protein solution is suspended over a 1
ml reservoir containing precipitant solution
e.g. ammonium sulfate solution or
polyethylene glycol

Mounting Crystals:
• Crystals are mounted in a way so that the sample can be
rotated and an X‐Ray beam can be passed through the
sample.
• Methods of mounting include using either a capillary or
a tube.
• Both capillary and tubes are mounted on a
goniometer.
Step3:Data collection:
Exposing X‐Rays:
Once the crystals are correctly mounted, they are
exposed to X‐Ray Beams. X‐Ray Sources include:
• Synchrotron: gives high resolution and luminosity
• X‐Ray generators: for smaller, laboratory use
http://serc.carleton.edu/research_education
/geochemsheets/techniques/SXD.html

• The source of the X-rays is often a synchrotron.
• The typical size for a crystal for data collection may be 0.3 x 0.3 x 0.1
mm.
• The crystals are bombarded with X-rays which are scattered from the
planes of the crystal lattice.
• The scattered X-rays are captured as a diffraction pattern on a detector
such as film or an electronic device.
Step3:Data collection:
http://pruffle.mit.edu/atomiccontrol/education/xray/xray_diff_files/image006.gif

• Rotate crystal through 1 degree and Record XRD pattern
• If XRD pattern is very crowded, reduce the degree of rotation
• Repeat until 30 degrees were obtained
• Sometimes 180 degrees depending on crystal symmetry
• Lower the symmetry= More data are required
• For high resolution, use Synchrotron
Step3:Data collection
http://upload.wikimedia.org/wikipedia/commons/d/de/Kappa_goniometer_animation.ogg

Step4:Structure Solution (Phasing)
A typical diffraction pattern from a
protein crystal
GOAL= From Diffraction Data to Electron Density
The 3D structure obtained above is the
electron density map of the crystal.
http://www.chem.ucla.edu/harding/IGOC/E/electron_density_map01http://www.chem.ucla.edu/harding/IGOC/D/diffraction_pattern01.jpg

Phasing
Purification &
Crystalization
Diffraction Phasing

• What is the Phase problem?
• In the measurement of data from an X-ray crystallographic
experiment only the amplitude of the wave is determined.
• To compute a structure, the phase must also be known.
• Since it cannot be determined directly, it must be determined
indirectly or by some other experiment.

• Methods for solving the phase problem
• Molecular Replacement (MR)
• Multiple/Single Isomorphous replacement (MIR/SIR)
• Multiple/Single wavelength Anomalous Diffraction(MAD/SAD)
• Principle using Fourier Transform (FT) :
• FT of the diffraction data gives us a representation of the contents of
the crystal.

Step5: Structure determination (Fitting):
• Fitting of protein sequence in the electron density.
• Electron density – Not self explanatory
• Can be automated, if resolution is close to 2Å or better.
• What can be interpreted is largely defined by resolution.

Step5: Structure determination (Refinement):
Automated improvement of the model, so it explains the observed data
better.
The phases get improved as well, so the electron density maps get better.

Nuclear magnetic resonance (NMR)
Introduction:
• The aim:
Measure set of distances between atomic nuclei.
• Why?
– For proteins that are hard to crystallize.
– For proteins that can be dissolved at high concentrations.
– To study dynamics of the protein: conformational equilibria,
folding and intra-, intermolecular interactions.

The concept
• The base is the nucleus Spin.
• Spin is characterized by angular momentum vector.
• Can be parallel or anti-parallel external magnetic field.
• Forms energy states , low and high
• Applying radio frequency can change the states.
http://www.umkcradres.org/Spec/RADPAGE/Magnet2.jpg

The concept
• Perturbation of the spins causes a NMR signal to be observed.
• The signal consists of RF waves with frequencies that match the energy
difference between the spin states of the individual nuclei involved.
• The resonance frequencies of different types of nuclei are widely
different.
http://en.wikipedia.org/wiki/File:NMR_EPR.gif

The concept
• Chemical shift is the resonant frequency of a nucleus relative to a
standard.
• Nuclear Overhauser effect (NOE) permits distance measurements
between nuclei.
http://www.cs.duke.edu/brd/Teaching/Bio/asmb/current/2papers/Intro-reviews/flemming.pdf

