Introduction toComputational Biology

BS123A/BME195/MB223

UC-Irvine

Ray Luo, MBB, BS

What to expect?

• This is an introduction to computational representations and algorithms for analysis of sequence, structure and function in molecular biology. It aims to give an understanding of the biological problems that arise and how algorithms are developed to address them.

Goals of this course

• Learn how to present molecular data using computer graphics

• Understand computational challenges in molecular biology

• Understand basic algorithms that establish context for rest of field

• Identify opportunities in this field, and perhaps formulate projects to explore further

How to reach these goals?

• Give you a feeling for main issues in computational molecular biology: sequence, structure, and function

• Give you exposure to classic computational problems as manifested in biology.

• Give you exposure to classic biological problems represented computationally.

Things that won’t be emphasized

• Won’t give you any balanced understanding of the experimental approaches used in molecular biology.

• Won’t force you to make a novel contribution to computational biology.

• Won’t ask you to write a computer program for any project, though you’re require to describe how a program/algorithm work in English.

Prerequisite

• Freshman calculus, phys, and chem completed or being taken concurrently.

• Willing to think quantitatively in biology.

• Willing to use computer in learning/working.

Course website

• http://rayl0.bio.uci.edu/teaching

• Lecture notes will be posted before the lectures if possible.

• Handouts will be passed around for additional information.

Contacts

• Office hours by appointment

• rluo@uci.edu

• 3206 Natural Sciences I

Lecture info

• Meeting place: NSI 2144

• Meeting times: Tue/Thur 9:00-10:30am

Grading

o 50% based on weekly homework/projecto 25% by in-class finalo 25% by take-home final

Grading: How to get an A?

• It is crucial to turn in weekly homework and projects to get a passing grade.

• The finals are 90% based on homework and projects so make sure you know each assigned problem well for the finals. You have to do the in-class final very fast.

Uses of Computation in Biology

• Ecology

• Physiology

• Cell Biology

• Molecular Biology

Careers in Computational Molecular Biology

• Pharmaceutical/Biotechnology Industry

• Universities and Colleges

Life in Industry

• Identification of potential drug targets, mostly enzymes, with molecular biologist and biochemists

• Discovery of lead compounds, with medicinal chemists, aka organic chemists

• Optimization of lead compounds, with medical chemists

• Prediction of drug-like properties, ADME-T (absorption, distribution, metabolism, excretion, toxicity )

Why the things you learn here are important?

Computer aided drug design

What is a drug?

• Defined composition with a pharmacological effect

• Regulated by the Food and Drug Administration (FDA)

• What is the process of Drug Discovery and Development?

Drugs and the Discovery Process

• Small Organic Molecules– Natural products

• fermentation broths • plant extracts • animal fluids (e.g., snake venoms)

– Synthetic Medicinal Chemicals• Project medicinal chemistry derived• Combinatorial chemistry derived

• Biological Molecules– Natural products (isolation)– Recombinant products

Discovery vs. Development

• Discovery includes: Concept, mechanism, assay, screening, hit identification, lead demonstration, lead optimization

• Discovery also includes In Vivo proof of concept in animals and demonstration of a therapeutic effect

• Development begins when the decision is made to put a molecule into phase I clinical trials

Discovery and Development

• The time from conception to approval of a new drug is typically 10-15 years

• The vast majority of molecules fail along the way• The estimated cost to bring to market a

successful drug is now $800 million!! (Dimasi, 2000)

• However, the annual profit of a drug can be $ 1 billion per year

• Pharmaceutical industry has been one of the best performing sections in economy

Drug Discovery Disciplines

• Medicine• Physiology/pathology• Pharmacology• Molecular/cellular biology• Automation/robotics• Medicinal, analytical,and combinatorial

chemistry• Structural and computational chemistries• Computational biology

Drug Discovery Program Rationales

• Unmet Medical Need

• Me Too! - Market - ($$$s)

• Drugs in search of indications– Side-effects often lead to new indications

• Indications in search of drugs– Mechanism based, hypothesis driven,

reductionism

Issues in Drug Discovery

• Hits and Leads - Is it a “Druggable” target?

