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Cadd assignment 4 (sarita)
1. SARITA MAURYA
CADD ASSIGNMENT UNIT-4
1. What do you mean by drug-likness and druggability? Explain 'rule of five'
for screening of drug like molecules.
2. Explain pharmacophore/ligand based virtual screening of libraries
3. What do you mean by virtual screening? How it is crucial in CADD?
4. What is docking? Explain rigid and flexible docking with the help of
suitble example
5. What is 'conformational search space', explain it with respect to the
docking.
6. What is the concept behied random searching, explain Tabu search and Monte-
calrlo search, how they differ with each other?
7. What do you mean by systematic searching? Explain 'Incremental-
construction searching' with the help of suitabe diagram.
8. What is the importance of scoring in docking?
9. Write note on following scoring functions
a) Force-Field Based scoring,
b) Empirical Scoring Function,
c) Knowledge-Based scoring function
10. Which scoring function is more suitable when we consider molecular
interaction in aqueous conditions and why?
Que1: What do you mean by drug-likeness and drug ability? Explain ‘rule of five’ for screening
of drug like molecules.
2. Ans: Drug -likeness:Drug-likenessisaqualitativeconceptusedin drugdesignforhow "drug-like"a
substance iswithrespecttofactors like bioavailability.Itisestimatedfromthe molecularstructure
before the substance isevensynthesizedandtested.
Drug-ability:Druggability isaterm usedin drugdiscovery todescribe abiological target(suchas
a protein) thatisknownto or ispredictedtobindwithhighaffinitytoa drug. Furthermore,bydefinition,
the bindingof the drug to a druggable targetmustalterthe functionof the target witha therapeutic
benefittothe patient.The conceptof druggabilityismostoftenrestrictedto small molecules (low
molecularweightorganicsubstances) butalsohasbeenextendedtoinclude biologicmedical
products such as therapeuticmonoclonal antibodies.
Despite technology advancement in the drug designing process, most drug discovery projects fail
because of the druggability problem. To avoid the failure of a drug discovery project, which very
expensive. It is very important to understand the difficulties associated with a potential target. Druggability
has become part of the target identification and validation process, more significantly in the case where
targets do not belong to traditional classes.
‘Rule of five’ for screening of drug like molecule:
Lipinski'srule of five,alsoknownasPfizer's rule of five orsimplythe rule of five (RO5),isarule of
thumbto evaluate drug-likenessordetermine if achemical compoundwith acertainpharmacological or
biological activityhaschemical propertiesandphysical propertiesthatwouldmake italikelyorally
active drug inhumans.
The rule was formulated by Christopher A. Lipinski in 1997, based on the observation
that most orally administered drugs are relatively small and moderately lipophilic
molecules.
The rule describes molecular properties important for a drug’s pharmacokinetics in the
human body, including their absorption, distribution, metabolism and excretion
(“ADME”).
The rule is important to keep in mind during drug discovery when a pharmacologically
active lead structure is optimized step-wise to increase the activity and selectivity of the
compounds as well as to ensure drug-like physicochemical properties are maintained as
described by Lipinski’s rule.
Candidate drugs that conform to the RO5 tend to have lower attrition rates during clinical
trials and hence have an increased chance of reaching the market.
Lipinski’s rule states that, in general, an orally active drug has no more than one violation of the
following criteria:
The molecular weight is greater than 500dalton.
The compound’s lipophilicity, expressed of the partition coefficient between water and 1-
octanol), is less than 5.
The number of groups in the molecule that can donate hydrogen atoms to hydrogen
bonds (usually the sum of hydroxyl and amine groups in a drug molecule) is less than 5.
3. The number of groups than can accept hydrogen atoms to from hydrogen bonds
(estimated by the sum of oxygen and nitrogen atoms) is less than 10.
Note that all numbers are multiples of five, which is the origin of the rule’s name. As with many
other rules of thumb, (such as Baldwin’s rules for ring closure), there are many exceptions to
Lipinski’s Rule.
The rules, based on the 90- percentile values of the drug’s property distributions, apply
only to absorption by passive diffusion of compounds through cell membranes;
-compounds that are actively transported through cell membrane by transporter proteins are
exceptions to the rule.
Physico-chemical criteria, such as those defined by the Lipinski rule of five, are typically used to
predict whether a compound is drug-like or not.
1. The bar chart shows the number of oral drugs that fall or pass the Lipinski rule, based on
a set of 771 drugs approved by the US Food and Drug Administration.
