Sequence Alignment and Searching

Question 1

What are homologues?

Answer 1

Evolutionarily-related proteins
Two types: orthologs and paralogs

Question 2

How do protein sequences evolve?

Answer 2

• Substitutions due to single-base mutations
• Insertions or deletions of residues - usually in the connecting loops (not the secondary structures)
• Indels make it harder to compare sequences (need to line up the equivalent regions and put gaps where there are indels)

Question 3

The formula for % sequence identity

Answer 3

(no. of identical residues/no. of residues in smallest protein) x100

Question 4

How do you search sequence databases?

Answer 4

• Do fast scans using approximate methods (BLAST or PSI-BLAST)
• Align proteins carefully using a dynamic programming method
• Scan against sequence profiles/HMMs in secondary databases
• Align query sequences against family relatives

Question 5

Tuple size

Answer 5

Runs of identical residues (at least 3 in a row)

Question 6

Window (path matrix)

Answer 6

The two red bars on either side of the matrix. The window is a certain distance not too far from the centre diagonal.

Question 7

Score (path matrix)

Answer 7

The score of the path (watch yt video)

Question 8

Types of residue substitution matrices

Answer 8

• Identity matrix
• Physicochemical properties matrix
• Evolutionary matrix

Question 9

Physicochemical properties matrix

Answer 9

Score residue pairs according to similarities in their physicochemical properties

Question 9

Identity matrix

Answer 9

Simplest scoring scheme - amino acids are either identical (1) or non-identical (0)

Question 10

Evolutionary matrices

Answer 10

Score residue pairs according to how frequently the mutation is observed to occur in evolution

Question 11

Dayhoff matrix

Answer 11

• Based on evolutionary relationships, it is based on analysing the substitutions observed in closely related sequences (>80% identity)
• The method measures evolutionary distance by determining the number of point-accepted mutations

Question 12

BLOSUM substitution matrices

Answer 12

• The matrix is derived from analysing substitution patterns in more distant relatives (<85% sequence identity)
• For clusters of related sequences derive multiple alignments without gaps
• For short regions of related sequences use the alignments to calculate residue substitution frequencies

Question 13

How do we know which matrix to use?

Answer 13

• Matrices derived from observed substitution data (e.g. BLOSUM) are better than identity matrices or those based on physical properties
• In database searching it may be best to use PAM120 or BLOSUM62
• Various studies suggest that PAM250 gives the best result when aligning distant proteins using dynamic programming algorithms

Question 14

Needleman & Wunsch Algorithm steps in dynamic programming

Answer 14

• Score the path matrix
• Accumulate scores in the path matrix
• Trace the highest-scoring path in the path matrix

Question 15

How do we accumulate scores in the path matrix?

Answer 15

• Start at the bottom right
• Move right to left accumulating scores
• Move up the next row

Question 16

How does BLAST work?

Answer 16

• A highest-scoring segment pair is found between two sequences
• The sequences may be related if HSP score >cutoff
  1. Match significant words
  2. Compare the word list to the database and identify exact matches
  3. For each word match, extend the alignment using a PAM matrix and dynamic programming
• BLAST searches for 2 non-overlapping segments on the same diagonal. They must be within a certain distance of each other before the extension is invoked. It can also allow gaps so that the method joins segments on different diagonals.

Question 17

How do we assess the significance of a sequence match?

Answer 17

• Length - we can get artificially high scores between small sequences
• Composition - if sequences are rich in particular amino acid residues we can get high scores for unrelated proteins
• To assess the significance of a match it is necessary to compare the score with that returned by random or unrelated sequences
• If the database is small or when considering a pair-wise comparison, the sequences can be shuffled to generate random sequences

Question 18

S (BLAST)

Answer 18

Score for the pairwise alignment

Question 19

E-value (BLAST)

Answer 19

Number of expected hits by chance with score S or higher given the size of the database and the length of the alignment

Question 20

How do you conduct a Multiple Sequence Alignment

Answer 20

• Align the most closely related pairs using DP and gradually align these groups together keeping the gaps that appear in earlier alignments fixed
• (or) Add sequences one at a time to a growing multiple alignment