LECTURE--Bioinformatics in Rational Drug Designing

Category: Entertainment

Presentation Description

No description available.


Presentation Transcript

Bioinformatics in Rational Drug Designing: An Introduction : 

Bioinformatics in Rational Drug Designing: An Introduction Anuradha Chaudhary Bioinformatic Centre (Biotech Park) Lucknow

Slide 2: 

What is ?

Slide 3: 

Bioinformatics combines the tools of Biology, Chemistry, Mathematics, Statistics and Computer Science to understand Life & its processes. Bioinformatics bridges many disciplines

Slide 4: 

Bioinformatics can be defined as "the collection, archiving, organisation and interpretation of biological data" (Orengo et al., 2003) What is Bioinformatics: Definition

Slide 5: 


Slide 6: 

Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data,including those to acquire, store, organize, archive, analyze, or visualize such data. The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline.There are three important sub-disciplines within bioinformatics: the development of new algorithms and statistics with which to assess relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information.“ What is Bioinformatics: Definition

Slide 7: 

Bioinformatics- Definition (As submitted to the Oxford English Dictionary) (Molecular) bio informatics: bioinformatics is conceptualising biology in terms of molecules (in the sense of Physical chemistry) and applying informatics techniques(derived from disciplinessuch as applied maths, computer science and statistics) to understand andorganise the information associatedwith these molecules, on a large scale. In short, bioinformatics is a management information system for molecular biology and has many practical applications. (3) --Source: What is bioinformatics? A proposed definition and overview of the field. NM Luscombe, D Greenbaum, M Gerstein (2001) Methods Inf Med 40: 346-58) What is Bioinformatics: Definition

Slide 8: 

Website / other sources Bioinformatics or computational biology is the use of mathematical and informational techniques, including statistics, to solve biological problems, usually by creating or using computer programs, mathematical models or both. One of the main areas of bioinformatics is the data mining and analysis of the data gathered by the various genome projects. Other areas are sequence alignment, protein structure prediction, systems biology, protein-protein interactions and virtual evolution. (source: www.answers.com) Bioinformatics is the science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research. (source: www.whatis.com) As a discipline that builds upon computational biology, bioinformatics encompasses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. As a discipline that builds upon the life, health, and medical sciences, bioinformatics supports medical informatics; gene mapping in pedigrees and population studies; functional-, structural-, and pharmaco-genomics; proteomics, and dozens of other evolving “Omics”.As a discipline that builds upon the basic sciences, bioinformatics depends on a strong foundation of chemistry, biochemistry, biophysics, biology, genetics, and molecular biology which allows interpretation of biological data in a meaningful context. As a discipline whose core is mathematics and statistics, bioinformatics applies these fields in ways that provide insight to make the vast, diverse, and complex life sciences data more understandable and useful, to uncover new biological insights, and to provide new perspectives to discern unifying principles. In short, bioinformaticists bring a multidisciplinary perspective to many of the critical problems facing the health-science profession today. What is Bioinformatics: Definition

Slide 9: 

Bioinformatics definition - Website / other sources "Biologists using computers, or the other way around. Bioinformatics is more of a tool than a discipline. (source: An Understandable Definition of Bioinformatics , The O'Reilly Bioinformatics Technology Conference, 2003) (4) The application of computer technology to the management of biological information. Specifically, it is the science of developing computer databases and algorithms to facilitate and expedite biological research.(source: Webopedia) Bioinformatics: a combination of Computer Science, Information Technology and Genetics to determine and analyze genetic information. (Definition from BitsJournal.com) Bioinformatics is the application of computer technology to the management and analysis of biological data. The result is that computers are being used to gather, store, analyse and merge biological data.(EBI - 2can resource) What is Bioinformatics: Definition

Slide 10: 

Bioinformatics definition - Website / other sources Even though the three terms: bioinformatics , computational biology and bioinformation infrastructure are often times used interchangeably, broadly, the three may be defined as follows: bioinformatics refers to database-like activities, involving persistent sets of data that are maintained in a consistent state over essentially indefinite periods of time; computational biology encompasses the use of algorithmic tools to facilitate biological analyses; while bioinformation infrastructure comprises the entire collective of information management systems, analysis tools and communication networks supporting biology.Thus, the latter may be viewed as a computational scaffold of the former two. Bioinformatics is currently defined as the study of information content and information flow in biological systems and processes. It has evolved to serve as the bridge between observations (data) in diverse biologically-related disciplines and the derivations of understanding (information) about how the systems or processes function, and subsequently the application (knowledge). A more pragmatic definition in the case of diseases is the understanding of dysfunction (diagnostics) and the subsequent applications of the knowledge for therapeutics and prognosis. What is Bioinformatics: Definition