Hopkins and colleagues report a method for predicting the drug-likeness of compounds on a
scale of 0 (not drug-like) to 1 (drug- like). The chart shows the distribution of drug-likeness
calculated using this method for the same drugs depicted in a.
The analysis shows an overlap of Lipinski passes and failure for a range of drug-likenesses,
Notably, some very drug-like molecules fall the Lipinski rule, whereas some very un-drug-like
compounds pass it.
Que2: Explain Pharmacophore/ligand based virtual screening of libraries.
Ans: Ligand Based Virtual Screening: In LBVS process, the most effective biologically active
lead molecule is detected using structural or topological similarity or Pharmacophoric similarity
4. search. Taking into consideration several criteria-such as structure as well as shape of
individual fragments or electrostatic properties of the molecule carries out the similarity
comparisons.
The leads generated are ranked based on their similarity score, obtained using different
methods or algorithms.
Similarity based: A quantitative measure of similarity between two sets of molecular
descriptors.
1 Molecular similarity search based screening
1a. Small Molecule Alignment: In small molecular alignment the detection of similarity is
carried out by superimposing each of the test molecules of the database with the reference
molecule, and based on their extent of similarity they are ranked.
Generally in the super-position process the test molecule is taken as flexible, and the reference
molecule can be rigid or flexible. For example in Flexs algorithm, the reference molecule is
considered as rigid.
2b. Fragment based superimposition: Fragment based superimposition processes are also
used in several similarity search algorithms in determining the bioactivity of molecule.
-For example Fflash program uses graph based clique detection procedure using the
fragment based comparison of feature patterns.
2. Descriptor similarity Based Screening
5. In molecular alignment technique, a single molecule comparison takes a considerable amount
of time.
Hence the descriptor representation of the molecule is introduced and being used for searching;
which has been proved to be more efficient aid in searching the large chemical databases
2.1) 1-D and 2D- Descriptors: Bulk properties like Molecular weight, Molar refractivity, log P are
in general considered as one-dimensional (1D) descriptors of a molecule
-a) Binary Descriptor in binary descriptor representation, the presence of structural properties
for each position of lead molecule is narrated by means of a Boolean bit set to ‘one’ otherwise to
‘zero’.
-b) Real-value Descriptors: The real value descriptor vectors represent the Pharmacophoric
site of a lead compound by generating a graph.
2) 3D Descriptors: 3D similarity search is based on the concept that molecule with similar
conformational features shows similar biological activity i.e. Pharmacophore.
A candidate ligand can compare to the Pharmacophore model to determine whether it is
compatible with it and therefore likely to bind.
The estimation of similarity in descriptor-based analysis is also based on different
framework of descriptor (3D descriptors) and the different coefficient used in this search
procedure.
The 3D descriptors can be generated using different programs such as 3D –FEATURE
based on different hydrophobic groups, hydrogen bond acceptor and hydrogen bond
donor.
The ligand can also be transformed from 2D to 3D by means of programs like CORINA.
Numerous descriptors can also be generated by taking into consideration the functional
groups and primary shape properties.
Que3: What do you mean by Virtual screening? How it is crucial in CADD?
Ans: Virtual screening: Virtual screening refers to computational and a range of in-silico
techniques used to search large compounds databases to select a smaller number for biological
testing. Virtual screening can be used to –select compounds for screening from in house
database choose compounds to purchase from external suppliers decide which compounds to
synthesis next. The technique applied depends on the amount of information available about
the particular disease target.
Virtual screening is basically two types-
1. Structure Based Virtual Screening (SBVS): When virtual screening has used the
structural knowledge of receptor, it is known as structure based screening. It involves
automated and fast docking of a large number of chemical compounds against a protein-
binding or active site, directing a way to use the rapidly increasing number of protein 3D-
struture.
6. 2. Ligand Based Virtual Screening (LBVS): When structure of receptor is not known
virtual screening use alternative approach, which is based on the structure
representative of known ligands for a particular receptor (Pharmacophore), such
approach is known as ligand/ Pharmacophore based virtual screening.
Scope: Virtual screening (vs) is an important component of cheminformatics, CADD and
molecular modeling.
-An abundance of structural information, indicated by both the ever-increasing availability of 3-
dimensional (3D) protein structures and the readiness of free conformational databases of
commercially available compounds, such as ZINC, supplies a broad platform for VS.
-At the same time, new technology enables the implementation of more accurate and
sophisticated Pharmacophore models and the screening of millions of compounds within a
manageable period.
