structural bioinformatics ii - github pagesmed chem, crystallography, modeling drug candidates small...
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BIMM 143Structural Bioinformatics II
Lecture 12
Barry Grant
http://thegrantlab.org/bimm143
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‣ Overview of structural bioinformatics• Major motivations, goals and challenges
‣ Fundamentals of protein structure• Composition, form, forces and dynamics
‣ Representing and interpreting protein structure
• Modeling energy as a function of structure
‣ Example application areas• drug discovery & Predicting functional dynamics
NEXT UP:
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THE TRADITIONAL EMPIRICAL PATH TO DRUG DISCOVERY
Compound library (commercial, in-house,
synthetic, natural)
High throughput screening(HTS)
Hit confirmation
Lead compounds(e.g., µM Kd)
Lead optimization(Medicinal chemistry)
Potent drug candidates (nM Kd)
Animal and clinical evaluation
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COMPUTER-AIDED LIGAND DESIGN
Aims to reduce number of compounds synthesized and assayed
Lower costs
Reduce chemical waste
Facilitate faster progress
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Two main approaches:(1). Receptor/Target-Based(2). Ligand/Drug-Based
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Two main approaches:(1). Receptor/Target-Based(2). Ligand/Drug-Based
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SCENARIO 1:RECEPTOR-BASED DRUG DISCOVERY
HIV Protease/KNI-272 complex
Structure of Targeted Protein Known: Structure-Based Drug Discovery
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PROTEIN-LIGAND DOCKING
VDW
Dihedral
Screened Coulombic+ -
Potential function Energy as function of structure
Docking softwareSearch for structure of lowest energy
Structure-Based Ligand Design
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STRUCTURE-BASED VIRTUAL SCREENING
Candidate ligands
Experimental assay
Compound database
3D structure of target(crystallography, NMR,
bioinformatics modeling)
Virtual screening(e.g., computational
docking)
Ligands
Ligand optimization Med chem,
crystallography, modeling
Drug candidates
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COMPOUND LIBRARIES
Commercial (in-house pharma) Government (NIH) Academia
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FRAGMENTAL STRUCTURE-BASED SCREENING
“Fragment” library 3D structure of target
Fragment docking
Compound design
http://www.beilstein-institut.de/bozen2002/proceedings/Jhoti/jhoti.html
Experimental assay and ligand optimization Med chem, crystallography, modeling Drug candidates
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Small organic probe fragment affinities map multiple potential binding sites across the structural ensemble.
Multiple non active-site pockets identified
**
GDPGTP
Residue No.
Prob
e O
ccup
ancy
ethanol
isopropanol
acetone
cyclohexane phenol
methylamine benzene
acetamide
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Ensemble docking & candidate inhibitor testing
1321
N1U1
38U2
51U3
73U3
43Ras-GTP
Total Ras
Compound effect on U251 cell lineRas activity in different cell lines
3) NCI ligands that target the C1 pocket of K-ras
13616
23895
36818
99660
117028
121182 99660
117028
121182
Top hits from ensemble docking against distal pockets were tested for inhibitory effects on basal ERK activity in glioblastoma cell lines.
Ensemble computational docking
PLoS One (2011, 2012)
DMSO
6627
96
3681
8
6430
0011
7028
P-ERK1/2
Total ERK1/2
10 µM
Compound testing in cancer cell lines
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Proteins and Ligand are Flexible
+
Ligand
Protein
Complex
ΔGo
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COMMON SIMPLIFICATIONS USED IN PHYSICS-BASED DOCKING
Quantum effects approximated classically
Protein often held rigid
Configurational entropy neglected
Influence of water treated crudely
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Two main approaches:(1). Receptor/Target-Based(2). Ligand/Drug-Based
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Do it Yourself!
Hand-on time! https://bioboot.github.io/bimm143_S18/lectures/#12
You can use the classroom computers or your own laptops. If you are using your laptops then you will need
to install VMD and MGLTools
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• If you want the 3D viewer in your R markdown you can install the development version of Bio3D
• For MAC:
• For Windows:
> download.file("https://tinyurl.com/bio3d-mac", "bio3d.tar.gz")> install.packages("bio3d.tar.gz", repos = NULL)
> install.packages("https://bioboot.github.io/bggn213_S18/class-material/bio3d_2.3-4.9000.zip", repos = NULL)
Bio3D view()
[ See: Appendix I in Lab Sheet ]
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Proteins and Ligand are Flexible
+
Ligand
Protein
Complex
ΔGo
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HTTP://129.177.232.111:3848/PCA-APP/
HTTP://BIO3D.UCSD.EDU/PCA-APP/
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Two main approaches:(1). Receptor/Target-Based(2). Ligand/Drug-Based
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e.g. MAP Kinase Inhibitors
Using knowledge of existing inhibitors to discover more
Scenario 2Structure of Targeted Protein Unknown:
Ligand-Based Drug Discovery
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Why Look for Another Ligand if You Already Have Some?
