MIDTERM EXAM DDD - COMPUTER AIDED DRUG DESIGN CADD

.Drug Design

the inventive process of finding new medications based on the knowledge of a biological target

complementary molecules in shape and charges to the biomolecular target

Drug design involves the design of _____ with which they interact and, therefore, will bind to it

• organic small molecule

• complementary in shape to the target

• oppositely charge to the biomolecular target

Selected/design molecule should be:

• interact with the target

• bind to the target

• activates or inhibits the function of a biomolecule, such as protein

The molecule will:

molecular level, target molecule(s), interact with the target molecule

When the disease process is understood at the _______ and ➢the ______ are defined, ➢drugs can be designed specifically to _______ in such a way as to disrupt the disease

Computer-aided drug design

The type of modeling that uses a computational approach is called computeraided

CADD

uses computational approaches to discover, develop, and analyze drugs and similar biologically active molecules

CADD

represents computational methods and resources that have made key contributions tot the development of drugs that are in clinical use or clinical trials or to facilitate the design and discovery of new therapeutic solutions

• hit identification

• hit-to-lead optimization

• lead optimization

Drug design with the help of computers may be used at any of the following stages of drug discovery

Hit identification

using virtual screening (structure-or ligand-based design)

Hit-to-lead optimization

of affinity and selectivity (structure-based design, QSAR,etc.)

Lead optimization

optimization of other pharmaceutical properties while maintaining affinity hit

• random screening against disease assays

• natural products, synthetic chemicals

Objectives of CADD
to change from:

• Rational drug design and testing

• Speed-up screening process

• Efficient screening (focused, target-directed)

• De novo design (target-directed)

• Integration of testing into the design process

• Fail drugs fast (remove hopeless ones as early as possible)

Objectives of CADD

target-directed

De novo design

Identify and optimize

THE TARGET OF COMPUTER-ASSISTED DRUG DESIGN (CADD) IS NOT TO FIND THE IDEAL DRUG BUT TO _______ LEAD COMPOUNDS AND SAVE SOME EXPERIMENTS

• safety

• efficiency

• stability

• solubility

• synthetic viability

• novelty

The parameters expected from a drug are

predicting 3D structures, design of compounds, prediction of druggability, and in silico ADMET predictiom

CADD is beneficial for pharmaceutical development in

50%

the use of CADD approaches can reduce the cost of drug discovery and development by up to

Screening is reduced

through it, we can reduce the synthetic and biological testing efforts

Information about the disease

it gives the most promising drug candidate by eliminating the compounds with undesirable properties (poor efficacy, poor ADMET, etc.) through in-silico filters

Cost-effective & Time saving

due to its rapid and automatic process

Database screening

through it , we can learn about the drug-receptor interaction pattern

Accuracy

it gives compounds with high hit rates through searching huge libraries of compounds in silico in comparison to traditional high throughout screening

• screening is reduced

• information about the disease

• cost-effective & time saving

• database screening

• accuracy

• less manpower is required

• minimizes the chances of failures in the final phase

advantages of CADD

• Ligand-based drug design/indirect approach

• Structure-based drug design/direct approach

Main types of approaches for CADD

Ligand-Based DD

indirect approach

Structure-based DD

direct approach

Ligand-Based DD

3d structure of the target is unknown, but the knowledge of ligands that bind to the desire target site is known

Ligand-Based DD

generally, are pharmacophore based approach and quantitative-structure activity relationships (QSARs)

Ligand-Based DD

it is assumed that compounds which having similarity in their structure also having the same biological action and interaction with the target protein.

Ligand-Based DD

relies on knowledge of other molecules that bind to the biological target of interest

Ligand-Based DD

relies on knowledge of other molecules that bind to the biological target of interest

Ligand-Based DD

used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess to bind to the target

Ligand-Based DD

a model of the biological target may be built based on the knowledge of what binds to it, and this model, in turn, may be used to design new molecular entities that interact with the target

Ligand-Based DD

Alternatively, a quantitative structure-activity relationship (QSAR) may be derived, in which a correlation between calculated properties of molecules and their experimentally determined biological activity. These QSAR relationships, in turn, may be used to predict the activity of new analogs.