• 1. Protein solution.
• 2. NMR spectroscopy (data collection)
• 3. Sequential resonance assignment
• 4. Collection of conformational constraints
• 5. Structure calculation
Steps in Structure Determination
http://uah.es/farmamol/New_Science_Press/nsp-protein-5.pdf

• Highly purified protein preparation.
• Unlike crystallography, structure determination by NMR is carried out on
aqueous sample.
• Usually, the sample consists of between 300 and 600 microlitres with a
protein concentration in the range 0.1 – 3 millimolar.
• The purified protein is usually dissolved in a buffer solution
Step1: Protein solution

• Each distinct nucleus produces a chemical shift by which it can be recognized
.
• Overlapping chemical shifts , So!
• Two main experiments categories
- One where magnetization is transferred through the chemical bonds.
- One where the transfer is through space.
Step2: NMR spectroscopy (data collection)

• Map chemical shift to atom by
sequential walking .
• Application of multidimensional
NMR spectroscopy allowed the
development of general
strategies for the assignment .
• Take advantage of the known
protein sequence.
Step3: Sequential resonance assignment
http://en.wikipedia.org/wiki/File:1H_NMR_Ethanol_Coupling_shown.GIF

• Can be obtained within one week.
• The assignment of inter-atomic distances based on proton/proton NOEs
observed in is quite time consuming.
• Structure calculation and NOE assignment is an iterative process.
Step3: Sequential resonance assignment

• Geometric conformational information to be derived from the NMR
data.
• Distance restraints.
• Restraints angle .
• Orientation restraints.
• Chemical shift data, provides information on the type of secondary
structure
•Step4: Collection of conformational constraints
You
r
Log
o

• Determined restraints is the input.
• Using computer programs The process
results in an ensemble of structures .
•Step5: Structure calculation
You
r
Log
o
http://en.wikipedia.org/wiki/File:Ensemble_of_NMR_structures.jpg

X-Ray Pros X-Ray Cons NMR Pros NMR Cons
Get whole 3D structure
by analysis of good
crystallized material
Protein has to form
stable crystals that
diffract well
Can provide information
on dynamics and
identify individual side-
chain motion
Requires concentrated
solution - therefore
danger of aggregation
Produces a single
model that is easy to
visualize and interpret
Crystal production can
be difficult and time
consuming
Secondary structure can
be derived from limited
experimental data
Currently limited to
determination of
relatively small proteins
More mathematically
direct image
construction
Inability to examine
solutions and the
behavior of the
molecules in solution
Free from artifacts
resulting from
crystallization
A weaker interpretation
of the experimental
data
Quality indicators
available (resolution, R-
factor)
There is no chance for
direct determination of
secondary structures
Useful for protein-
folding studies
Produces an ensemble
of possible structures
rather than one model
Large molecules can be
determined
Unnatural, non-
physiological
environment
Closer to biological
conditions in some
respects
Advantages & Disadvantages
X-Ray vs. NMR
You
r
Log
o

Cryo-Electron microscopy
Another method for structure determination
You
r
Log
o
• Definition:
– is a new technology for studying the architecture of cells, viruses and
protein assemblies at molecular resolution.
• Biological specimens:
1. Thin film
2. Vitreous sections

Cryo-Electron microscopy
Another method for structure determination
You
r
Log
o
• Advantages :
1. Allows the observation of specimens that have not been stained or
fixed in any way
2. Showing them in their native environment
3. Less in functionally irrelevant conformational changes
• Disadvantages:
1. Expensive
2. The resolution of cryo-electron microscopy maps is not high enough

Step1:Protein Purification(Backup)
You
r
Log
o
A figure summarizing the steps involved in a metal binding strategy for protein purification
http://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Protein_Purification_MetalBinding.tif/lossy-page1-320px-Protein_Purification_MetalBinding.tif.jpg

Braggs law
Step2:Protein crystallization(Backup)
You
r
Log
o
http://www.eserc.stonybrook.edu/ProjectJava/Bragg/
Scattered beams in phase,
they add up
Scattered beams not in
phase, they cancel each other
nl = 2d sinq

Local Global
SB
ML
UPGMA
NJ
SBpima multal
multalign
pileup
clustalx
MLpima
SB - sequential branching UPGMA- Unweighted Pair Grouping Method
ML - maximum likelihood
NJ - neighbor-joining
Progressive multiple alignment
95