Experimental screening generated some ligands, but they don’t bind tightly enough
A company wants to work around another company’s chemical patents
An high-affinity ligand is toxic, is not well-absorbed, difficult to synthesize etc.
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LIGAND-BASED VIRTUAL SCREENING
Compound Library Known Ligands
Molecular similarityMachine-learning
Etc.
Candidate ligands
Assay
Actives
Optimization Med chem, crystallography,
modeling
Potent drug candidates
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CHEMICAL SIMILARITY LIGAND-BASED DRUG-DISCOVERY
Compounds(available/synthesizable)
Compare with known ligandsDifferent
Test experimentally
Similar
Don’t bother
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CHEMICAL FINGERPRINTSBINARY STRUCTURE KEYS
Molecule 1
Molecule 2
phen
yl
methyl
keton
eca
rboxyl
ate
amide
aldeh
yde
chlor
ine
fluori
ne
ethyl
naph
thyl
S-S bond
alcoh
ol …
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Molecule 1
Molecule 2
phen
yl
methyl
keton
eca
rboxyl
ate
amide
aldeh
yde
chlor
ine
fluori
ne
ethyl
naph
thyl
S-S bond
alcoh
ol …
CHEMICAL SIMILARITY FROM FINGERPRINTS
NI=2Intersection
NU=8Union
Tanimoto Similarity (or Jaccard Index), T
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+ 1
Bulky hydrophobe
Aromatic
5.0 ±0.3 Å 3.2 ±0.4 Å
2.8 ±0.3 Å
Pharmacophore ModelsΦάρμακο (drug) + Φορά (carry)
A 3-point pharmacophore
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Molecular DescriptorsMore abstract than chemical fingerprints
Physical descriptorsmolecular weightchargedipole momentnumber of H-bond donors/acceptorsnumber of rotatable bondshydrophobicity (log P and clogP)
Topologicalbranching indexmeasures of linearity vs interconnectedness
Etc. etc.
Rotatable bonds
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A High-Dimensional “Chemical Space”Each compound is at a point in an n-dimensional space
Compounds with similar properties are near each other
Descriptor 1
Descriptor 2
Des
crip
tor 3
Point representing a compound in descriptor space
Apply multivariate statistics and machine learning for descriptor-selection. (e.g. partial least squares, PCA, support vector machines,
random forest, deep learning etc.)
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Approved drugs and clinical candidates • Catalogue approved drugs and clinical candidates from
FDA Orange Book, and USAN applications
• Small molecules and biotherapeutics
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Drug properties
enzyme&
pep(de/&protein&
oligosaccharide&
oligonucleo(de&
synthe(c&small&molecule&&
natural&product6&derived&
inorganic&
monoclonal&an(body&
polymer&
Rule&of&Five&
Drug&Type&
First&in&Class&
Chira
lity&
Prod
rug&
Oral&
Parenteral&
Topical&
Black6Bo
x&Warning&
Availability&Type
&
prescrip(on&only&
over6&the6counter&
discon(nued&
Ingredient6related&&
(USANs,&candidates&&and&approved&drugs)&
Product6related&&
(approved&drugs&only)&
racemic&mixture&
chirally&pure&
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LIPINSKI’S RULE OF FIVE
Lipinski’s rule of five states that, in general, an orally active drug has no more than one violation of the following criteria:
• Not more than 5 hydrogen bond donors (nitrogen or oxygen atoms with one or more hydrogen atoms)
• Not more than 10 hydrogen bond acceptors (nitrogen or oxygen atoms)
• A molecular mass less than 500 daltons
• An octanol-water partition coefficient log P not greater than 5
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Rules for drug discovery success
• Set of approved drugs or medicinal chemistry compounds and their targets can be used to derive rules for drug discovery success (or failure):
• What features make a successful drug target?
• What features make a protein druggable by small molecules?
• What features of a compound contribute to good oral bioavailability?
• What chemical groups may be associated with toxicity?
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Details of sites identified
View cavities (and ligands) on structure
Druggability prediction
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‣ Overview of structural bioinformatics• Major motivations, goals and challenges
‣ Fundamentals of protein structure• Composition, form, forces and dynamics
‣ Representing and interpreting protein structure
• Modeling energy as a function of structure
‣ Example application areas• Drug discovery & predicting functional dynamics
NEXT UP:
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PREDICTING FUNCTIONAL DYNAMICS
• Proteins are intrinsically flexible molecules with internal motions that are often intimately coupled to their biochemical function
– E.g. ligand and substrate binding, conformational activation, allosteric regulation, etc.