Slide 11: 

Evolution of Bioinformatics From collection of computational tools to a full-fledged scientific discipline Voluminous collection of genomic sequence data Assembly, Storage, Analysis and Annotation Knowledge discovery

Slide 12: 

Bioinformatics has various applications in research in medicine, biotechnology, agriculture etc. Following research fields has integral component of Bioinformatics Computational Biology: The development and application of data-analytical andtheoretical methods, mathematical modeling and computational simulation techniquesto the study of biological, behavioral, and social systems. Genomics: Genomics is any attempt to analyze or compare the entire genetic complement of a species or species (plural). It is, of course possible to compare genomes by comparing more-or-less representative subsets of genes within genomes. Research Fields Related to Bioinformatics

Slide 13: 

Proteomics: Proteomics is the study of proteins - their location, structure and function. It is the identification, characterization and quantification of all proteins involved in a particular pathway, organelle, cell, tissue, organ or organism that can be studied in concert to provide accurate and comprehensive data about that system. Proteomics is the study of the function of all expressed proteins. The study of the proteome, called proteomics, now evokes not only all the proteins in any given cell, but also the set of all protein isoforms and modifications, the interactions between them, the structural description of proteins and their higher-order complexes, and for that matter almost everything 'post-genomic'." Research Fields Related to Bioinformatics

Slide 14: 

Pharmacogenomics : Pharmacogenomics is the application of genomic approaches and technologies to the identification of drug targets. In Short, pharmacogenomics is using genetic information to predict whether a drug will help make a patient well or sick. It Studies how genes influence the response of humans to drugs, from the population to the molecular level. Pharmacogenetics: Pharmacogenetics is the study of how the actions of and reactions to drugs vary with the patient's genes. All individuals respond differently to drug treatments; some positively, others with little obvious change in their conditions and yet others with side effects or allergic reactions. Much of this variation is known to have a genetic basis. Pharmacogenetics is a subset of pharmacogenomics which uses genomic/bioinformatic methods to identify genomic correlates, for example Research Fields Related to Bioinformatics

Slide 15: 

SNPs (Single Nucleotide Polymorphisms), characteristic of particular patient response profiles and use those markers to inform the administration and development of therapies. Strikingly such approaches have been used to "resurrect" drugs thought previously to be ineffective, but subsequently found to work with in subset of patients or in optimizing the doses of chemotherapy for particular patients. Cheminformatics: 'The mixing of information technology and information management resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the arena of drug lead identification and optimization.' (Frank K Brown 'Chemoinformatics: what is it and how does it impact drug discovery.' Ann. Rep. Med. Chem. 1998, 33 , 375-384.) (6) Research Fields Related to Bioinformatics

Slide 16: 

Related terms of cheminformatics are chemi-informatics, chemometrics, computational chemistry, chemical informatics, chemical information management/science, and cheminformatics. But we can distinguish chemoinformatics and chemical informatics as follows: Chemical informatics : 'Computer-assisted storage, retrieval and analysis of chemical information, from data to chemical knowledge.' ( Chem. Inf. Lett. 2003, 6 , 14.) This definition is distinct from ' Chemoinformatics ' (and the synonymous cheminformatics and chemiinformatics) which focus on drug design. Chemometrics: The application of statistics to the analysis of chemical data (from organic, analytical or medicinal chemistry) and design of chemical experiments and simulations. [IUPAC Computational] Research Fields Related to Bioinformatics

Slide 17: 

computational chemistry : A discipline using mathematical methods for the calculation of molecular properties or for the simulation of molecular behavior.  It also includes, e.g., synthesis planning, database searching, combinatorial library manipulation (Hopfinger, 1981; Ugi et al., 1990). [IUPAC Computational]  Structural genomics or structural bioinformatics refers to the analysis of macromolecular structure particularly proteins , using computational tools and theoretical frameworks. One of the goals of structural genomics is the extension of idea of genomics , to obtain accurate three-dimensional structural models for all known protein families, protein domains or protein folds . Comparative genomics: The study of human genetics by comparisons with model organisms such as mice, the fruit fly, and the bacterium E. coli . Research Fields Related to Bioinformatics