• Resistance

• Delivery - oral and otherwise

• Metabolism

• Solubility, toxicity

• Patentability

• … …

A Little History of Computer Aided Drug Design

• 1960’s - Review target-drug interactions• 1980’s- Automation - high throughput target/drug selection• 1980’s- Databases (information technology) - combinatorial libraries• 1980’s- Fast computers - docking• 1990’s- Faster computers - genome assembly - genomic based target selection• 2000’s- Fast information handling - pharmacogenomics

From the Computer Perspective

Comparing Growth Rates

0

5

10

15

20

25

30

35

40

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Inc

rea

se

fa

cto

rProcessor performance growth

Memory bus speed growth

Pixel fill rate growth

From the Target Perspective

(a) myoglobin (b) hemoglobin (c) lysozyme (d) transfer RNA(e) antibodies (f) viruses (g) actin (h) the nucleosome (i) myosin (j) ribosome

Status - Numbers and Complexity

Courtesy of David Goodsell, TSRI

From the Drug Perspective

Combinatorial Libraries

Blaney and Martin - Curr. Op. In Chem. Biol. (1997) 1:54-59

• Thousands of variations to a fixed template• Good libraries span large areas of chemical and conformational space - molecular diversity• Diversity in - steric, electrostatic, hydrophobic interactions...• Desire to be as broad as “Merck” compounds from random screening• Computer aided library design is in its infancy

Computer-Assisted Drug Design

• Computer driven drug discovery• Data driven drug discovery

An overview of biomolecules

• Living organisms are more ordered than their surroundings.

• So the first task is to maintain a separation between inside and outside.

• The second task is to spend energy to keep things in order.

• The functions of life are to facilitate the acquisition and expenditure of energy.

Cell

• Cells are the smallest compartments that are ordered and separated from the surroundings.

• Note that ordered compartments were difficult to get started de novo, and so have found ways to pass on the apparatus necessary to perpetuate themselves.

Tasks of a living cell

• Gather energy from surroundings.• Use energy to maintain inside/outside

distinction.• Use extra energy to reproduce.• Develop strategies for being efficient at

their tasks: developing ways to move around; developing signaling capabilities; developing ways for energy capture; developing ways of reproduction.

Molecular means to realizethese tasks

• Ability to separate inside from outside with lipids

• Ability to build three-dimensional molecules that assist their functions, proteins, RNA

• Ability to store information for these tasks, part of reproduction also, DNA

A simple model of a cell

proteins Lipid membrane DNA

Lipids

• Made of hydrophilic (water loving) molecular fragment connected to hydrophobic fragment.

• Spontaneously form sheets (lipid bilayers, membranes) in which all the hydrophilic ends align on the outside, and hydrophobic ends align on the inside.

• Creates a very stable separation, not easy to pass through except for water and a few other small atoms/molecules.

Lipids

Lipid bilayers: Structure

Lipid bilayers: Structure

Lipid bilayers: Functions

Lipid bilayers

A simple model of a cell

proteins Lipid membrane DNA

Proteins: A chain of linked subunits

• These subunits are amino acids (also called protein residues for historical reasons).

• There are 20 different amino acids with different physical and chemical properties.

• The interaction of these properties allows a chain of the amino acids (upto 1000’s long) to fold into a unique, reproducible 3D shape.

20 amino acids

• Common back bone

• Unique side chain

Ala A Alanine

Glu E Glutamic Acid

Arg R Arginine

Amino acid structures

Fig. 5.3

Fig. 5.3

Amino acid properties

• Polar amino acids: THR, SER, ASN, GLN, TYR, HIS, TRP, CYS

• Charged amino acids: ASP, GLU, LYS, ARG, HIS, CYS

• Hydrophobic amino acids: VAL, LEU, ILE, PHE, ALA, PRO, GLY, MET, TYR, TRP

Representations of proteins

• 1-d sequence:

Alanine-Tyrosine-Valine=

ALA-TYR-VAL=

A-Y-V

Representations of proteins: 2-dTHH HHHHHTLLLH HHHHHGGGLS STTEEEEEEE

Representations of proteins: 3-d

Protein features

• Protein can be stabilized by salt bridges

• Protein can be folded to a unique structure due to the existence of disulfide bonds

• Protein may function as an enzyme whose active sites are crucial for its function

A simple model of a cell

proteins Lipid membrane DNA

DNA structures

DNA packs in the nucleus toform chromosome

DNA structure

DNA is a sequence too

• It has a common back bone, and side chains, though only 4 kinds.