Crucial role in CADD:
The experimental efforts to carry out the biological screening of billions of compounds
are still considerably high, and therefore, computer-aided drug design approaches have
become attractive alternatives.
In recent years, virtual screening has reached a status of a dynamic and lucrative
technology in probing for novel drug-like compounds or so called hits in the
pharmaceutical industry.
Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two
basic approaches of computer-aided drug design (CADD) used in modern drug
discovery and development program. Virtual screening (or in silico screening) has been
used in drug discovery program as a complementary tool to high throughput screening
(HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained
considerable interest in the pharmaceutical research as a productive and cost-effective
technology in search for novel molecules of medicinal interest. In recenttimes,the use of
VStechniqueshasbeenshowntobe an excellentalternative tohighthroughputscreening,
especiallyintermsof cost-effectivenessandprobabilityof findingthe mostappropriate result
througha large virtual database.
Virtual Screeningfordrugdiscoveryisbecominganessentialtool toassistinfastand cost-
effectiveleaddiscoveryanddrugoptimisation.Thistechniquecanaidinthe discoveryof
bioactive molecules,since theyallow the selectionof compoundsinastructure database that
are mostlikelytoshowbiological activityagainstatarget of interest.VStoolsplayaprominent
role amongthe strategiesusedforthe identificationof new bioactivesubstances,since they
increase the speedof the drugdiscoveryprocessaslongas theyautomaticallyevaluate large
compoundlibrariesthroughcomputational simulations.
Que4: What is docking? Explain rigid and flexible docking with the help of suitable
example.
7. Ans: Docking: Docking is a method which predicts the preferred orientation of one molecule to
a second when bound to each other to form a stable complex. Knowledge of the preferred
orientation in turn may be used to predict the strength of association or binding affinity between
two molecules using, for example, scoring functions.
Docking has been a capable choice for the modeling of 3-dimentional structure of the
receptor-ligand complex and evaluating the stability of the complex that determines the
specific biological recognition.
In a simple definition, docking is a technique/method or process that is used to predict
how a protein (enzyme) interacts with small molecules (ligands). The ability of a protein
(enzyme) and nucleic acid to interact with small molecules to form a supramolecular
complex plays a major role in the dynamics of the protein, which may enhance or inhibit
its biological function.
The docking problem can be subdivided into two steps-
Exploring the conformational space of ligands that bind to target molecules
Scoring this set, i.e. ranking it in according to the estimated binding affinity.
Rigid Docking: In the rigid molecule docking problem we will relate to the molecules as rigid
body and components are not allowed to modify at any stage.
Example of Rigid Docking: Matching algorithm
Matching algorithm (MA): Several docking procedures have been classed here as matching
algorithms, as they common method of aligning structural features of the protein and ligand.
The constraints a pattern-matching algorithm can be based on shape and/or chemical
information. Frequently, complementary atom types such as hydrogen bond donors and
acceptors are paired between the protein and ligand.
Clique detection, a pattern-matching technique from graph theory, can be used to match
structural (physiochemical) features of ligand and receptor pockets.
- For example, a set of atoms with given chemical properties can be matched to a set of
complementary atom positions within the binding pocket, and the inter-atomic distances
between ligand atoms provide constraints on solution set.
- Generally these algorithms are applied to a rigid body or sets of rigid bodies
representing possible ligand conformations. Pattern-matching for ligand-docking ZDOCK
and FLOG.
Fast Shape Matching (SM)/Geometric Hashing:Shape matching algorithms are approaches
that take into account the geometric overlap between two molecules.
-Different algorithms are employed in order to make several alignments between and
receptor.
-This approach may identify the possible binding sites of a protein by a macromolecular surface
search.
8. Flexible Docking: In this we consider the molecule as flexible body and permit conformational
changes.
(a) Random searching
(b) Systematic searching
(c) Simulation based searching
Example of Flexible Docking: Genetic Algorithm
Genetic Algorithm: Genetic algorithm is based on Darwinian evolution-survival of the fittest
and decent with modification.
In a GA, there is a population of solutions that undergo unary (mutation) and higher
order (crossover) transformations. The newly generated solutions undergo selection,
biased towards the fit among them.
The algorithm maintains a selective pressure towards an optimal solution, with
randomized information exchange permitting exploration of the search space.
Example of application of genetic algorithm to docking procedures includes GOLD,
AutoDock, DARWIN,DOCK.