Slide 18: 

Biophysics:The British Biophysical Society defines biophysics as: "an interdisciplinary field which applies techniques from the physical sciences to understanding biological structure and function". Biomedical informatics / Medical informatics: "Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information." Mathematical Biology: Mathematical biology also tackles biological problems, but the methods it uses to tackle them need not be numerical and need not be implemented in software or hardware. It includes things of theoretical interest which are not necessarily algorithmic, not necessarily molecular in nature, and are not necessarily useful in analyzing collected data. Research Fields Related to Bioinformatics

Slide 19: 

Computational chemistry: Computational chemistry is the branch of theoretical chemistry whose major goals are to create efficient computer programs that calculate the properties of molecules (such as total energy, dipole moment, vibrational frequencies) and to apply these programs to concrete chemical objects. It is also sometimes used to cover the areas of overlap between computer science and chemistry. Functional genomics: Functional genomics is a field of molecular biology that is attempting to make use of the vast wealth of data produced by genome sequencing projects to describe genome function. Functional genomics uses high-throuput techniques like DNA microarrays, proteomics, metabolomics and mutation analysis to describe the function and interactions of genes. Research Fields Related to Bioinformatics

Slide 20: 

Pharmacoinformatics: Pharmacoinformatics concentrates on the aspects of bioinformatics dealing with drug discovery In silico ADME-Tox Prediction:(absorption, distribution, metabolism, and excretion, Agroinformatics / Agricultural informatics: Agroinformatics concentrates on the aspects of bioinformatics dealing with plant genomes. Systems biology: Systems biology is the coordinated study of biological systems by investigating the components of cellular networks and their interactions,by applying exprerimental high-throughput and whole-genome techniques, and integrating computational methods with experiemntal efforts. Research Fields Related to Bioinformatics

Slide 21: 

W W Components of Bioinformatics Edxperiments/Data Database-b/g/s/i/str Algorithms Analytical tools Simulation Visualisation Server based and Client based Data Mining

Slide 22: 

What is a “DRUG”

Slide 23: 

What is a “DRUG” & how it works? A chemical substance used in the diagnosis, treatment, or prevention of a disease or as a component of a medication. Any chemical agent that affects the function of living things. Some, including antibiotics, stimulants, tranquilizers, antidepressants, analgesics, narcotics, and hormones, have generalized effects. Others, including laxatives, heart stimulants, anticoagulants, diuretics, and antihistamines, act on specific systems. Vaccines are sometimes considered drugs. Drugs may protect against attacking organisms (by killing them, stopping them from reproducing, or blocking their effects on the host), substitute for a missing or defective substance in the body, or interrupt an abnormal process. A drug must bind with receptors in or on cells and cannot work if the receptors are absent or its configuration does not fit theirs. Drugs may be given by mouth, by injection, by inhalation, rectally, or through the skin. The oldest existing catalogue of drugs is a stone tablet from ancient Babylonia (c. 1700 BC); the modern drug era began when antibiotics were discovered in 1928. Synthetic versions of natural drugs led to design of drugs based on chemical structure. Drugs must be not only effective but safe; side effects can range from minor to dangerous state of poisoning. Many illegal drugs also have medical uses.

The NDD Scope : 

The NDD Scope The area of new drug development has now become highly modern biology and information intensive The area is heading towards an unimaginable revolution in future, and the days are not too far when whole process of gene-to-drug discovery shall be totally automated and industrialised Personalised medicines shall be the drugs of future

Slide 25: 

DD is a process used in the biopharmaceutical industry to discover and development new drug compounds. RDD uses a variety of computational methods to identify novel compounds design compounds for selectivity, efficacy and safety, and develop compounds into clinical trial candidates. These methods fall into several natural categories- structure based drug design, ligand based design de novo design and homology modeling-depending on how much information is available about Drug Targets and potential drug compounds. TYPES OF DRUG DESIGNING RATIONAL DRUG DESIGNING-

Slide 26: 

Structure-based drug design is one of several methods in the rational drug design toolbox. Drug targets are typically key molecules involved in a specific metabolic or cell signaling pathway that is known, or believed, to be related to a particular disease state. Drug targets are most often proteins and enzymes in these pathways. Drug compounds are designed to inhibit, restore or otherwise modify the structure and behavior of disease-related proteins and enzymes. SBDD uses the known 3D geometrical shape or structure of proteins to assist in the development of new drug compounds. TYPES OF DRUG DESIGNING Structure-Based Drug Design (SBDD)-