Multiple Sequence Alignment
 Introduction: what is a multiple alignment?
 Multiple alignment construction
 Traditional approaches: optimal, progressive
 Alignment parameters
 Iterative and co-operative approaches
 Multiple alignment analysis
 Conserved/homologous regions
 Quality analysis/error detection
 Multiple alignment applications
108

Basic Local Alignment Search
Tool
BLAST
Why Use BLAST?
111

BLAST
• The BLAST algorithm was written balancing speed
and increased sensitivity for distant sequence
relationships.
• Instead of relying on global alignments (commonly
seen in multiple sequence alignment programs)
BLAST emphasizes regions of local alignment to
detect relationships among sequences which share
only isolated regions of similarity.
112

BLAST
• Blast creates a list of all short sequences (words) that
have a certain “threshold” score when compared with the
query sequence.
• These are 16-256 nucleotides or 3 amino acids in a row.
• Then the database is searched for occurrences of these
words.
• Find this in BLAST algorithm Parameters
113

FastA Format
• A sequence in FASTA format begins with a single-line
description, followed by lines of sequence data.
• The description line is distinguished from the sequence
data by a greater-than (">") symbol in the first column.
• It is recommended that all lines of text be shorter than 80
characters in length.
114

DifferentTypes of BLAST Programs
115
blastn nucleotide nucleotide
Program Query Database
blastp protein/peptide protein/peptide
blastx nucleotide protein/peptide
tblastn protein/peptide nucleotide
tblastx nucleotide nucleotide
http://www.ncbi.nlm.nih.gov/blast

Criteria for considering two
sequences to be homologous
• Proteins are homologous if
• Their amino acid sequences are at least 25% identical
• DNA sequences are homologous if
• they are at least 70% identical
• Note that sequences must be over 100 a.a. (or bp) in length
116

Whenever possible, it is better
to compare proteins
than to compare genes
117

BLAST compares sequences
• BLAST takes a query sequence
• Compares it with millions of sequences in the
Genbank databases
• By constructing local alignments
• Lists those that appear to be similar to the query
sequence
• The “hit list”
• Tells you why it thinks they are homologs
• BLAST makes suggestions
• YOU make the conclusions
119

How do I input a query into
BLAST?
120

Choose which “flavor” of BLAST to
use
• BLAST comes in many “flavors”
• Protein BLAST (BLASTp)
• Compares a protein query with sequences in GenBank protein
database
• Nucleotide BLAST (BLASTn)
• Compare nucleotide query with sequences in GenBank
nucleotide database
121

How do I interpret the results of a BLAST
search?
126

BLAST creates local alignments
• What is a local alignment?
• BLAST looks for similarities between regions of two
sequences
127

The BLAST output then describes
how these aligned regions are
similar
• How long are the aligned segments?
• Did BLAST have to introduce gaps in order to align the segments?
• How similar are the aligned segments?
128

The Graphic Display
1. How good is the match?
• Red = excellent!
• Pink = pretty good
• Green = OK, but look at other factors
• Blue = bad
• Black = really bad!
2. How long are the matched segments?
Longer = better
130

The hit list
• BLAST lists the best matches (hits)
• For each hit, BLAST provides:
• Accession number – links to Genbank flatfile
• Description
• “G” = genome link
• E-value
• An indicator of how good a match to the query sequence
• Score
• Link to an alignment
131

What is an E-value?
• E-value
• The chance that the match could be random
• The lower the E-value, the more significant the match
• E = 10-4 is considered the cutoff point
• E = 0 means that the two sequences are statistically identical
132

E-values
• E-values (Expect values) provide information about the
likelihood that a given sequence alignment is significant.
• The smaller the E-value, the less likely the alignment was
by chance.
• At some point, you are just generating random junky
data- unless you have other information like a structural
comparison.
133

Most people use the E- value
as their first indication of
similarity!
134

The Alignment
• Look for:
• Long regions of alignment
• With few gaps
• % identity should be >25% for proteins
• (>70% for DNA)
135

BLAST makes suggestions,
You draw the conclusions!
• Look at E-value
• Look at graphic display
• If necessary, look at alignment
• Make your best guess!
136

