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Multiple sequence alignment
- 2. Why sequence alignment • Lots of sequences with unknown structure and function vs. a few (but growing number) sequences with known structure and function • If they align, they are “similar” • If they are similar, then they might have similar structure and/or function. Identify conserved patterns (motifs) • If one of them has known structure/function, then alignment of other might yield insight about how the structure/functions works. Similar motif content might hint to similar function
- 3. Principles of Sequence Alignment • Alignment can reveal homology between sequences • Similarity is descriptive term that tells about the degree of match between the two sequences • Sequence similarity does not always imply a common function • Conserved function does not always imply similarity at the sequence level • Convergent evolution: sequences are highly similar, but are not homologous
- 4. Pairwise alignment • GLOBAL ALIGNMENT: the alignment is stretched over the entire sequence length to include as many matching amino acids as possible up to and including the sequence ends. Vertical bars between the sequences indicate the presence of identical amino acids. eg- Needleman-Wunsch algorithm LOCAL ALIGNMENT: the alignment tends to stop at the end of the regions of identity or strong similarity. A much higher priority is given to finding these local regions than to extending the alignment to include more neighboring amino acid pairs. eg- Smith-Waterman algorithm
- 5. Pairwise Sequence Alignment vs Multiple Sequence Alignment • Pairwise Sequence Alignment is used to identify regions of similarity that may indicate functional, structural and/or evolutionary relationships between two biological sequences (protein or nucleic acid). Multiple Sequence Alignment(MSA) is the alignment of three or more biological sequences of similar length. From the output of MSA applications, homology can be inferred and the evolutionary relationship between the sequences studied.
- 6. Pairwise alignment in Multiple Alignment Method • The most practical and widely used method in multiple sequence alignment is the hierarchical extensions of pairwise alignment methods. • The principal is that multiple alignments is achieved by successive application of pairwise methods.
- 7. Why we do multiple alignments? • In order to characterize protein families, identify shared regions of homology in a multiple sequence alignment • Determination of the consensus sequence of several aligned sequences. • Help prediction of the secondary and tertiary structures of new sequences; • Preliminary step in molecular evolution analysis using Phylogenetic methods for constructing phylogenetic trees.
- 8. Different computational approaches to perform a multiple alignment 1. The Dynamic programming approach 2. Progressive alignment method 3. Iterative refinement method
- 9. 1. The Dynamic programming approach Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. DNA and RNA alignments may use a scoring matrix, but in practice often simply assign a positive match score, a negative mismatch score, and a negative gap penalty. For more than a few sequences, exact algorithms become computationally impractical . This is why this method is not widely used The runtime increases exponentially with the number of sequences you want to align . Aligning 4 sequences of hundred amino acids takes 3 days
- 10. 2. Progressive alignment method • The most widely used approached to multiply sequence alignment. • Heuristic algorithms for multiple alignment are generally used,as they are fast. • Progressive alignment builds up a final MSA by combining pairwise alignment beginning with the most similar pair and progressing to the most distantly related • Common tools eg-CLUSTAL ,T-COFFEE,PIPE-UP
- 11. Progressive alignment method cont… All Progressive alignment methods require two stages First stage in which the relationships between the sequence are represented as a tree called guide tree Second step in which the MSA is built by adding the sequences sequentially to growing MSA according to the guide tree
- 12. Progressive alignment TOOL :- CLUSTAL
- 13. • A popular heuristic algorithm is CLUSTAL by Des Higgins and Paul Sharp in 1988. • CLUSTAL makes a global multiple alignment using a progressive alignment approach.
- 14. The CLUSTAL W processing :- First it computes all pairwise alignment and calculates sequence similarity between pairs 1. Then align the most similar pairs of sequence (this gives us an alignment of 2 sequences called ‘profile’) 2. Align the next closest pair of sequence(or pair of profiles or sequence and profile) 3. Align the nest closest pair of sequences/profile A property of this method is that gap creation is irreversible ’once a gap ,always a gap’
- 15. Iterative refinement method • A set of methods to produce MSAs while reducing the errors inherent in Progressive methods are classified as iterative • They work similarly to progressive methods but repeatedly to the growing MSA • Barton and Sternberg formulated this method for MSA • Common iterative method used – DIALGIN.MUSCLE,ProbCons
- 16. Iterative refinement cont… • It similarly to progressive alignment method but in this case once or new sequence is added to the algorithm the initially aligned sequences are repeatedly realigned in order to obtain the best alignment refinement Refineing step