• A sequence of these subunits is also specified as a string: ACTTAGGACATTTTAG, which is a simplified representation of a chemical structure.

DNA is a sequence too

• DNA uses an alphabet of 4 letters (ATCG), i.e. bases.

• Long sequences of these 4 letters are linked together to create genes and control information.

Information in DNA

• DNA encodes proteins: each amino acid can be specified by 3 bases. Ribosome reads a DNA sequence and creates the corresponding protein chain.

• GENETIC CODE: 64 mappings of 3 bases to 1 amino acid.

Genetic code

The gene for myoglobin• ctgcagataa ctaactaaag gagaacaaca acaatggttc tgtctgaagg• tgaatggcag ctggttctgc atgtttgggc taaagttgaa gctgacgtcg• ctggtcatgg tcaggacatc ttgattcgac tgttcaaatc tcatccggaa• actctggaaa aattcgatcg tttcaaacat ctgaaaactg aagctgaaat• gaaagcttct gaagatctga aaaaacatgg tgttaccgtg ttaactgccc• taggtgctat ccttaagaaa aaagggcatc atgaagctga gctcaaaccg• cttgcgcaat cgcatgctac taaacataag atcccgatca aatacctgga• attcatctct gaagcgatca tccatgttct gcattctaga catccaggta• acttcggtgc tgacgctcag ggtgctatga acaaagctct cgagctgttc• cgtaaagata tcgctgctaa ctgggttacc agggttaatg aggtacc

BASE COUNT 155 a 108 c 115 g 129 t

MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAILKKKGHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRHPGNFGADAQGAMNKALELFRKDIAAKYKELGYQG

Genes and control

• The set of all genes required for an organism is the organism’s GENOME.

• Human genome has 3,000,000,000 bases divided into 23 linear segments (chromosomes).

• A gene has on average 1340 DNA bases, thus specifying a protein of about 447 amino acids.

• Humans have about 35,000 genes = 40,000,000 DNA bases = 3% of total DNA in genome.

• Humans have another 2,960,000,000 bases for control information. (e.g. when, where, how long, etc...)

Genotype and phenotype

• Genotype—the genetic sequences associated with an individual organism.

• Phenotype—the observable non-sequence features of an individual organism (e.g. color, shape, activity of an enzyme)

How do we proceed?

In order to obtain insight into the ways in which genes and gene products function:

• Analyze DNA and protein sequences to search clues for structure, function and control – sequence analysis

• Analyze structures to search clues for sequences, function and control – structural analysis

• Understand how sequences and structures leads to functions – functional analysis

But what are functions of genes?

• Signal transduction: sensing a physical signal and turning into a chemical signal

• Structural support: creating the shape and of a cell or set of cells

• Enzymatic catalysis: accelerating chemical reactions otherwise too slow to be useful for living things

• Transport: getting things in and out of a compartment.

But what are functions of genes?

• Movement: contracting in order to pull things together or push things apart

• Transcription control: deciding when other genes should be turned on/off

• Trafficking: affecting where different elements end up inside a cell.

Evolution is the key

• Common descent of organisms implies that they will share many basic approaches

• Development of new phenotypes in response to environmental pressure can lead to specialized approaches

• More recent divergence implies more shared approaches between species

• The important thing is which is shared and which is not unshared. This is also important for drug discovery in biomedicine.

Seeing is believing:Computer Graphics

Je-2147/HIV Protease Complex

HIV Integrase

The Small Ribosomal Subunit

The Large Ribosomal Subunit