Evaluating Blast Results
• A Blast search can produce dozens or hundreds of
candidate alignments.
• Out of these alignments, which are really specific?
• Raw Scores, Bit Scores and E-values are used as statistics.
137

FASTA Steps
142
1
Local regions of
identity are found
Different offset values
Identical offset
values in a
contiguous sequence
2
Rescore the local regions
using PAM or Blos. matrix
Diagonals are extended
3
Eliminate short diagonals
below a cutoff score
4
Create a gapped alignment in
a narrow segment and then
perform S-W alignment

ktup analysis____________________
1 proteins- distantly related
2 proteins- somewhat related (default)
3 DNA-default
146

Evaluating the Results of FASTA
151
Best
SCORES Init1: 2847 Initn: 2847 Opt: 2847
z-score: 2609.2 E(): 1.4e-138
Smith-Waterman score: 2847; 100.0% identity in 413
overlap
Good
z-score: 734.0 E(): 3.8e-34
overlap
Mediocre
z-score: 243.2 E(): 8.3e-07
overlap

When to use the correct program
Problem Program Explanation
Identify
Unknown
Protein
BLASTP;
FASTA3
General protein
comparison. Use ktup=2
for speed; ktup=1 for
sensitive search.
Smith-Waterman Slower than FASTA3 and
BLAST but provides
maximum sensitivity
TFASTX3;TFASTY3;
TBLASTN
Use if homolog cannot
be found in protein
databases; Approx. 33%
slower
Psi-BLAST Finds distantly related
sequences. It replaces
the query sequence with
a position-specific score
matrix after an initial
BLASTP search. Then it
uses this matrix to find
distantly related
sequences
153

Contents
• Introduce the process of homology modelling.
• Summarise the methods for predicting the structure from sequence.
• Describe the individual steps involved in creating and optimising a protein
homology model.
• Outline the methods available to evaluate the quality of homology models.
• Case Study – Modelling the Drug binding site of hERG.
158

Why Homology Model?
• Solving protein structures is not trivial.
• There are currently ~1.8 million known
protein coding sequences.
• But only ~44,000 protein structures in the
PDB.
• Even so, many of these structures are
duplicates.
• For Membrane Proteins structural data is
even more sparse:
• There are currently 304 membrane protein
structures, of which only 142 are unique.
RSCB Protein Data Bank (PDB)
Statistics (30/11/07)
Method Totals
X-ray 37557
NMR 5984
EM 109
Other 83
Total 43733
159www.rscb.org

Amino Acid Residues
• Proteins are made up of amino
acids, which are interconnected by
peptide bonds.
• There are 20 naturally occurring
amino acids.
• Amino acids may be subdivided by
their individual properties.

DSSRRQYQEKYKQVEQYMSFHKLPADFRQKIHDYYEHRYQGKMFDEDSILGELNGPLREEIVNFNCR
KLVASMPLFANADPNFVTAMLTKLKFEVFQPGDYIIREGTIGKKMYFIQHGVVSVLTGNKEMKLSDG
SYFGEICLLTRGRRTASVRADTYCRLYSLSVDNFNEVLEEYPMMRRAFETYVAIDRLDRIGKKNSIL
From Sequence to Structure
Secondary
Structure
Tertiary
Structure
Quaternary
Structure
Primary Structure – Amino Acid Sequence
What information can we get from a Sequence of amino acids?

Secondary Structure Prediction
• The Secondary Structure of Proteins is Defined by the DSSP algorythm.
• Amino acids classified as either α-helix (H), β-strand (S) or loop (C).
• It is possible to extract structural information from amino acid sequence.
• These prediction methods were initially proposed by Chou & Fasman in 1978.
• They used a statistical method based on 15 known crystal structures.
• Recent developments and an increase in structural information has improved these methods and they are currently ~80% accurate.
PSI-Pred: http://bioinf.cs.ucl.ac.uk/psipred/psiform.html
JPred: http://www.compbio.dundee.ac.uk/~www-jpred/
162

Transmembrane Helix Prediction
• The amino acids at the centre of
transmembrane helices are generally
hydrophobic in nature.
• Analysis of Hydropathicity can be
used to predict the number of
membrane spanning helices.
• The analysis for the G-protein
coupled receptor to the right
suggests it has 7TM helices.
• The example used the Kyte &
Doolittle scale.
Hydropathy Plot
http://expasy.org/tools/protscale.html

