bioc3010: bioinformatics - revision lecture dr. andrew c.r. martin martin@biochem.ucl.ac.uk

BIOC3010: Bioinformatics - Revision lecture

Dr. Andrew C.R. Martin

martin@biochem.ucl.ac.uk

http://www.bioinf.org.uk/

Data Creation

Analysis

Prediction

Presentation

Searching

OrganizingSequences

DNAProtein

ComputersStructures

Introductionary Lecture

Introduction

• helps you create data

• example of fragment assembly

Bioinformatics…

Introduction

• provides tools to store and search data

• databases and databanks• primary/secondary/composite/gateways

Bioinformatics…

Introduction

• allows you to make predictions

• prediction techniques– moving windows, – computer learning

Bioinformatics…

Introduction

• allows you to create 3D models

• separate lecture

Bioinformatics…

Introduction

• allows transfer of annotations

• homologous proteins likely to perform similar functions

Bioinformatics…

IntroductionAnnotations…

• Pre-genome world• Post-genome world

• Annotations will change

Genomes and Gene Prediction Lectures

Genome structure• C-paradox

• Compare prokaryotes and eukaryotes

• Complexity of eukaryotes:– Introns/exons,– Repeated sequences,– Transposable elements,– Pseudogenes

• Problems introduced by these...

ORF Scanning in Eukaryotes

exon intron exon5’ 3’

Intron/exonsplice sites

Finding Genes in Genomic DNA

Ab initio methods Similarity based methods

Integrated approaches

30 40

TRY4

Prediction accuracy

• Nucleotide level• Exon level

• Measures for assessment

Computing Lecture

Computers

Operating systems

• What is an operating system?• Examples of operating systems• Choice of operating systems for different areas of

research

Computers and computer science

• Data structures and information retrieval

– Relational databases– Design of databases to reduce errors in data

• Simple examples of SQL and structuring data into tables

Must handle:

Computers and computer science

• Algorithms: how to solve a problem

– Defined an algorithm– Looked at an example

Must handle:

Computers and computer science

• Data mining and machine learning

– Extract patterns, etc from data– Computer software which learns from examples and

is then able to make predictions

Must handle:

Comparative Modelling Lecture

What is comparative modelling?

• Build a three-dimensional (3D) model of a protein...

• …based on known structure of a (generally) homologous protein sequence

• "Homology Modelling" is misleading:– fold recognition and threading allow recognition of

non-homologous sequences which adopt the same fold

Stages in CM

1. Identify templates (or ‘parents’)

2. Align the target sequence with the parent(s),

3. Find:structurally conserved regionsstructurally variable regions

4. Inherit the SCRs from the parent(s)

5. Build the SVRs

6. Build the sidechains

7. Refine the model

8. Evaluate errors in the model

Correct alignment is the structural alignment.

Align target with parent(s)

Structure ofTarget

Optimal alignmentbased on

Structural Equivalents

Structure ofParent

We don’t have this!

Guess structural alignment

from sequence alignment

An example MLSA

Sequence alignment quality

Assessing the model

• Ideal is to compare the model with the true target structure - 4-6Å; 2Å; 0.5Å

NidRMS

2

Model quality

The main factors are:

The sequence identity with the primary parent The number and size of indels The quality of the alignment The amount of change which has been necessary to the

parent(s) to create the model.

Summary of CASP2results

CASP8 ransummer 2008

http://predictioncenter.gc.ucdavis.edu

Medical Applications Lecture

Mutations, Alleles & Polymorphisms

• Mutation:– any change in DNA sequence

• Allele:– alternative form of a genetic locus; one inherited from

each parent– e.g. eye colour locus - brown and blue alleles

• Polymorphism:– genetic variation present in >= 1% of a normal

population

How are SNPs useful?

• Understanding evolution– Some alleles may be advantageous in one

environment, but disadvantageous in another

• DNA fingerprinting• Markers to map traits

– diseases, characteristics

• Pharmacogenomics– genotype-specific medications

Drug responses

Drug efficacy may be affected by:

• transporters• metabolism• receptors• signalling pathways, etc.

Potentially lethal SNPs

First described ~2000 years ago

“What is food to some men may be fierce poison toothers”

Lucretius Caro

Protein Sequence

DNA Sequence

Protein Structure Protein Function

Mutation

Altered Sequence

Altered Structure

AlteredFunction

UnderstandStructure &

Function

Restore Structure

RestoredFunction

DesignDrugs

• Looked at p53...

• Local level - effects of mutations

• General classes– Functional– Fold Preventing– Destabilizing

Types of mutations

How human?

Chimeric: 67% human

Humanized: 90% human

Mouse: 0% human

Antibody Humanization

Summary

– Diagnosis of disease– Prediction of disease risk– Prognosis– Customized response to disease– Identifying drug targets - treatments– Engineering of proteins for therapy

Docking and Drug Design Lecture

Van der Waals forcesElectrostatic (Salt bridge) InteractionHydrogen bondsHydrophobic bonding

+ + -+ +

Surface complementarity

-+ + + ++

Six degrees of freedom- protein and ligand both treated as rigid- 3 rotations / 3 translations

Docking methods - rigid body

Just like docking the space shuttle with a satellite

Image from NASA

Treat receptor as static / ligand as flexible

Dock ligand into binding pocket- generate large number of possible orientations

Evaluate and select by energy function

Docking methods - flexible ligand

Ligand Matching

• Match sphere centres against ligand atoms• Find possible ligand orientations• Often >10,000 orientations possible

Find the transformation (rotation + translation) to maximize sphere matching

DOCK

Virtual Screening

• Docking can be used for virtual screening

• Scan a library of potential drug molecules• Identify leads

LUDI (InsightII) - find fragments that can bind

GRID - uses molecular mechanics potential to find interaction sites for probe groups

X-site - uses an empirical potential to find interaction sites for probe groups

De Novo Drug Design

Stupid mistakes...

• Don't confuse secondary databases with secondary structure!

• Ensure you know the difference between SCOP/PFam functional domains and CATH structural domains

Summary

• Find pockets• Principles for docking - complementarity• Docking

– rigid body / ligand flexibility

• Virtual screening• Identifying probe interaction sites

– build ligands de novo