Slide 27: 

Rational Drug Discovery Definition Types of Drug Discovery Searches Rational – Reason-based Not based on chance alone May not involve computers Structure – based Drug Design (structure of the receptor, binding site, AA residues, thermodynamics) Pharmacophore – based Drug Design (structure of the ligand, SAR, QSAR) Mechanism – based Drug Design (molecular mechanism of action, transition state)

Slide 28: 

Computer-Aided Drug Design (CADD is a specialized discipline that uses computational methods to simulate drug-receptor interactions. CADD methods are heavily dependent on bioinformatics tools, applications and databases. As such, there is considerable overlap in CADD research and bioinformatics. TYPES OF DRUG DESIGNING 3 Computer-Aided Drug Design (CADD): Drug discovery and development can broadly follow two different paradigms—physiology-based drug discovery and target-based discovery. The main difference between these two paradigms lies in the time point at which the drug target is actually identified.

Slide 29: 

Conventional approach in new drug discovery NATURAL PRODUCTS OF MEDICINAL USE RANDOM CHEMICAL LIBRARY in-vivo Screening LEAD MOLECULE Lead Optimisation CANDIDATE DRUG MOLECULE Clinical Studies NEW DRUG WITH PROVEN EFFICACY & SAFETY Biochemical & Physiological Studies DRUG TARGET IDENTI- FICATION Pharmacokinetic; Pharmaceutics Studies DRUG DELIVERY MODEL

Bioinformatics Driven Drug Discovery : 

Bioinformatics Driven Drug Discovery Genome sequence Validated Therapeutic targets Structure determination 3D protein structures Ligand binding sites Structure-based ligand design NEW DRUG LEADS PROTEIN SEQUENCE DATABASE INITIAL PROTEIN INDEX (IPI) Homology modeling Site analysis Structural motif determination GENE SEQUENCE DATABASE INITIAL GENE INDEX (IGI) Intron-exon boundry definition Single nucleotide polymorphisms Sequence motif searching RNA splicing

Comparative GenomicsWhat one should look for? : 

Comparative GenomicsWhat one should look for? Human P.falciparum Mosquito Proteins that are shared by – All genomes Exclusively by & Mosquito Exclusively by P.f. & Mosquito Unique proteins in – Human P.f. Targets for anti-malarial drugs Mosquito

Slide 32: 

Full Toolbox Required to Compete Successfully Rational Drug Discovery

Slide 33: 

Drug Discovery Traditional Screening of natural compounds for biological activity. Isolation and purification. Determination of structure Structure-Activity Relationship (SAR). Synthesis of analogs. Receptor Theories. Design and Synthesis of novel drug structures Rational Molecular generation using crystal data or by modeling techniques. Strictly structural and Mechanism based approaches using computational and experimental techniques Deriving the bioactive conformer by conformational search. Superposition and alignment. Deriving the pharmacophoric pattern. Receptor mapping. Studying the ligand-receptor interactions by docking

Slide 34: 

Receptor-based Drug Design Design of Antithrombin Activators

Slide 35: 

Receptor-based Drug Design Computerized Modeling

Slide 36: 

Receptor-based Drug Design Design of New Structures

Slide 37: 

Pharmacophore-based Drug Design Natural Product Derivatization and Pharmacophore Elucidation

Slide 38: 

Z.Phe[CH(OH)CH2N]Pro.OtBu 6,500 Inhibitor Structure HIV-1 HIV-2 IC50 (nM) Z.Asn.Phe[CH(OH)CH2N]Pro.OtBu 140 330 Z.Asn.Phe[CH(OH)CH2N]Pro.OtBu 300 Mechanism-based Drug Design

Slide 39: 

Since the 1990s, dozens of rational drug design packages have been published.  These programs fall in one of three main genres:  1. Scanners, 2. Builders, or 3. Hybrids Scanners: All database search programs fall into this category.   Scanner type programs are more or less used for lead compound screening Builders Although builder-type programs may be used for denovo ligand design, their chief utility is in the optimization of lead compounds.  These programs also utilize a database of structures; however, the database contains fragments and chemical building blocks instead of complete compounds. Rational drug design software - State of the art

