Introducción a la Bioinformática2002
Universidad Nacional San Cristobal de Huamanga, Ayacucho
Mirko Zimic
Tópicos de interés en la bioinformática
• Análisis de secuencias
• Filogenia y evolución molecular
• Modelamiento molecular
• Plegamiento de Proteínas
• Genómica y Proteómica
• Genética estadística
• Microarreglos
• Programación científica
Pongamos un ejemplo …
Cisteíno proteasa de la fasciola hepática: En busca de un péptido
inmunogénico
VPKSVDWREKGYVTPVKNQGQCGSCWAFSATGALEGQMFRKTGR ISLSEQNLVDCSRPQGNAVPDKIDWRESGYVTEVKDQGNCGSCWAFSTTGTMEGQYM KNERTSISFSEQQLVDCSRPWGN
_____ROJO_________
QGCNGGLMDNAFQYIKENGGLDSEESYPYEATDTSCNY KPEYSVANDTGFVDIPQREKA LMKNGCGGGLMENAYQYLKQF GLETESSYPYTAVGGQCRYNKQLG VAKVTGYYTV QSGSEVEL KN _VIOLETA____ _AMARILLO_______
AVATVGPISVAIDAGHSFQFYKSGIYYEPDCSSKDLDHGVLVVGYGFEG TDSNNNKYW IVKNSWLIGSEGPSAVAVDVESDFMMYRSGIYQSQTCSPLRVNHAVLAVGYGTQGGTD YW IVKNSW_____ _VERDE_____
GPEWGM-GYVKMAKDRNNH CGIATAASYPTVGLSWGERGYIRMV RNRGNMCGIASLASLPMVARFP
Alineamiento: cisteíno proteasas de mamífero Vs. cisteíno
proteasa de Fasciola hepatica.
AA Idénticos AA divergentes
Epítope Discontinuo, formado por porciones distantes de la secuencia.
Denaturación
El epítope se pierde con la denaturación.
Denaturación
El epítope se conserva como tal.
Epítope Continuo, formado por una porción de la secuencia
Modelaje tridimensional por homología. Identidad de secuencia de 56% con quimopapaína (1YAL)
AA idénticos AA divergentes
Análisis de Superficie: vista de átomos por radio de van der Waals
TMEGQYMKNERTSISFS
YYTVQSGSEVELKNLIGSE
QSQTCSPLRVN
RYNKQLGVAKV
Selección de secuencias (1)divergentes, (2)accesibles al solvente y (3)contínuas.
Evaluación de la estabilidad conformacional de los péptidos por minimización de energía.
H2O “backbone”
TMEGQYMKNERTSISFS YYTVQSGSEVELKNLIGSE
Pongamos otro ejemplo…
Sensibilidad de la aspartyl proteasa del HIV-1 a los inhibidores más
frecuentes
Representación en “cartoon” de la enzima proteasa de HIV-1
MONOMERO PROTEASA HIV
Enzima proteasa de HIV-1 mostrando los elementos de estructura secundaria, flaps y
sitio activo
Enzima proteasa de HIV-1 indicando los residuos consenso de unión inhibidor-enzima
INDINAVIR
RITONAVIR
Asociación de indinavir a la proteasa de HIV-1
Proteasa de HIV-1 mutante modelada en complejo con
Ritonavir
COMPARACION ENTRE UNA ENZIMA SENSIBLE Y UNA
RESISTENTE A RITONAVIR
Un ejemplo más…
Ordenamiento filogenético y el contenido de GC en tripanosomátidos
Reported %GC variation for each codon position in Trypanosomatids
(Alonso et al,1992)
4 2 4 4 4 6 4 8 5 0 5 2 5 4 5 6 5 8 6 04 0
4 5
5 0
5 5
6 0
6 5
7 0
7 5
8 0
8 5
9 0
C r i t h i d i aL e i s h m a n i a
T . c r u z iT . b r u c e i
1 s t2 n d3 r d
% G Cc o d o np o s i t i o n
% G C t o t a l D N A
Codon usage in Trypanosomatids leucine
0
10
20
30
40
50
60
70
TTA
CTA
TTG
CTT
CTC
CTG
TTA
CTA
TTG
CTT
CTC
CTG
TTA
CTA
TTG
CTT
CTC
CTG
TTA
CTA
TTG
CTT
CTC
CTG
T.brucei T.cruzi Leishmania Critidia
Codon usage in Trypanosomatids serine
0
5
10
15
20
25
30
35
40
AG
T
TCA
TCT
TCC
AG
C
TCG
AG
T
TCA
TCT
TCC
AG
C
TCG
AG
T
TCA
TCT
TCC
AG
C
TCG
AG
T
TCA
TCT
TCC
AG
C
TCG
T.brucei T.cruzi Leishmania Critidia
Phylogeny of Trypanosomatid lineage (Maslov & Simpson)
“Hole” formation by DNA replication
GC content variation in timeRestriction: AA family conservation
and AA conservation
%GC variation in Trypanosomatid lineage(Nuclear coding DNA)
GC variation in trypanosomatidae lineage Nuclear DNA
0.00
0.100.20
0.300.40
0.50
0.600.70
0.800.90
1.00
T.b
ruce
i
T.c
ruzi
Le
ishm
ani
a
Cri
thid
ia
% G
C
P1
P2
P3
P3*
P
I. Proyecto Genoma Humano
La secuencia del genoma está casi completa!– aproximadamente 3.5 billones de pares de bases.
All the Genes
• Any human gene can now be found in the genome by similarity searching with over 90% certainty.