Structure-based DD

The structure of the target protein is known, and interaction or bioaffinity for all tested compounds is calculated after the process of docking

Homology modeling

also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" and an experimental three-dimensional structure of a related homologous protein (the "template")

First Cycle

SBDD cycle?

comprises isolation, purification, and structure determination of the target protein by one of three key methods:

First Cycle SBDD

• X-ray crystallography, homology modeling, or NMR.

• Using compounds comes through virtual screening of different databases placed into a selected protein region (active site). These compounds are scored and ranked based on this molecule's steric, hydrophobic, and electrostatic interaction with the target protein’s active site.

• Top-ranked compounds are tested with biochemical assays

Second Cycle

SBDD cycle?
comprises structure determination of the protein in complex with the most optimistic lead of the first cycle, the one with minimum micro-molar inhibition in-vitro, and shows sites of the compound that can be optimized for further increment in the potency

1. Interactive graphics

2. Intelligence of a medicinal chemist

3. Various automated computational procedures may be used to suggest new drug candidates

➢ Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using

1. Target Identification & validation

2. Analyze structure for potential ligand binding sites

3. Lead identification & molecular docking

4. Lead validation & optimization

5. Clinical trials

Steps involved in SBDD

Binding site identification

the first step in SBDD

Binding site identification

relies on identifying concave surfaces on the protein (target) that can accommodate drug-sized molecules with appropriate “hot spots” (hydrophobic surfaces, hydrogen boding sites, etc.) that drive ligand binding.

Docking

attempts to find the “best” matching between two molecules. it includes finding the Right Key for the Lock

Docking

aims:

• to place a ligand (small molecule) into the binding site of a receptor in the manners appropriate for optimal interactions with a receptor

  • to evaluate the ligand-receptor interactions in a way that may discriminate the experimentally observed mode from others and estimate the binding affinity

Rigid Docking (Lock and Key)

the internal geometry of both the receptor and ligand are treated as rigid

Flexible Docking (Induced Fit)

an enumeration on the rotations of one of the molecules (usually smaller one) is performed. Every rotation the energy is calculated; later the most optimum pose is selected

protein-ligand

protein-protein

protein-nucleotide

docking can be between

Molecular Docking

an in-silico method that predicts that placement of small molecules or ligands within the active site of their target protein (receptor). It is mainly used for accurate estimation of most favorable binding modes and bio-affinities of ligands with their receptor; presently, it has been broadly applied to virtual screening for the optimization of the lead compounds

search algorithm and scoring functions

in molecular docking method, the basic tools are a ___ and ____ for creating and analyzing conformations of the ligand

binding pose, binding affinity, and virtual screening

molecular docking methodology comprises mainly three goals which are interconnected to each other like prediction of

I - pre-and/or during docking

II - during docking

III - during docking and scoring

Components of docking

I - pre-and/or during docking

representation of receptor biding site and ligand

II - during docking

sampling of configuration space of the ligand-receptor complex

III - during docking and scoring

evaluation of ligand-receptor interactions

Importance

• key to rational drug design

• docking results can be used to find inhibitors for specific target proteins and thus to design new drugs.

• gaining importance as the number of proteins whose structure is known increases

• In addition to new drug discovery, it is of extreme relevance in cellular biology, where function is accomplished by proteins interacting with themselves and with other molecular component

• rational design of drugs

• identification of the ligand’s correct binding geometry (pose) in the binding site (binding mode)

• prediction of the binding affinity (scoring function)

Molecular Docking Importance

Drug targets

Uses of docking

• virtual screening (hit identification)

• drug discovery (lead optimization)

• bioremediation

Application of Docking

Virtual screening (hit identification)

docking with a scoring function can be used to quickly

Drug Discovery (lead optimization)

• docking can be used to predict where and in which relative orientation a ligand binds to a protein (binding mode or pose)

  • this information may, in turn, be used to design more potent and selective analogs

Bioremediation

protein ligand docking can be used to predict pollutants that enzymes can degrade

1. Virtual screening

2. De Novo Design

3. Optimization of new ligands

Methods of CADD

Virtual Screening

the first method is the identification of new ligands for a given receptor by searching large databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs.