Rational Drug Design : 

Rational Drug Design Some Examples Using the 3D Shape of Proteins to Design Drugs that Inhibit Protein Function

Slide 41: 

Hormones Insulin binds to receptors on cell membranes signalling cells to take up glucose from the blood Protein ChannelsRegulate movement of substances across the plasma membrane. E.g. The CFTR protein pumps ions across membranes Source: http://www.biology.arizona.edu/biochemistry/tutorials/chemistry/page2.html http://www.cbp.pitt.edu/bradbury/projects.htm http://www.abc.net.au/cgi-bin/common/printfriendly.pl?/science/news/enviro/EnviroRepublish_1191825.htm http://www.umass.edu/microbio/chime/ Transport Haemoglobin (far right) in red blood cells transports oxygen to cells around the body Examples of Protein Function

Catalase - enzyme power! : 

Catalase - enzyme power! Hydrogen peroxide, a natural product of metabolism in your cells, is highly toxic in high concentrations and must be removed quickly! Source: http://accad.osu.edu/~ibutterf/ibp/molecule/ http://folding.stanford.edu/education/water.htm http://www.opti-balance.com/hyperox.htm Add ferric ions (Fe 3+) Rate increases 30 000-fold Add Catalase Rate increases 100 000 000-fold Reactants 2 2 Products Hydrogen peroxide oxygen water Location of active site where Hydrogen peroxide binds

How enzymes do it! : 

How enzymes do it! Enzyme proteins have specific sites where all the action happens. We call this the active site. Molecules that need to be ripped apart or put together enter the active site. Each protein has a specific shape so it will only perform a specific job. http://chsweb.lr.k12.nj.us/mstanley/outlines/enzymesap/Enzymesap.html http://academic.brooklyn.cuny.edu/biology/bio4fv/page/active_.html Joining things together Ripping things apart

Many toxins are proteins : 

Many toxins are proteins Source: http://www.wiley.com/legacy/college/boyer/0470003790/cutting_edge/molecular_recognition/molecular_recognition.htm http://science-univers.qc.ca/image/ricin061.jpg http://www.staabstudios.com/Spider.htm Funnel web spider toxin: blocks movement of calcium ions. Ricin from the seeds of the castor oil plant destroys ribosomes

Protein molecules are polymers : 

Protein molecules are polymers Proteins are very large polymer molecules. Polymers are made by linking smaller molecules, monomers, together to make a long chain. In the case of proteins, the monomers are amino acids. There are 20 different amino acids. AA AA AA AA AA AA AA

Why is protein structure important? : 

Why is protein structure important? Each protein molecule has a characteristic 3D shape that results from coiling and folding of the polymer chain. The function of a protein depends upon the shape of the molecule.

Protein chains : 

Protein chains Each protein has a specific sequence of amino acids that are linked together, forming a polypeptide http://www.mywiseowl.com/articles/Image:Protein-primary-structure.png

The protein chain folds : 

The protein chain folds Interactions between amino acids in the chain form: alpha helices beta sheets Random coils Source: http://www.rothamsted.bbsrc.ac.uk/notebook/courses/guide/prot.htm#I Together usually form the binding and active sites of proteins

And folds again! : 

And folds again! After folding, amino acids that were distant can become close Now the protein chain has a 3D shape that is required for it to function correctly Source: io.uwinnipeg.ca/~simmons/ cm1503/proteins.htm

The final protein… : 

The final protein… The final protein may be made up of more than one polypeptide chain. The polypeptide chains may be the same type or different types. Source: http://fig.cox.miami.edu/~cmallery/150/chemistry/hemoglobin.jpg

Designing a Drug to Block Amylase Action : 

Designing a Drug to Block Amylase Action Amylase is a protein that cuts small maltose sugar molecules off starch molecules. Another enzyme, maltase, is responsible for breaking down the maltose molecules into two simple sugars known as glucose. Glucose is absorbed into the blood and transported to cells around the body to provide them with energy. STARCH MALTOSE STARCH AMYLASE MALTASE GLUCOSE GLUCOSE

Block the active site of amylase : 

Block the active site of amylase Pig Active Site

Influenza Pandemics : 

Influenza Pandemics The Hong Kong Flu in 1968 evolved into H3N2. 750,000 people died of the virus worldwide The Spanish Flu in 1918, killed approximately 50 million people. It was caused by the H1N1 strain of influenza A. The Asian Flu in 1957 was the H2N2 influenza A strain. Worldwide it is estimated that at least one million people died from this virus.