• However, the sequence still has many gaps– one is unlikely to find a complete and
uninterrupted genomic segment for any gene – still can’t identify pseudogenes with certainty
• This will improve as more sequence data accumulates
Raw Genome Data:
The next step is obviously to locate all of the genes and describe their functions. This will probably take another 15-20 years!
– so why are there 60,000 human genes on Affymetrix GeneChips?
– Why does GenBank have 49,000 gene coding sequence and UniGene have 89,000 clusters of unique ESTs?
• Clearly we are in desperate need of a theoretical framework to go with all of this data
…Algunos años atrás…Celera sostenía que sólo
habrían 30,000 genes
Implications for Biomedicine
• Physicians will use genetic information to diagnose and treat disease.– Virtually all medical conditions (other than
trauma) have a genetic component.
• Faster drug development research– Individualized drugs– Gene therapy
• All Biologists will use gene sequence information in their daily work
II. Bioinformatics Challenges
Lots of new sequences being added- automated sequencers
- Human Genome Project
- EST sequencing
GenBank has over 10 Billion bases and is doubling every year!!
(problem of exponential growth...)
How can computers keep up?
The huge dataset
New Types of Biological Data
• Microarrays - gene expression
• Multi-level maps: genetic, physical, sequence, annotation
• Networks of Protein-protein interactions
• Cross-species relationships– Homologous genes– Chromosome organization
Similarity Searching the Databanks
What is similar to my sequence?
Searching gets harder as the databases get bigger - and quality degrades
Tools: BLAST and FASTA = time saving heuristics (approximate)
Statistics + informed judgement of the biologist
Alignment Alignment is the basis for finding similarity Pairwise alignment = dynamic
programming Multiple alignment: protein families and
functional domains Multiple alignment is "impossible" for lots
of sequences Another heuristic - progressive pairwise
alignment
Sample Multiple Alignment
Structure- Function Relationships Can we predict the function of protein
molecules from their sequence?
sequence > structure > function Conserved functional domains = motifs
Prediction of some simple 3-D structures (-helix, -sheet, membrane spanning, etc.)
Protein domains
DNA Sequencing Automated sequencers > 40 KB per day 500 bp reads must be assembled into
complete genes- errors especially insertions and deletions
- error rate is highest at the ends where we want to overlap the reads
- vector sequences must be removed from ends
Faster sequencing relies on better software
overlapping deletions vs. shotgun approaches: TIGR
Finding Genes in genome Sequence is
Not Easy • About 2% of human DNA encodes
functional genes.
• Genes are interspersed among long stretches of non-coding DNA.
• Repeats, pseudo-genes, and introns confound matters
Pattern Finding Tools• It is possible to use DNA sequence patterns
to predict genes:• promoters• translational start and stop codes (ORFs)• intron splice sites• codon bias
• Can also use similarity to known genes/ESTs
Phylogenetics Evolution = mutation of DNA (and
protein) sequences
Can we define evolutionary relationships between organisms by comparing DNA sequences- is there one molecular clock?- phenetic vs. cladisitic approaches- lots of methods and software, what is the
"correct" analysis?
II. El papel del Biólogo en la Era de la
Información
El Internet provee abundante información biologica
Puede resultar abrumador…
- Web
Necesidad de nuevas habilidades = localizar información necesaria de manera eficiente
Computing in the lab - everyday tasks (vs. computational biology)
ordering supplies reference books lab notes literature
searching
Training "computer" scientists
Know the right tool for the job
Get the job done with tools available
Network connection is the lifeline of the scientist
Jobs change, computers change, projects change, scientists need to be adaptable
The job of the biologist is changing
• As more biological information becomes available …– The biologist will spend more time using
computers– The biologist will spend more time on data
analysis (and less doing lab biochemistry)– Biology will become a more quantitative science
(think how the periodic table and atomic theory affected chemistry)
III. Molecular Biology Software Tools
GCG (Wisconsin Package) The most popular and most
comprehensive set of tools for the molecular biologist.
- Runs on mainframe computers: (UNIX)
- Web, X-Windows (SeqLab) interfaces
- Inexpensive for large numbers of users
- Requires local databases (on the mainframe computer)
- Allows for custom databases and programming
The Web Many of the best tools are free over the Web
BLAST ENTREZ/PUBMED Protein motifs databases
Bioinformatics “service providers” DoubleTwist™, Celera, BioNavigator™
Hodgepodge collection of other tools PCR primer design Pairwise and Multiple Alignment
Personal Computer Programs Macintosh and Windows applications
- Commercial: Vector NTI™, MacVector™, OMIGA™, Sequencher™
- Freeware: Phylip, Fasta, Clustal, etc.
Better graphics, easier to use Can't access very large databases or perform
demanding calculations Integration with web databases and computing
services
Putting it all together The current state of the art requires the
biologist to jump around from Web to mainframe to personal computer
The trend is for integration
– Web + personal computer will replace text interface to mainframe ?
– Will the Web become the ultimate interface for all computing ??
IV. Genómica
Genomics Technologies
• Automated DNA sequencing• Automated annotation of sequences• DNA microarrays
– gene expression (measure RNA levels)– single nucleotide polymorphisms (SNPs)
• Protein chips (SELDI, etc.)• Protein-protein interactions
cDNA spotted microarrays
Affymetrix Gene Chips
Impact on Bioinformatics
• Genomics produces high-throughput, high-quality data, and bioinformatics provides the analysis and interpretation of these massive data sets.
• It is impossible to separate genomics laboratory technologies from the computational tools required for data analysis.
Pharmacogenomics • The use of DNA sequence information to
measure and predict the reaction of individuals to drugs.
• Personalized drugs
• Faster clinical trials– Selected trail populations
• Less drug side effects– toxicogenomics