G. Narahari Sastry

Molecular Modelling GroupOrganic Chemical Sciences

Indian Institute of Chemical TechnologyHyderabad – 500 007

Gnsastry@iict.res.in; gnsastry@yahoo.comhttp://203.199.182.73/gnsmmg

National Seminar on BioInformatics - Pondicherry

Drug Discovery & DevelopmentIt starts with disease identification

Isolate proteininvolved in disease (2-5 years)

Find a drug effectiveagainst disease protein(2-5 years)

Preclinical testing(1-3 years)

Formulation

Human clinical trials(2-10 years)

Scale-up

FDA approval(2-3 years)

Discovery and Development of Drugs

Discover mechanism of action of disease

Identify target protein

Screen known compounds against target or

Chemically develop promising leads

Find 1-2 potential drugs

Toxicity, pharmacology

Clinical Trials

Genomic Approach to Drug Discovery

Target Discovery

Existing Chemical and biochemical knowledge

Target gene annotation

Literature

Functional & comparative Genomics

Functionally validated target

A CBTarget

PrioritizationBiochemical & Cell

Based Assays

Drug Development Small molecule lead

Screening and improvementHTS+/- in silico SBDD

Therapeutic Application

Translated gene products

A B C

Sequence-structure analysis

Experimental Validation Comparative Proteomics

Genome data

GO terms 1. Molecular Function 2. Biological process 3. Cellular component

Role of targets in disease

Screening and Optimization Cycle with in-silico components

Structure based design

Target Selected

Assay developed

HTS Chemistry begins

Target structure obtained

Candidate taken forward

Database clustering

Similarity analysis

QSAR pharmacophore

Nucleotide Sequence Analysis

Protein Analysis

Protein Modeling

Indirect

Drug Design

Docking

BI

Virtual Screening

106 small-molecule compounds

vHTS: MM + scoring functions

N x 102 leads

Filters: ADMET / QSAR

M x 101 leads

Filters: synthesis / manufacturing / IP / patent / biological assays

1 - 5 leads

Integration of Chemoinformatics and BioinformaticsIntegration of Chemoinformatics and Bioinformatics

Computational chemistry

SmallMolecules

Large MoleculeTargets

Genomic Biology

Bioinformatics Cheminformatics

In silico

HighThroughputScreening

Assays

Much About StructureMuch About Structure

• Structure Function

• Structure Mechanism

• Structure Origins/Evolution

• Structure Anything!!!

• Exact solutions are available only for Hydrogen atom.

• Modeling any realistic system needs approximations (mathematically not solvable)

• Plenty of approximations were put forward to tackle mathematic complexity

Quantum Mechanics

“The underlying physical laws necessary for the mathematical theory of…the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too

complicated to be soluble.” -P. A. M. Dirac

Chemistry is an experimental science

ExperimentalX-Ray

NMR

Structure, Stability

and Reactivity

Thermochemistry

…

…

ComputationalSemiempirical

Ab Initio

DFT

Molecular Dynamics

Simulations

Monte Carlo

…

ResultsFactual Data!!!

Understanding, Patterning and Predicting Qualitative theory, Concepts, Rules, CorrelationsBasis for Doing Science and Doing it Better

But alternative routes are attractive at times!!!

The Jargon of nomenclature • Molecular Modeling• Computational Chemistry• Theoretical Chemistry• Simulations• Quantum Chemistry• Computational Biology• Molecular Dynamics• Mathematical Chemistry

Central Paradigm: Deriving information on molecular systemswithout really synthesizing them.

Computational Chemistry

Quantum Mechanics (QM) Molecular Mechanics (MM)

Hybrid QM / MM Semi-empirical (SE)

The current scenario in chemistry

• Computation has become an effective alternative to explore the structural, energetic, mechanistic and other properties of small molecules (say less than 8-10 atoms).

SOMETIMES THE COMPUTATIONAL ACCURACY SUPERCEDES THE

EXPERIMNTAL ACCURACY

Every Computational Experiment at Any Level of Theory Yields an Answer… Usually Answers for Many Questions

Judging the Reliability is the Crucial Task

Just Like Experiments Fail, Computations Fail

However, the challenges are of different kind in modeling

chemistry and biology!!