BLAST
• How to find an appropriate template Structure for
homology modelling…
• Basic Local Alignment Search Tool
• Used to search protein databases:
• e.g. Non-redundant (nr) & SwissProt to find
similar sequences.
• Protein Data Bank (PDB) to find structures with
similar sequences.
• PSI- & PHI-blast are more advanced Blast
methods.
http://www.ncbi.nlm.nih.gov/blast/Blast.cgi

The Importance of Resolution
• In X-ray crystallography it is not
always possible to flawlessly resolve
the crystal density of the protein of
interest.
• This results in a lower resolution
structure.
• The lower the resolution the more
likely the structure is wrong.
• The resolution of the template
structure also reflects in the quality
of the homology model.
high
low
4 Å
2 Å
3 Å
1 Å

Sequence Alignment
• Aligns the sequence(s) of interest to that of the template structure(s).
• Emboss may be used for two sequence, to generate a pairwise alignment & a percentage identity – ideally an identity
of >50%:
http://www.ebi.ac.uk/emboss/align/
• T-Coffee, Clustal & MUSCLE are popular methods for multiple sequence alignment.All may be found at :
http://www.ebi.ac.uk/
• ESPRIPT is useful for formatting to creating black & white figures:
http://espript.ibcp.fr/

Automated Homology Modelling
If you are lazy there are servers that do the modelling for you!
• Swiss Model : http://swissmodel.expasy.org//SWISS-MODEL.html
• Robetta : http://robetta.bakerlab.org/
• 3D Jigsaw: http://www.bmm.icnet.uk/servers/3djigsaw/
• Phyre: http://www.sbg.bio.ic.ac.uk/phyre/
• EsyPred3D: http://www.fundp.ac.be/sciences/biologie/urbm/bioinfo/esypred/
• CPHmodels: http://www.cbs.dtu.dk/services/CPHmodels/
167

Modeller
from modeller import *
from modeller.automodel import *
log.verbose()
env = environ()
env.io.atom_files_directory = './'
a = automodel(
env,
alnfile = 'herg.ali',
knowns = '1q5o',
sequence = 'herg'
)
a.starting_model= 1
a.ending_model = 1
a.make()
>P1;1q5o
structureX: 1q5o : 443 : A : 644 : A ::::
DSSRRQYQEKYKQVEQYMSFHKLPADFRQKIHDYYEHRYQ-GKMFDEDSILGELNGPLRE
EIVNFNCRKLVASMPLFANADPNFVTAMLTKLKFEVFQPGDYIIREGTIGKKMYFIQHGV
VSVLTKGNKEMKLSDGSYFGEICLL--TRGRRTASVRADTYCRLYSLSVDNFNEVLEEYP
MMRRAFETVAIDRLDRIGKKNSIL.*
>P1;herg
sequence: herg : 1 :::::::
YSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQA
DICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGS
IEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYP
EFSDHFWSSLEITFNLRDTN-MIP.*
• Well regarded program for Homology/Comparative Modelling.
• CurrentVersion 9v2. http://www.salilab.org/modeller/
• Requires an Input file, Sequence alignment &Template structure.
ATOM 1 N ASP A 443 -15.943 41.425 44.702 1.00 44.68
ATOM 2 CA ASP A 443 -15.424 42.618 45.447 1.00 43.15
ATOM 3 C ASP A 443 -14.310 43.306 44.686 1.00 41.81
ATOM 4 O ASP A 443 -14.298 44.528 44.539 1.00 42.61
etc...
Input File (*.py) Template Structure (*.pdb)
Sequence Alignment (*.ali)

How Does itWork?
169
Energy
Minimisation
Amino acid
Substitution
Template Structure Initial Model (*.ini) Output Model(s) (*.B999*)
Valine Glutamine Change in
Rotamer

Structural Convergence
• The catalytic triad of Serine,Aspartate and Histidine is found in certain protease
enzymes. (a) Subtilisin (b) Chymotrypsin.
• However, the overall structure of the enzyme is often different.
• This is also important when considering ligand binding sites.

