Multiple Alignments Motifs/Profiles What is multiple alignment? HOW does one do this? WHY does one do this? What do we mean by a motif or profile? BIO520 BioinformaticsJim Lund Prev. reading: Ch 1-5 Assigned reading: Ch 6.4, 6.5, 6.6

Information from Alignments Infer biological function –Conserved elements critical for function –Divergent elements relate to divergent function Infer structure (2°, 3°) Infer phylogeny –History –Evolutionary forces (selection…)

How do I find similar sequences?

Multiple Alignment Global, Optimal Theory Computation Progressive Alignment

Multiple Alignment: better alignments

Alignment Methods/Programs GAP (GCG suite) –Optimal Alignment MSA –(nearly) Optimal Alignment Clustal W/X –Progressive Alignment PSI-BLAST –Searches for matching sequences iteratively –Search seq is invariant master for the alignment.

MSA Strategy c(A)=  c(A i,j ) Minimize score! HUGE matrix(aa # of seqs)  CRASH computer –time~product of sequence length –1000x10,000 OK, but 200x200x200x200 NOT Alignment procedure –nearly optimal--only considers a subset of all alignment) –weight sequences via distance –branch-and-bound algorithm

Running MSA Download and run it locally (UNIX): – chaffer/genetic_analysis.htmlhttp:// chaffer/genetic_analysis.html On the internet: – align/multi-align.html Rerun on segments AFTER Clustal...

Clustal Strategy 1.Rapid pairwise alignments each-to-each 2.Calculate distance matrix –Create guide tree (neighbor joining) 3.Align –Closest pairs first –Add pairs or align sub-alignments –Adjust similarity matrix as alignment proceeds 4.Add sequences –introduce gaps gaps at loops, not inside known 2° structures Dynamic gap weighting

Clustal Strategy Pairwise alignments Guide tree Align

Clustal W(X) Strategy 1. Pairwise alignments The pairwise alignment number here is a dissimilarity measure.

Clustal W(X) Strategy 2. Unrooted neighbor tree (dendrogram)

Clustal W(X) Strategy 3. Guide tree

Clustal W(X) Strategy 4. Progressive alignment using guide tree

Running Clustal W/X WWW, Win, Mac, UNIX – Input –Multiple sequence file (PIR, FASTA,…) Can FORCE alignments Specify secondary structures Considerations –Fast, easy, widely used –Divergent proteins OK (trees misleading)

“The Right Proteins” GAPDH Rabbit KAENGKLVING-KAITIFQERDPANIKWGDAGAEYVVESTGVFTTMEKAGAHLKGGAKRV 117 Chick KAENGKLVING-HAITIFQERDPSNIKWADAGAEYVVESTGVFTTMEKAGAHLKGGAKRV 117 *********** :**********.:***.*******************************

“The Right Proteins” GAPDH Rabbit KAENGKLVING-KAITIFQERDPANIKWGDAGAEYVVESTGVFTTMEKAGAHLKGGAKRV 117 Chick KAENGKLVING-HAITIFQERDPSNIKWADAGAEYVVESTGVFTTMEKAGAHLKGGAKRV 117 Human KAEDGKLVIDG-KAITIFQERDPENIKWGDAGTAYVVESTGVFTTMEKAGAHLKGGAKRI 118 Tobacco KVKDEKTLLFGEKSVRVFGIRNPEEIPWAEAGADFVVESTGVFTDKDKAAAHLKGGAKKV 110 Entamoeba EAGENAIIVNGHKIV-VKAERDPAQIGWGALGVDYVVESTGVFTTIPKAEAHIKGGAKKV 105 :. : :: * : : :*:* :* *. *. :********* ** **:*****::

Alignment Interpretation DNA sequences –>50% “worth looking at” (eyeball test) –~75% needed for phylogeny Polypeptide sequences –80% similar=SAME tertiary structure –30-80% domains=similar structure –15-30% ???? –<15% short motifs

Uses of Alignment Understanding or predicting mutant function Finding motifs in DNA or polypeptides Directing experiments--e.g. PCR primers Phylogeny

“The Right Proteins” Rabbit KAENGKLVING-KAITIFQERDPANIKWGDAGAEYVVESTGVFTTMEKAGAHLKGGAKRV 117 Chick KAENGKLVING-HAITIFQERDPSNIKWADAGAEYVVESTGVFTTMEKAGAHLKGGAKRV 117 Human KAEDGKLVIDG-KAITIFQERDPENIKWGDAGTAYVVESTGVFTTMEKAGAHLKGGAKRI 118 Tobacco KVKDEKTLLFGEKSVRVFGIRNPEEIPWAEAGADFVVESTGVFTDKDKAAAHLKGGAKKV 110 Entamoeba EAGENAIIVNGHKIV-VKAERDPAQIGWGALGVDYVVESTGVFTTIPKAEAHIKGGAKKV 105 :. : :: * : : :*:* :* *. *. :********* ** **:*****::

Viewing and interpreting alignments Color residues by property Conservation in the alignment Known properties Substitution groups: STA, HY Physiochemical property charge hydrophobicity Programs for visualization Jalview AMAS Alscript

Viewing alignments JalView alignment viewer

How to build multiple alignments 1.Find sequences to align (db search). 2.Choose which regions of each protein to include. Sequences should be of similar lengths. 3.Run multiple alignment program. 4.Inspect multiple alignment for problems. Regions with many gaps have aligned poorly. 5.Remove disruptive sequences and re-run alignment. 6.Add back remaining sequences avoiding disruption.

Motifs vs Alignment Motifs are short conserved segments In proteins: –PROSITE (“signal sites”) –Interpro In DNA: –TFD Tools for finding motifs: –ProfileScan –MEME

Interpro Pfam 7.3 (3865 domains), PRINTS 33.0 (1650 fingerprints), PROSITE 17.5 (1565 and 252 preliminary profiles), ProDom (1346 domains), SMART 3.1 (509 domains), TIGRFAMs 1.2 (814 domains), SWISS-PROT ( entries), TrEMBL ( entries).

Interpro A database of protein families, domains and functional sites PROSITE, home of regular expressions and profiles; Pfam, SMART, TIGRFAMs, PIRSF, and SUPERFAMILY keepers of hidden Markov models(HMMs); PRINTS, provider of fingerprints (groups of aligned, un-weighted motifs);

Interpro

NCBI CDD (Conserved Domain Database Domains from: Pfam (Protein families) –A database of protein families that currently contains > 7973 entries. SMART ( a Simple Modular Architecture Research Tool) –More than 500 domain families found in signalling, extracellular and chromatin-associated proteins are detectable. –Domains are extensively annotated with respect to phyletic distributions, functional class, tertiary structures and functionally important residues. COGs (Clusters of Orthologous Groups) –Proteins or groups of paralogs from at least 3 lineages that correspond to an ancient conserved domain