Que5: What is conformational search space? Explain it with respect to the docking.
Ans: Inthe case of protein docking, the searchspace consistsof all possible orientationsof the protein
withrespecttothe ligand. Flexible dockinginadditionconsidersall possible conformations of the
proteinpairedwithall possible conformationsof the ligand.
The search space in theory consists of all possible orientations and conformations of the protein
paired with the ligand. However, in practice with current computational resources, it is
impossible to exhaustively explore the search space—this would involve enumerating all
possible distortions of each molecule (molecules are dynamic and exist in an ensemble of
conformational states) and all possible rotational and translational orientations of the ligand
relative to the protein at a given level of granularity. Most docking programs in use account for
the whole conformational space of the ligand (flexible ligand), and several attempt to model a
flexible protein receptor. Each "snapshot" of the pair is referred to as a pose.
A variety of conformational search strategies have been applied to the ligand and to the
receptor. These include:
Systematic or stochastic torsion searches about rotatable bonds
Molecular dynamics simulations
Genetic algorithms to "evolve" new low energy conformations and where the score of each pose
acts as the fitness function used to select individuals for the next iteration.
Que6: What is the concept behind random searching? Explain Tabu search and Monte-
carlo search, how they differ with each other?
9. Ans: Random search: These searchalgorithmsoperate bymakingrandomchangestoeithera single
ligandor a populationof ligands.A newlyobtainedligandisevaluatedonthe basisof a predefined
probabilityfunction. Example,GA,Tabusearch and Monte Carlo
Tabu Search algorithm:Tabu search (ithas a memorystructure),combinesaminimizationprocedure
withrestrictionsonthe searchpath,such that the solutionisforcedintopreviouslyunexploredregions
on the searchspace.It proceedsstepwisefromaninitial solution, while maintainingalistof previous
solutions.
The listof previoussolutionsprovidesbotharankingof solutionsanda partial recordof
exploredregionsof the searchspace.
The Tabu search algorithmgeneratesasetof N new solutionsfromthe previous solution,and
one of N solutioniskept.A solutionisaddedtothe listif itis the bestsolutionsofar,or the
solutionof exploresanewregionsof the searchspace.
The Tabu search algorithmhasbeenusedinthe PRO_LEADS, dockingalgorithm, andin
conjunctionwithgeneticalgorithms.
Monte Carlo Algorithm: It algorithmsgeneratesainitial configurationof ligandinanactive site,
consistingof a randomconformation(translationandrotation).
-Score the initial configuration
- Generate anewconfigurationanscore it
-Use Metropoliscriterion(If ascore of new configurationisbetterthanthe previousone,itwill
immediatelyaccepted.If aconfigurationisnota minimum, aboltzman-basedprobabilityfunctionis
applied.Now,if the conformation passesthe probabilityfunctiontest,itwillaccepted,otherwise
rejected) todetermine whetherthe new configurationisretained.
-Repeatpreviousstepsuntil the desirednumberof configurationare obtained.
Que7: What do you mean by systematic searching? Explain ‘Incremental –construction
searching’ with the help of suitable diagram.
Ans: systematicsearch: These method/algorithmsare tryto explore all degree of freedomina
molecule,butultimatelyface the problemof combinatorialexplosion.Therefore ligandsare often
incrementallygrownintoactive sites.
A stepwise/incrementalsearchcanbe accomplishedindifferentways,fore.g.bydockingthem
covalentlyoralternativebydividingdockingligandsintorigid(core fragment) andflexibleparts(side
chain).Ex.FlexX,Dock.
Incremental construction (IC):Incremental constructionalgorithmsuse apiecewiseassemblyof the
ligandwithinadefinedbindingpocketinthe receptor.Rigidfragmentsare generatedfromthe ligandby
breakingitat rotatingbinds,tocreate a setof fragmentstobe usedby the dockingalgorithm.One or
10. more of these fragments, usuallythe largest,isselectedasthe starting/base fragmentsandisdockedto
the receptor.
A setof possible dockedorientationsforthisfragmentiskept,andotherfragmentsare addedinvarious
orientations.Afterthe fragmentsare dockedthe parts are fusedtogetherandthenscored.Thisprocess
isrepeateduntil the entire ligandisassembled.
Que8: What is importance of scoring in docking?
Dockingapplicationsusingincremental constructioninclude FlexX,Hammerhead,HOOK,and as a
componentof DOCK_4.0 and Surflex.De novoliganddesignalgorithmsusingincremental construction
include LUDIand GROWMON.