It is not only the size but the philosophy!!!..!!!

The paradigm shift …

MESDAMESETMESSRSMYNAMEISWALTERYALLKINCALLMEWALLYIPREFERDREVILMYSELFIMACENTERDIRATVANDYINTENNESSEEILIKENMRANDDYNAMICSRPADNAPRIMASERADCALCYCLINNDRKINASEMRPCALTRACTINKARKICIPCDPKIQDENVSDETAVSWILLWINITALL

3D structure

Biological StructureBiological Structure

Organism

CellSystem Dynamics

CellStructures

SSBs

polymerase

Assemblies

helicase

primase

Complexes

Sequence

Structural Scales

Bottlenecks in developing Bottlenecks in developing Structure – Function RelationshipsStructure – Function Relationships

Structures determined by NMR, computation, or X-ray crystallography are static snapshots of highly dynamic molecular systems

Biological process (recognition, interaction, chemistry) require molecular motions and time dependent.

To comprehend and facilitate thinking about the dynamic structure of molecules is crucial.

Relevant timescalesRelevant timescales

10-15

femto10-12

pico10-9

nano10-6

micro10-3

milli100

seconds

Bond vibration

Isomeris-ation

Waterdynamics

Helixforms

Fastestfolders

typicalfolders

slowfolders

long MD run

where weneed to be

MDstep

where we’dlove to be

Conformational transitions

Enzyme catalysis

Ligand binding

Protein folding

How does the drug differ from an inhibitor?

SelectivityLess toxicityBioavailabilityReach the targetEase of synthesisLow priceSlow (or) no development of resistance Stability upon storage as tablet or solutionPharmacokinetic parametersNo allergies

Bioavailability (ADMET)

• ADMET• Adsorption• Distribution• Metabolism• Excretion• Toxicity

• Model and Predict:• Biotransformations & metabolites• Catalytic reactions• Drug-receptor interactions• GI physiology• Transepithelial transport• Epithelial permeability• Solubility• Toxicity

Which Strategy?

• Do you have a validated target?• Do you have active ligands?• Do you have both?

Computer Aided Drug Design

Science Support

Drug Design

Structure based Ligand based

Ligand (analog)based drug design

Receptor structure is not knownMechanism is known/ unknownLigands and their biological activities are known

Target (structure) based drug design

Receptor structure is knownMechanism is knownLigands and their biological activities are known/ unknown

Various Steps InvolvedVarious Steps Involved

• Get the structure of the receptor

• Identify the active site

• Build a library of possible ligands

• Docking & Scoring

• Understand receptor-ligand interactions

• Design new ligands

Structure Based Ligand DesignStructure Based Ligand Design

O

NH

O

H

O

NH

?

O

O

O

H

O

NH

NSO

O

H

O

NH

O

H

O

NHS?

?

O

H

O

NH

??

?

OO

H

O

NH

DockingBuilding

Linking

CADD Success Stories• FKBP Ligand

• docking and scoring• P. Burkhard et al., J. Mol. Biol. 287, 853-858, 1999

• K+ ion channel blocker• fragment-based evolutionary design• G. Schneider et al., J. Computer-Aided Mol. Design 14, 487-494, 2000

• Ca2+ antagonist / T-channel blocker• pharmacophore similarity search• G. Schneider et al., Angew. Chem. Int. Ed. Engl. 39, 4130-4133, 2000

• Glyceraldehyde-phosphate DH inhibitors• combinatorial docking• J.C. Bressi et al., J. Med. Chem. 44, 2080-2093, 2001

• Thrombin inhibitor• docking, de-novo design• H.J. Bohm et al., J. Computer-Aided Mol. Design 13, 51-56, 1999

• HIV-1 RNA TAR inhibitor• docking, database search• A.V. Filikov et al., J. Computer-Aided Mol. Design 14, 593-610, 2000

• Aldose reductase inhibitors• 3-D database searching• Y. Iwata et al., J. Med. Chem. 44, 1718-1728, 2001

• DNA gyrase inhibitor• structure-based virtual screening• H.J. Boehm et al., J. Med. Chem. 43, 2664-2674, 2000

• Let us look at some of recent interests

Broad Objectives: Aiding the experimentalists in Drug/Molecule/Reaction design

• Theoretical/computational approaches to bring insights which might trigger interest of the prospective experimental groups

(Usually with no collaboration with experimentalists)• Rationalizing the experimental finding with

computations and participate in the designing of experiments

(In collaboration with experimentalists or groups of experimentalists)

We strongly believe that while chemistry and biology are experimental sciences

THEORY-EXPERIMENT INTERPLAY IS INDISPENSABLE

In our pursuit to engage with experimentalists for lead discovery or optimization, our efforts become

restricted in the absence of an experimental structure of the receptor protein/enzyme.

