This alignment is of 2 types:
1) Local Alignment.
2) Global Alignment.
LOCAL ALIGNMENT
In this alignment, stretches of sequences with high density of matches are aligned, thus generating one or more islands of matches or subalignments in the aligned sequences. Local alignments are more suitable for aligning sequences that are similar along some of their lengths but dissimilar in other sequences that differ in length or sequences that share a conserved region or domain.
The SMITH-WATERMAN ALGORITHM is used to produce local alignments between pairs of DNA or protein sequences -1) Assigns a score to each pair of bases
- uses similarity scores (where identical or similar residues have positive scores and dissimilar ones have 0 or negative scores) only.
- uses positive scores for related residues
- uses negative scores for substitutions and gaps
2) Initializes edges of the matrix with zeros
3) As the scores are summed in the matrix, any score below 0 is recorded as 0.
4) Begins traces back at the maximum value found anywhere in the matrix.
5) Continues until the score falls to 0.
GLOBAL ALIGNMENT
In global alignment, an attempt is made to align the entire sequence, using all sequence characters. Sequences that are quite similar and approximately the same length are suitable candidates for global alignment.
Here, the NEEDLEMAN-WUNSCH ALGORITHM is used to produce global alignments. The alignment is stretched over the entire sequence length to include as many matching characters as possible up to and including the sequence ends. A global alignment is made possible by including gaps either within the middle of the alignments or at either end of one or both sequences. Vertical bars between the sequence indicate the presence of identical characters.
GLOBAL ALIGNMENT vs LOCAL ALIGNMENT
When trying to deduce evolutionary history by examining protein sequence similarity and differences, global alignment is typically meaningful and effective since it compares proteins of the same sequence family.
However, in many biological applications, local alignment is more meaningful than global alignment. It is particularly true when large stretches of anonymous DNA are compared since only some internal stretches of those strings maybe related.
When comparing protein sequences, local alignment is critical because proteins from very different families are often made up of the same structural or functional subunits (Motifs or Domains) and local alignment is appropriate for searching for these subunits.
An interesting example of conserved domain comes from protein encoded by Homeoboxgenes. These genes show up in a wide variety of species from fruit flies to frogs to humans. These genes regulate, at high level, embryonic development and a single mutation in their genes can transform one body part to another. The protein sequences that these genes encode are very different in each specie except in one region called the Homeodomains. This domain consists of about 60 amino acids that form the part of regulatory protein that binds to DNA. Homeodomains made by certain insect and mammalian genes are particularly similar showing about 50-95% identity in alignment without spaces.
Protein to DNA binding is central in how those proteins regulate embryo development and cell differentiation. So, the amino acid sequences is the most biologically critical part of those proteins (highly conserved) whereas the other parts of the protein sequences show very little similarity. In these cases, local alignment is certainly more appropriate way to compare protein sequence than in global alignment. Local alignment in proteins is additionally important because certain isolated characters of related proteins maybe more highly conserved than the rest of the proteins.
Local alignments will more likely detect these conserved characters than global alignment. It is the most appropriate type of alignment for comparing protein from different protein families.
No comments:
Post a Comment