Influenza epidemics : 

Influenza epidemics Economic Effects: Days away from work Providing medical advise and treatment Mortalities Figure 1. Weekly number of influenza and pneumonia deaths per 10 000 000 population in the United States, France, and Australia (black line).

Slide 55: 

There are two types of protein = N and H. N and H have special shapes to perform specific jobs for the virus. Influenza viruses are named according to the proteins sticking out of their virus coat. (H) (N) Designing a Flu Drug Step 1: looking for protein targets

Slide 56: 

Human Lung Cell Virus Proteins on cell surface H attaches to cell surface proteins so virus can enter cell Virus genes are released into the cell. The lung cell is ‘tricked’ into using these genes to make new virus particles. N cuts the links between the viruses and the cell surface so virus particles are free to go and infect more cells.

Your home work…Explore the research of an Australian team of scientists headed by Prof Peter Coleman. They designed the flu drug, Relenza. : 

Your home work…Explore the research of an Australian team of scientists headed by Prof Peter Coleman. They designed the flu drug, Relenza. Source: http://www.vnn.vn/dataimages/original/images126851_relenza.jpg http://www.omedon.co.uk/influenza/beans/relenza%20binding.jpg

Blocking the active site : 

Blocking the active site RELENZA

Venoms to drugs : 

Venoms to drugs Source: http://www.unimelb.edu.au/ExtRels/Media/02media/02july08.html A team of scientists from Melbourne University have patented a toxic chemical from the venom of an Australian Cone Shell. The chemical, called ACV1, is an analgesic that will help relieve chronic pain. It is more powerful than morphine and is not addictive. This analgesic will be used to treat pain resulting from nerve injury, post-surgical pain, “phantom limb” pain in amputees, leg ulcers in diabetics or the pain of terminal AIDS or cancer. ACV1 treats pain by blocking the transmission of pain along our peripheral nervous system This drug could generate an annual profit of greater than1 billion dollars to the company that develops it!

Some facts… : 

Some facts… Calcium, sodium and potassium ions control essential functions inside cells: calcium, for example, helps regulate the contraction of muscle cells. Ion channels control the entry and exit of ions into and out of cells. Some conotoxins act as analgesics, interacting with ion channel receptors in nerves so the ion channel cannot open. Blocking ion channels stops ions from entering a neighbouring nerve fibre. No electrical impulse is set off so the ‘pain’ message is switched off! Phew!

The nerve impulse : 

The nerve impulse + + + + - - - - Synaptic Junction Sodium ion Calcium ion Acetylcholine Ca2+ Na+ 4. Acetylcholine binds with receptor proteins changing the shape of the ion channel. 5. This opens the sodium ion channel to let in sodium. 6. Sodium ions set off an electrical impulse along the next nerve cell. 7. The pain message is working. To block pain we can try to target the ion channels. 1. Electrical impulse generated along axon – sodium ions (red) rush in and Potassium ions (green) rush out 2. Sodium ions accumulate causing Calcium ion channels to open. 3. Influx of Calcium causes acetylcholine to be released into synaptic junction.

Acetylcholine at work : 

Acetylcholine at work 2 Acetylcholine molecules bind to Receptor binding protein on an ion channel. The shape of the ion channel protein changes so the Na+ gate opens. Ions move into the neuron setting off an impulse. The message is passed on! Below is an image of a section of a nerve cell cut open to reveal one of the Sodium Ion channels that studs its surface. Let’s slice through an ion channel to show its inner workings.. Inside Cell Outside Cell

Na+ ion channel : 

Na+ ion channel Cell membrane (Phospholipid bylayer) Inside neuronal cell Outside neuronal cell You will explore this part of the ion channel. This is the section that binds acetylcholine &/or drug molecules causing the ion channel to change its shape. Some conotoxins block acetylcholine (nACh) receptors that stud the surface of neurons.

Expected gains out of use of in silico approaches : 

Expected gains out of use of in silico approaches Reduction in cost of development by at least US$ 300 million per medicine Reduction in development time by at least two years

Total time required for new drug development : 

Total time required for new drug development Lead identification – to candidate drug: Conventional approaches : 6 - 8 years Modern approaches : 3 – 5 years Clinical trials and regulatory : 5 – 8 years clearances (for both the approaches)