Modelling Ligand Interactions
• Small molecules, waters and ions can be
retained from the template structure.
• It is possible to search for homologues
based on the ligands they bind.
• Experimental data, especially
mutagenesis is very useful when
modelling ligand binding sites.
• Although the key residues may often
remain, the overall structure of the
protein may vary radically.
• The presence of the ligand is also likely
to alter the conformation of the protein.
1ATN
1E4G
ATP Binding Site

Conformational States
• The backbone structure of the
model will be almost identical to
that of the template.
• Therefore the conformational state
of the template will be retained in
the resultant homology model.
• This is important when considering
the open or closed conformation of
a channel…
• … or the Apo versus bound state of a
ligand binding site.
Closed
Open

Loop Modelling
Issues with Loop Modelling
• As loops are less restrained by hydrogen bonding networks they often have
increased flexibility and therefore are less well defined.
• In addition the increased mobility make looped regions more difficult to
structurally resolve.
• Proteins are often poorly conserved in loop regions.
• There are usually residue insertions or deletions within loops.
• Proline and Glycine resides are often found in loops – we’ll come back to this
when discussing Model evaluation protocols.
176

Loop Modelling
• There are two main methods for modelling loops:
1. Knowledge based:
A PDB search for fragments that match the sequence to be modelled.
2. Ab initio:
A first principles approach to predict the fold of the loop, followed by
minimisation steps.
• Many of the newer loop prediction methods use a combination of the two methods.
• These approaches are being developed into methods for computationally predicting the tertiary structure of
proteins. eg Rosetta.
• But this is computationally expensive.
• Modeller creates an energy function to evaluate the loop’s quality.
• The function is then minimised by Monte Carlo (sampling), Conjugate Gradients (CG) or molecular dynamics
(MD) techniques.
177

Model Evaluation
Initial Options
1. For every model, Modeller creates an objective function energy term, which is reported in the
second line of the model PDB file (.B*).
• This is not an absolute measure but can be used to rank models calculated from the same
alignment.The lower the value the better.
2. A Cα-RMSD (Root Mean Standard Deviation) between the template structure and models can
also be used to compare the final model to its template.
• A good Cα-RMSD will be less than 2Å.
179

Model Evaluation
MoreAdvanced Options
• Procheck, PROVE,WhatIf:
Stereochemical checks on bond
lengths, angles and atomic contacts.
• Ramachandran Plot is a major
component of the evaluation.
• Ensures that the backbone
conformation of the model is normal.
• Modeller is good on the whole, but
sometimes struggles with residues
found in loops.
• RAMPAGE:
α-helix
β-strand
Psi
Dihedral
Angle
Phi Dihedral Angle
left-handed
helix
http://mordred.bioc.cam.ac.uk/~rapper/rampage.php

Ramachandran Plot
• The results of the ramachandran plot will be very
similar to that of the template.
• A Good template is therefore key!
• Most residues are mainly found on the left-hand
side of the plot.
• Glycine is found more randomly within plot
(orange), due to its small sidechain (H)
preventing clashes with its backbone.
• Proline can only adopt a Phi angle of ~-60°
(green) due to its sidechain.
• This also restricts the conformational space of
the pre-proline residue.
N
Peptide
dihedral
angles

+----------<<< P R O C H E C K S U M M A R Y >>>----------+
| |
| mgirk .pdb 2.5 104 residues |
| |
*| Ramachandran plot: 91.7% core 7.6% allow 0.3% gener 0.4% disall |
| |
*| All Ramachandrans: 15 labelled residues Backbone |
*| Chi1-chi2 plots: 6 labelled residues Sidechain |
| Main-chain params: 6 better 0 inside 0 worse |
| Side-chain params: 5 better 0 inside 0 worse |
| |
*| Residue properties: Max.deviation: 16.1 Bad contacts: 10 |
*| Bond len/angle: 8.0 Morris et al class: 1 1 3 |
| |
| G-factors Dihedrals: 0.10 Covalent: 0.29 Overall: 0.16 |
| |
| M/c bond lengths: 99.1% within limits 0.9% highlighted |
*| M/c bond angles: 98.1% within limits 1.9% highlighted |
| Planar groups: 100.0% within limits 0.0% highlighted |
| |
+----------------------------------------------------------------------------+
+ May be worth investigating further. * Worth investigating further.
PROCHECK
182
Biotech Validation Suite: http://biotech.embl-ebi.ac.uk:8400/
Procheck: www.biochem.ucl.ac.uk/~roman/procheck/procheck.html

