Multiple sequence alignment

Transcription

1 Multiple sequence alignment Bas. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, February 18 th 2016 Protein alignments We have seen how to create a pairwise alignment of two sequences by using a model of evolution combined with a dynamic programming algorithm Alignment matrix Gap penalty: C A P T C F T Substitution matrix 1

2 Multiple sequence alignment What if we want to align many sequences, for example a homologous gene in several animals? Option: dynamic programming in multiple dimensions Alignment matrix 1 2 C C N N U Q U Q S S S Q U N C 3 This algorithm is inefficient, because the size of the matrix (and thus the number of computational steps) scales exponentially with the number of sequences Progressive multiple sequence alignment Algorithm goes through a series of pairwise alignments You first need a guide that indicates how similar/different the sequences are to each other A guide is not a phylogenetic tree Phylogenetic trees show evolutionary history, guides only show similarity You need an alignment first before you can create a phylogenetic tree More about phylogenetic trees later in this course Align the most similar pair of sequences first, and then progressively alignmore divergent sequence pairs Iterate Create a sequence profile to summarize the already-aligned sequences This algorithm is efficient, because the computational steps scale linearly with the number of sequences 2

3 Multiple sequence alignment Shows you if sequences are related(homologous) Identifies more/less conserved regions Sequence conservation is a clue for functional importance Needed to create sequence profiles and sequence logos Needed to make good phylogenetic trees We will create phylogenetic trees later in this course Alignment files Alignments can be stored in Fasta format Other formats are also possible, check files in plain text editor Alignment files can easily be spotted when opened in a plain text editor: Some of the sequences contain gap characters: So that all sequences have exactly the same length >protein_sequence_a MTQSHHHVAA FDLGSSIRQ GLTT DPNRAIG TFGI >protein_sequence_b MTQSSHHVAA FDLGAALHQ GLTTDYSV QRDPNRAVG TFGV >protein_sequence_c AVAA FDLGAALRQ GLTTDYAI QRDPNHALG TF-- As in Fasta files, spaces and newlines just make sequences easier to read, they do not have any meaning 3

4 Some useful programs Using existing bioinformatic programs is recommended because it makes your analysis reproducible Programs to align sequences Clustal Omega T-Coffee MAFFT Muscle Programs to view alignments Clustal Jalview Seaview Sequence identifiers Aligned sequences Conservation: identity at position Quality: conservation of similar amino acids Consensus: frequency of top residue 4

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6 2/21/16 Trimming alignments Conserved regions are easier to align than variable regions Variable regions can add noise to an alignment Badly aligned regions can be trimmed before further analysis Gblocks eliminates poorly aligned positions and divergent regions of a DNA or protein alignment As time passes, mutations accumulate and sequences diverge Use variable regions to compare closely related sequences Use conserved regions to compare distantly related sequences Sequence alignments allow us to look back in time Origin of mammals Origin of vertebrates Origin of animals Origin of eukaryotes arliest fossils Origin of life 6

7 2/21/16 Meaningful sequence variation Use alignments to identify highly conserved sites that may be important for function Active site (enzymatic function) Binding site (molecular interaction) Binding site Conservation Alignments can also identify unexpected variable regions Low High ß ß ß ß ß Metagenome ß Aligning metagenomic reads from many different samples to a bacteriophage genome reveals metagenomic islands: crassphage genomic position à Metagenomic islands Regions in a genome that are highly divergent from the sequences found in metagenomes 7

8 HIV-1 Glycoprotein Viral envelope Capsid Reverse transcriptase HIV HIV infects helper T cells of the immune system Loss of immune cells impairs immune responses, leading to acquired immune deficiency syndrome (AIDS) HIV eludes the immune system by mutating very rapidly RNA (two identical strands) New virus Viral RNA RNA-DNA hybrid DNA RNA genome for the next viral generation Chromosomal DNA HOST CLL Reverse transcriptase mrna NUCLUS Provirus Timeline of HIV-1 infection 8

9 Immune escape Amount of virus In blood Antibodies to variant 1 appear Variant 1 Antibodies to variant 2 appear Variant 2 Antibodies to variant 3 appear Variant Weeks after infection Which HIV-1 proteins should we use in vaccine? 9

10 Which HIV-1 proteins should we use in vaccine? HIV nvelope HIV Capsid 10