When we analyze, it occurred to us that most of these ‘important target receptors’ whose structures are not available belong to the class of ‘membrane

proteins’.

Non-availability of the receptor structure is a bottleneck…

• Membrane proteins are those that exist in cell membranes.

• They can serve as structural supports, as both passive and active channels for ions and chemicals, or serve more specialized functions such as light reception.

• Membrane proteins form about 25% of all protein sequences.

(They constitute close to 70% of drug targets)

• Only 2% of PDB structures belong to membrane proteins!

MEMBRANE PROTEINS – What are they

Sastry et al, Computational Biology and Chemistry, 2006, in press

Membrane proteins form about 25% of all protein sequences. Only 2% of PDB structures belong to this class!

Membrane Proteins: Classification…

• Receptors for extracellular ligandsEx :- G-Protein coupled receptors Tyrosine kinase receptors

• Transport proteinsEx :- Molecular translocators Ion channels

• Membrane-bound enzymesEx :- Lipid synthases Cytochrome P-450 enzymes

• Proteins associated with cytoskeletal networkEx :- Cytoskeletal attachments

• Proteins associated with energy production Ex :- Photosynthetic complexes

Respiratory chain complexes

Challenges in computer simulations of membrane proteins.

•Heavy molecular weight and size.

•Their association with lipid bilayer.

•Technical limitations related to the accuracy of the

empirical potential function.

•Difficulties with accurately incorporating important variables

such as pH, transmembrane potential.

•Starting configuration of a simulation may also bias the

results in undesirable ways.

•Comparative protein modelling approaches are very

essentialSastry et al, Computational Biology and Chemistry, 2006, in press

•Membrane bound microsomal cytochrome P450 enzyme.•Converts androgens to estrogens by aromatisation of A-ring of steroids.•Estrogens and their carcinogenic metabolites are responsible for progression of breast cancer. WHAT IS THE ROLE OF THESE ACIDIC RESIDUES IN THE AROMATIZATION MECHANISM?

HUMAN AROMATASE: A PERIPHERAL MPHUMAN AROMATASE: A PERIPHERAL MP

PLAY A MAJOR ROLE IN STEROID ANDINHIBITOR BINDING.

HEME

ACIDIC RESIDUES

HOMOLOGY MODEL

Sastry et al, J. Com. Aided Mol. Design, 2006, in press

Our Attempts of Modeling Aromatase

• A protein model is constructed (based on CYP 2C5 (pdb code: 1NR6, sequence identity is found to be 28%)

• The role of acidic residues in controlling the function(substrate binding with androstenedione, testosterone and nor-androgens) is studied.

• Studies help in designing putative inhibitors to control the aromatase activity.

Sastry et al, J. Com. Aided Mol. Design, 2006, in press

PROPOSED AROMATIZATION MECHANISM

A-ring of ANDROGENS

O

O

A

ANDROGEN

MOLECULAR DYNAMICS SIMULATIONSBefore complexation to steroidal substrates

Environment suitable for carboxylate formation

High conformational flexibility

No H-bond interaction

ACTIVE SITE ACIDIC RESIDUES

MOLECULAR DOCKINGAfter complexation to steroidal substrates

A MOLECULE WHICH ARRESTS THESE PROPERTIES IS PROPOSED TO BE AN INHIBITOR

Flexibility decreases. Environmentsuitable for carboxylate formation.

CLAMPED !

H-Bond formation

Repulsive interaction predicted.

Inhibition of aromatase activity by 4-hydroxy androstenedione (formestane)

Critical H-bond between inhibitor and T310 hampering its’ role in the mechanism.