Ans: Scoring: The evaluationandrankingof predictedligandconformationisacrucial aspectof
structure-basedvirtualscreening.
-Evenwhen bindingconformationsare correctlypredicted,the calculationsultimatelydonotsucceedif
theydo notdifferentiate correctposesfromincorrectones.
-Sothe designingof reliable scoringfunctionandschemesisof fundamental importance.
Scoring functionsare approximate mathematical methodsthatusedtopredictthe strengthof the non-
covalentinteraction(alsorefferedtoasbindingaffinity) betweentwomolecules. The evaluationand
rankingof predictedligandconformationisaactual aspectof structure-basedvirtual screening.
Goals/Importance:
The firstrequirementforauseful scoringfunctionistobe able todistinguishthe experimentally
observedbindingmodes –associatingthemwiththe lowestbindingenergiesof the energy
landscape – fromall the other posesfoundbythe searchalgorithm(poseprediction).
The secondgoal isto classifyactive andinactive compounds(VS),andthe thirdisthe prediction
of the absolute bindingaffinity,rankingcompoundscorrectlyaccordingtotheirpotency
(binding affinityprediction).
The last one isthe mostchallengingtask,mainlyin denovo designandleadoptimization,since
small differencesinthe compoundcouldleadtodrasticchangesinbindingaffinity.
An ideal scoringfunctionwouldbe able toperformthe three tasks.However,givenseveral
limitationsof currentscoringfunctions,theyexhibitdifferentaccuraciesondistincttasksdue to
modelingassumptionsandsimplificationsmade duringtheirdevelopmentphase,being
intrinsicallyassociatedwiththe mainpurpose of the evaluated scoringfunction.
Dockingprotocolscan adoptdifferentscoringfunctionsforeachstep,e.g.,one canuse a fast
scoringfunctiontopredictbindingmodesandfurtherpredictaffinitiesemployingamore
sophisticatedscoringfunctionspecificforaffinityprediction.
Scoringfunctionsare typicallydividedintothree mainclasses:
(A) Force-FieldBasedscoring
13. Ans: Scoringfunctionsare usedtodiscriminate betweendifferentsolutionsevaluatingabroad range of
propertiesincluding,butnotlimitedtointermolecularinteraction,Desolvation,electrostatic,and
entropiceffects.Itcanbe classifiedasforce field-Based,empirical,andknowledge-Based.The scoring
functiontermare hydrophobiccomplementary,polarcomplementary,andentropy.The fourequations
relatedtostericscore,polarscore,polar repulsionscore,andentropyscore are usedtodefine the
scoringfunction.Forthe molecularinteractioninaqueous,the more suitable scoringfunctionamongst
the three isforce field-basedscoringfunction.A force fieldwithexpressesthe energyof the systemasa
sumof diverse non-bondedTerms(viz;vanderWaals(VDW) interactions,electrostaticinteractions,and
Bondstretching/binding/torsionforces),involvedinmolecularrecognition,are Usedforthe
developmentof force-fieldscoringfunctions.
Force-filedmethodsutilizeavarietyof Force-fieldparameters.Empirical scoringfunctionuses
several intermolecularinteractionterms whichare calibratedwithmaximumpossible
experimental data.The ideathatbindingenergiescanbe approximatedbyasumof individual
uncorrelatedtermsisusedindesigningof these functions.A typical semi-empirical force-field
scoringfunctionused inmoleculardockingthroughDOCKiscomparedof twoenergy
componentsof Lennard-JonesPotentialandanelectrostatictermwhose energyparametersare
takenfromthe Amberforce-field.
The common wayof introducingthe effectof desolvationtermsistotreatwatermolecules
explicitly.Howeverthese methodsare computationallyexpensive.The computational costis
reducedbytreatingwateras continuumdielectricmedium.
These modelsincludespoission- Boltzmansurface area(PB/SA ) andGeneralized-Bornsurface
area (GB/SA ) whichare oftenusedinpost scoringof the dockingprograms.
In some simplifiedscoringsolvationeffectinligandbindingfree energycalculationsis
performedusingaGB/SA approach.
The electrostaticinteractionsandthe electrostaticdesolvationcostsare calculatedwiththe GB
model while the hydrophobiccontributionsfornon-polaratomsare estimatedusingthe
solvent-accessiblesurface area(SA) of the atoms. Lennard-Jonespotentialisutilizedforthe
estimationof van-derwaalsenergies.
