Introducción a la Bioinformática 2002 Universidad Nacional San Cristobal de Huamanga, Ayacucho Mirko Zimic.

Presentación del tema: "Introducción a la Bioinformática 2002 Universidad Nacional San Cristobal de Huamanga, Ayacucho Mirko Zimic."— Transcripción de la presentación:

1 Introducción a la Bioinformática 2002 Universidad Nacional San Cristobal de Huamanga, Ayacucho Mirko Zimic

2 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

3 Pongamos un ejemplo … Cisteíno proteasa de la fasciola hepática: En busca de un péptido inmunogénico

4 Alineamiento: cisteíno proteasas de mamífero Vs. cisteíno proteasa de Fasciola hepatica. AA IdénticosAA divergentes

5 Epítope Discontinuo, formado por porciones distantes de la secuencia. Denaturación El epítope se pierde con la denaturación.

6 Denaturación El epítope se conserva como tal. Epítope Continuo, formado por una porción de la secuencia

7 Modelaje tridimensional por homología. Identidad de secuencia de 56% con quimopapaína (1YAL)

8 AA idénticosAA divergentes Análisis de Superficie: vista de átomos por radio de van der Waals

9 TMEGQYMKNERTSISFS YYTVQSGSEVELK NLIGSE QSQTCSPLRVN RYNKQLGVAKV Selección de secuencias (1)divergentes, (2)accesibles al solvente y (3)contínuas.

10 Evaluación de la estabilidad conformacional de los péptidos por minimización de energía. H2OH2O “backbone” TMEGQYMKNERTSISFSYYTVQSGSEVELKNLIGSE

11 Pongamos otro ejemplo… Sensibilidad de la aspartyl proteasa del HIV-1 a los inhibidores más frecuentes

12 Representación en “cartoon” de la enzima proteasa de HIV-1

13 MONOMERO PROTEASA HIV

14 Enzima proteasa de HIV-1 mostrando los elementos de estructura secundaria, flaps y sitio activo

15 Enzima proteasa de HIV-1 indicando los residuos consenso de unión inhibidor-enzima

16 INDINAVIR

17 RITONAVIR

18 Asociación de indinavir a la proteasa de HIV-1

19 P roteasa de HIV-1 mutante modelada en complejo con Ritonavir

20 COMPARACION ENTRE UNA ENZIMA SENSIBLE Y UNA RESISTENTE A RITONAVIR

21 Un ejemplo más… Ordenamiento filogenético y el contenido de GC en tripanosomátidos

22 Reported %GC variation for each codon position in Trypanosomatids (Alonso et al,1992)

23 Codon usage in Trypanosomatids leucine

24 Codon usage in Trypanosomatids serine

25 Phylogeny of Trypanosomatid lineage (Maslov & Simpson)

26 “Hole” formation by DNA replication

27 GC content variation in time Restriction: AA family conservation and AA conservation

28 %GC variation in Trypanosomatid lineage (Nuclear coding DNA)

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30 I. Proyecto Genoma Humano La secuencia del genoma está casi completa! – aproximadamente 3.5 billones de pares de bases.

31 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

32 Raw Genome Data:

33 The next step is obviously to locate all of the genes and describe their functions. This will probably take another 15-20 years!

34 –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

35 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

36 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

37 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

38 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

39 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

40 Sample Multiple Alignment

41 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.)

42 Protein domains

43 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

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45 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

46 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

47 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?

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49 II. El papel del Biólogo en la Era de la Información

50 El Internet provee abundante información biologica  Puede resultar abrumador… -e-mail - Web  Necesidad de nuevas habilidades = localizar información necesaria de manera eficiente

51 Computing in the lab - everyday tasks (vs. computational biology)  ordering supplies  reference books  lab notes  literature searching

52 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

53 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)

54 III. Molecular Biology Software Tools

55 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

56 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

57 Personal Computer Programs u Macintosh and Windows applications - Commercial: Vector NTI™, MacVector™, OMIGA™, Sequencher™ - Freeware: Phylip, Fasta, Clustal, etc. u Better graphics, easier to use u Can't access very large databases or perform demanding calculations u Integration with web databases and computing services

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59 Putting it all together u The current state of the art requires the biologist to jump around from Web to mainframe to personal computer u The trend is for integration –Web + personal computer will replace text interface to mainframe ? –Will the Web become the ultimate interface for all computing ??

60 IV. Genómica

61 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

62 cDNA spotted microarrays

63 Affymetrix Gene Chips

64 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.

65 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

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