ACTIVE SITE

ONE COULD DESIGN A MOLECULEBY ADDING OR DELETING A GROUP FROM ANDROGENSKELETON TO ARREST THE

PROPERTIES OBSERVED FOLLOWINGCOMPLEXATION.

O

O

A

O

O

A

OH

ANDROSTENE-DIONE(Substrate)

FORMESTANE(Inhibitor)

Human 5-lipoxygenase (5-LO)-Peripheral MPHuman 5-lipoxygenase (5-LO)-Peripheral MP

MODELMODEL

Catalytic domainCatalytic domain

β-barrel domain

•5-LO catalyses the rate limiting steps in leukotriene synthesis. •Calcium binds reversibly to 5-LO, triggering its translocation from the cytoplasm to the nuclear membrane.

Ca(2+) binding

Mg(2+) binding

Tryptophan residues

Non-heme iron

Sastry et al, Biophys. Biochem. Res. Comm, 2004, 320, 461-467

barrel domainbarrel domain•Two calcium binding sites are identified ; ligating residues: F14, A15, G16, Two calcium binding sites are identified ; ligating residues: F14, A15, G16, D18, D19, L76 and D79. D18, D19, L76 and D79.

Ca(2+) location

Important residues which affect activity are marked.

Transmembrane

Lumenal

CytoplasmicATP binds here

Phosphorylation.

Inhibitor binding sites.

E1E2

•Expose ion binding sites sequentially to each side of the membrane.Expose ion binding sites sequentially to each side of the membrane.

Cation binding sites

Sastry et al, Biophys. Biochem. Res. Comm, 2004, 319, 312-320; Biophys. Biochem. Res. Comm, 2005, 336, 961-966

Gastric Proton Pump H(+)K(+)-ATPase – Integral MPGastric Proton Pump H(+)K(+)-ATPase – Integral MPANTI-ULCER TARGETANTI-ULCER TARGET

Inhibitor binding sites

CYS323

CYS815Omeprazole

Covalent linkage

Inhibitor Binding in TM region

However, the large SBA in E2 precludes the covalent binding of Cys815 to omeprazole. This suggested another intermediate conformation with slightly more exposed Cys815. The existence of stable intermediate structures has been proved in 2004.

Cation binding in E1 conformation

H3O+

H3O+

Cα – carbons of arenes in the pump.Regular dispositionaids hydronium transport.

T825

Q941

E797

N794

A341

V340

V343

E345E822

D826

Proposed hydronium binding.

Amino acid ligands (D,E,N,Q) that bind to metal ions in proteins

In general, the acidic amino acid or their amides (ASP, GLU, ASN, GLN) are present in the ligating sphere of the cations (Ca, Na, K, Mg, etc.) .Additional ligating amino acid residues: Ala, Val, Thr, Leu, Phe etc.

Typical non-covalent binding to cations (from PDB). The distances between the ligating atoms and ion vary for different cations.

# of Binding structures for metals

PDB (June 2004)

Ca2+ : 2020; Cu(II) : 298 Ni(II) : 118

Na+ : 678; Mn (II) : 454 Co(II) :101

K+ : 258; Fe (II) : 100 Fe(III) :269

Mg2+ : 1167; Zn (II) : 1545

Asp Glu Asn Gln

An investment in knowledgeAn investment in knowledge pays the best interest. pays the best interest.

Benjamin FranklinBenjamin Franklin

CAUTION….

macromolecular structure

protocols

methods

Structure determinations methods

•Don't be a naive user!?!

•When computers are applied to biology, it is vital to understand the difference between mathematical & biological significance

•computers don’t do biology, they do sums quickly

Traditional ApproachRational Approach

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It’s like a game of LUDO

Drug Discovery

““This isn’t rocket science. This isn’t rocket science. This is much harder.”This is much harder.”

-- Alan Holmer-- President, PhRMA

GNS, Dr. G. Madhavi Sastry, Dr. Y. Soujanya, Srinivas Reddy, Punnagai, Gayatri, Srivani, Sateesh, Nagaraju, Dolly, Srinivasa Rao, Prasad, Mukesh, Murty, Usha Rani, Srinivas, Janardhan, Bharat, Upendra.Past Ph.D. students: Dr. U. Deva Priyakumar, Mr. T.C. Dinadayalane