CASP
• Critical Assessment of Structure Prediction.
• A Biennial competition that has run since 1994.
• The next competition will be in 2008 (CASP8)
• http://predictioncenter.org/
• Its goal is to advance the methods for predicting protein structure from sequence.
• Protein structures yet to be published are used as blind targets for the prediction methods, with only sequence information released.
• Competitors may use Homology Modelling, Fold recognition or Ab Initio structural prediction methods to propose the structure of the
protein.
183

Pymol
• A powerful visualisation
and picture generation tool
for protein and DNA.
• Two windows
• Graphical User Interface (GUI)
• PymolViewer
• BothText and Mouse driven.
• Website:
http://pymol.sourceforge.net/
• More Info &Tutorials:
http://www.pymolwiki.org/
A-Action
S-Show
H-Hide
L-Label
C-Colour
Sequence Viewer

Pymol
Primary Uses
• Visualisation of Macromolecular Structures.
• High quality image generation capabilities (~1/4 of published images).
• Structural alignment of two structures in three dimensional space.
• Single amino acid mutagenesis.
• Investigating Protein-Ligand interactions.
• Assessing multiple-frame simulation data – not as robust as VMD.
185

Homology Modelling
Case Study:
Drug Binding Site
of the hERG
Potassium Channel
186

S1
_
_
_
S2
_
S3b
_
+
+
+
S4
+
+
+
S6S5
Turret
Helix
N-Terminal
Domain
C-Terminal
Domain
Pore
Helix
Voltage Sensor Domain
Pore Domain
Selectivity
Filter
Intracellular
Extracellular
_
S3a
hERG Subunit Topology
187

Templates for Homology Modelling
KcsA KirBac1.1 MthK KvAP
Filter
188

Amino Acids involved in Drug Binding
189
S5
S6
P
Selectivity
Filter
F656
G648
Y652
V659
S624
T623
V625
Drug
Access

Closed and Open State hERG
190
KcsA Based KvAP Based

Ligand Docking to hERG
191
KcsA Based - Closed KvAP Based - Open

Combining IndividualTemplate
Structures into a Complete Model
192
1EYW
1Q5O
1ORS
1ORQ

Predicting Conformational Changes
193
Side Below
Morph Server: http://www.molmovdb.org/cgi-bin/submit.cgi

Predicting Protein Structure:
Threading / Fold Recognition
Basis
It is estimated there are only around 1000 to
10 000 stable folds in nature
*
Fold recognition is essentially finding the best
fit of a sequence to a set of candidate folds
*
Select the best sequence-fold alignment using a
fitness scoring function
*
196

TheThreading Problem
• Find the best way to “mount” the residue sequence of
one protein on a known structure taken from another
protein
197

Why is it called threading ?
• threading a specific sequence through all known folds
• for each fold estimate the probability that the sequence
can have that fold
198

Threading: Basic Strategy
Sequence
Template
Spatial
Interactions
dhgakdflsdfjaslfkjsdlfjsdfjasd
Library of
folds
Query
Scoring & selection
199

ProteinThreading
• Conserved Core Segments
200
Protein B
J
L
K
I
Protein A Conserved
Core
Segments

Two structurally
similar proteins
Spatial adjacencies
(interactions)
Possible threading
with a sequence
201

Input/Output of ProteinThreading
202
Pairwise
amino acid
scoring
function
Amino acid
sequence a[1..n]
g(…)
Core segments
C[1..m]
T
H
R
E
A
D
I
N
G

Fold recognition (Threading)
The sequence:
+
Known protein folds
SLVAYGAAM
structural model
203

Input:
sequence
H bond donor
H bond acceptor
Glycin
Hydrophobic
Library of folds of known proteins
204

S=20S=5S=-2
Z=5Z=1.5Z= -1
H bond donor
H bond acceptor
Glycin
Hydrophobic
205

Fold recognition/Threading
Disadvantages:
• threading methods seldom lead to the alignment
quality that is needed for homology modeling.
• less than 30% of the predicted first hits are true
remote homologues (PredictProtein).
207