• Thus knowledge of dynamics can provide a deeper understanding of the mapping of structure to function
– Molecular dynamics (MD) and normal mode analysis (NMA) are two major methods for predicting and characterizing molecular motions and their properties
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McCammon, Gelin & Karplus, Nature (1977) [ See: https://www.youtube.com/watch?v=ui1ZysMFcKk ]
• Use force-field to find Potential energy between all atom pairs
• Move atoms to next state
• Repeat to generate trajectory
MOLECULAR DYNAMICS SIMULATION
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KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION
OF ITS STRUCTURE
Two main approaches:(1). Physics-Based(2). Knowledge-Based
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KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION
OF ITS STRUCTURE
Two main approaches:(1). Physics-Based(2). Knowledge-Based
Ener
gy
Structure/Conformation
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Two main approaches:(1). Physics-Based(2). Knowledge-Based
KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION
OF ITS STRUCTURE
Ener
gy
Structure/Conformation
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PHYSICS-BASED POTENTIALS ENERGY TERMS FROM PHYSICAL THEORYThe Potential Energy Function
Ubond = oscillations about the equilibrium bond length
Uangle = oscillations of 3 atoms about an equilibrium bond angle
Udihedral = torsional rotation of 4 atoms about a central bond
Unonbond = non-bonded energy terms (electrostatics and Lenard-Jones)
CHARMM P.E. function, see: http://www.charmm.org/
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img044.jpg (400x300x24b jpeg)
Slide Credit: Michael Levitt
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2223234
img054.jpg (400x300x24b jpeg)
Slide Credit: Michael Levitt
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PHYSICS-ORIENTED APPROACHESWeaknesses
Fully physical detail becomes computationally intractableApproximations are unavoidable
(Quantum effects approximated classically, water may be treated crudely)Parameterization still required
StrengthsInterpretable, provides guides to designBroadly applicable, in principle at leastClear pathways to improving accuracy
StatusUseful, widely adopted but far from perfectMultiple groups working on fewer, better approxs
Force fields, quantumentropy, water effects
Moore’s law: hardware improving
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–Johnny Appleseed
Put Levit’s Slide here on Computer Power Increases!
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SIDE-NOTE: GPUS AND ANTON SUPERCOMPUTER
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SIDE-NOTE: GPUS AND ANTON SUPERCOMPUTER
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Two main approaches:(1). Physics-Based(2). Knowledge-Based
KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION
OF ITS STRUCTURE
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KNOWLEDGE-BASED DOCKING POTENTIALS
Histidine
Ligand carboxylate
Aromaticstacking
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Example: ligand carboxylate O to protein histidine NFind all protein-ligand structures in the PDB with a ligand carboxylate O
1. For each structure, histogram the distances from O to every histidine N2. Sum the histograms over all structures to obtain p(rO-N)3. Compute E(rO-N) from p(rO-N)
ENERGY DETERMINES PROBABILITY (STABILITY)
Boltzmann distribution
Ene
rgy
Pro
babi
lity
x
Boltzmann:
Inverse Boltzmann:
Basic idea: Use probability as a proxy for energy
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KNOWLEDGE-BASED DOCKING POTENTIALS
A few types of atom pairs, out of several hundred total
Atom-atom distance (Angstroms)
Nitrogen+/Oxygen- Aromatic carbons Aliphatic carbons
“PMF”, Muegge & Martin, J. Med. Chem. (1999) 42:791
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KNOWLEDGE-BASED POTENTIALSWeaknesses
Accuracy limited by availability of data
StrengthsRelatively easy to implementComputationally fast
StatusUseful, far from perfectMay be at point of diminishing returns
(not always clear how to make improvements)
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MD Prediction of Functional Motions “close”
“open”
Yao and Grant, Biophys J. (2013)
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• MD is still time-consuming for large systems• Elastic network model NMA (ENM-NMA) is an example
of a lower resolution approach that finishes in seconds even for large systems.
Atomistic
C. G.
• 1 bead / 1 amino acid
• Connected by springs
Coarse Grained
i
jrij
COARSE GRAINING: NORMAL MODE ANALYSIS (NMA)
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NMA models the protein as a network of elastic strings
Proteinase K
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Do it Yourself!
Hand-on time!
Focus on section 3 & 4 exploring PCA and NMA apps
https://bioboot.github.io/bimm143_S18/lectures/#12
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Ilan Samish et al. Bioinformatics 2015;31:146-150
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INFORMING SYSTEMS BIOLOGY?
Genomes
DNA & RNA sequence
DNA & RNA structure
Protein sequence
Protein families, motifs and domains
Protein structure
Protein interactions
Chemical entities
Pathways
Systems
Gene expression
Literature and ontologies
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• Structural bioinformatics is computer aided structural biology
• Described major motivations, goals and challenges of structural bioinformatics
• Reviewed the fundamentals of protein structure
• Introduced both physics and knowledge based modeling approaches for describing the structure, energetics and dynamics of proteins computationally
• Introduced both structure and ligand based bioinformatics approaches for drug discovery and design
SUMMARY