Virtual screening

has been used as a most convenient tool nowadays to find the most favorable bioactive compounds with the help of information about the protein target or known active ligands

“mind-blowing alternative of high-throughput screening)

in recent times, virtual screening is known as a , mainly in terms of cost-effectiveness and probability of finding the most appropriate novel hit through filtering the large libraries of compounds

1. Structure-based virtual screening (SBVS)

2. Ligand-based virtual screening (LBVS)

There are generally two types of virtual screening approaches

Structure-based virtual screening (SBVS)

relies on the structure of the target protein active site

Ligand-based virtual screening (LBVS)

is based on estimating calculated similarity between the known active and compound come from databases

De Novo Design

➢ means start afresh, from the beginning, from scratch

De Novo Design

a process in which the 3D structure of the receptor is known and the structure of the drug is unknown

De Novo Design

involves structural determination of the lead target complexes and lead modifications using molecular modeling tools'; these pieces can be either individual atoms or molecular fragments

De Novo Design

the key advantage of such method is that novel structures can be suggested

• rather slow and inefficient

• ignores synthetic feasibility while constructing structures

• cannot be the sole basis for drug design

Limitations of De Novo Design

Assembling possible compounds and evaluating their quality

Searching the sample space for novel structures with drug-like properties

Principles of De Novo Drug Design

1. Growing

2. Linking

3. Lattice-Based sampling dynamics based

4. Molecular methods building strategy

De Novo Drug Design

Growing

A single key building block is the starting point ➡ Fragments are added to provide suitable interactions to both key sites and space between key sites ➡ These include simple hydrocarbon chains, amines, alcohols, and even single rings ➡ In the case of multiple seeds, growth is usually simultaneous and continues until all pieces have been integrated into a single molecule

Linking

The fragments, atoms or building blocks are either placed at key interaction sites (or) pre-docked using another program ➡ They are joined together using pre-definied rules to yield a complete molecule ➡ linking groups or linkerse may be predefined or generated to satisfy all required conditions

Lattice-based

The lattice is placed in the binding site, and atoms around key interactions sites are joined using the shortest path ➡ then various interaction search of which includes translation, rotation, or mutation of atoms are guided by a potential energy function, eventually leading to a target molecule

Molecular Dynamics Method

The building blocks are initially randomly placed and then by MD simulations allowed to rearrange ➡ after each rearrangement certain bonds were broken and the process repeated ➡ during this procedure high scoring structures were stored for later evaluation

Optimization of new ligands

evaluating proposed analogs within the binding cavity

1. Generation of potential primary constraints

2. Derivation of interaction sites

3. Building up methods

4. Assay (or) scoring

5. Search strategies

6. Secondary target constraints

Computer Based Drug Design consists of following steps

Categories of Software

• databases & Draw tools

• Molecular Modelling & Homology Modelling

• Binding site prediction & Docking Ligand design

• Screening -QSAR

• Binding free energy estimation A

• DME Toxicity

Databases

• ZincDatabase

• Zinc15Database

• ChEMBL

• JChemforExcel

• ProteinDataBank(PDB)

• BindingMOAD(MotherO fAllDatabase)

• PDBbind

• STITCH,SMPDB

SBDD

Human Renin (Hypertension)

SBDD

Collagenase & Streptomylesin (anti-CA, anti-RA)

SBDD

Purine nucleotide phosphorylase (antidepressant)

SBDD

Thimydylate synthase

- structural based discovery

K+ ion channel blocker

- chemical descriptor-based discovery

Ca2+ antagonist / T-channel blocker

- combinatorial docking

Glyceraldehyde-phosphate DH inhibitors (anti-trypanosomatid drugs)

- docking, de-novo design

Thrombin inhibitor

De Novo Drug Design

Protease Inhibitor- HIV-1

Indinavir

- 3D structure and QSAR