Threading resources
• TOPITS
HeuristicThreader, part of larger structure prediction
system
• 3DPSSM
Integrated system, does its own MSA and secondary
structure predictions and then threading
• GenThreader
Similar to 3DPSSM
208

In homology modelling, construction of the side chains is done using the
template structures when there is high similarity between the built
protein and the templates
In spite of the huge size of the problem (because each side chain
influences its neighbours) there are quite succesful algorithms to this
problem.
Side chain construction
Without such similarity the construction can be done using rotamer libraries
A compromise between the probability of the rotamer and its fitness in
specific position determines the score. Comparing the scores of all the
rotamer for a given amino acid determines the preferred rotamer.
209

In this work we examined differences in structures of
aminoacid side chains around point mutations.
Phe
Asn
Conformation - a given set
of dihedral angle which
defines a structure.
Rotamer - energetically
favourable conformation.
210

Ab initio
The sequence
SLVAYGAAM
structural model
212

Ab initio methods for modelling
This field is of great theoretical interest but, so far, of very little
practical applications. Here there is no use of sequence
alignments and no direct use of known structures
The basic idea is to build empirical function that simulates real
physical forces and potentials of chemical contacts
If we will have perfect function and we will be able to scan all the
possible conformations, then we will be able to detect the correct
fold
213

Predicting Protein Structure:
Ab Initio Methods
Sequence
Secondary
structure
Prediction
Tertiary
structur
e
Low energy
structures
Predicted
structureEnergy
Minimization
Validation
Mean field
potentials
214

Name Method Description Link
3D-JIGSAW Fragment assembly Automated webserver server
RaptorX
remote homology
detection, protein 3D
modelling, binding site
prediction
Automated webserver
and Downloadable
program
server and download
Biskit
wraps external programs
into automated workflow
BLAST search,T-
Coffee alignment,
and MODELLER constructio
n
project site
CABS Reduced modeling tool Downloadable program download
CPHModel Fragment assembly Automated webserver server
EasyModeller GUI to MODELLER
Standalone windows
executable
download
ESyPred3D
Template detection,
alignment, 3D modeling
Automated webserver server
215

FoldX
Energy calculations and
protein design
Downloadable program download
GeneSilico
Consensus template
search/fragment
assembly
Webserver server
Geno3D
Satisfaction of spatial
restraints
Automated webserver server
HHpred
Template detection,
alignment, 3D modeling
Interactive webserver
with help facility
serverdownload article
LIBRA I
LIght Balance for Remote
Analogous proteins, ver. I
Webserver server
LOMETS
Local Meta threading
server
Meta-server combining 9
different programs
Server
download
MODELLER
Satisfaction of spatial
restraints
Standalone program
mainly
in Fortran and Python
download Server
216

Phyre and Phyre2
Remote template detection,
alignment, 3D modeling, multi-
templates, ab initio
Webserver with job
manager, automatically
updated fold library,
genome searching and
other facilities
server
Prime Physics-based energy function
, homology modeling,
protein refinement, loop-
prediction, and side-chain
prediction
Download
Protinfo CM
Comparative modelling of protein
structure using minimum
perturbation and loop building
Web server server
ROBETTA
Rosetta homology modeling and
ab initio fragment assembly with
Ginzu domain prediction
Webserver server
BHAGEERATH-H
Combination of ab initio folding
and homology methods
Protein tertiary structure
predictions
server
Selvita Protein
Modeling Platform
Package of tools for protein
modeling
Free demo, interactive
webserver and standalone
program
Home page
STRUCTUROPEDIA WebInterface to MODELLER Homology server
217

SWISS-MODEL
Local similarity/fragment
assembly
Automated webserver
(based on ProModII)
server
TIP-STRUCTFAST Automated Comparative Modeling
Webserver
and knowledgebase[dead
link]
server
WHAT IF Position specific rotamers
Standalone program and
webinterface
Home page[dead
link]Webinterface
Yasara
Detection of templates, alignment,
modeling incl. ligands and
oligomers, hybridization of model
fragments
Graphical interface or text
mode (clusters)
Home pageCASP8 results
218

Drug design and discovery

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Drug design and discovery

Similar to Drug design and discovery (20)

More from Shikha Popali

More from Shikha Popali (20)

Recently uploaded

Recently uploaded (20)

Drug design and discovery