1. Predicting protein structure and function

2. Protein function Genome/DNA Transcription factors Transcriptome/mRNA
Proteome
Metabolome
Physiome
Transcription factors
Ribosomal proteins
Chaperonins
Enzymes

3. Protein function Not all proteins are enzymes:
-crystallin: eye lens protein – needs to stay stable and transparent for a lifetime (very little turnover in the eye lens)

4. What can happen to protein function through evolution
Proteins can have multiple functions (and sometimes many -- Ig).
Enzyme function is defined by specificity and activity
Through evolution:
Function and specificity can stay the same
Function stays same but specificity changes
Change to some similar function (e.g. somewhere else in metabolic system)
Change to completely new function

5. How to arrive at a given function
Divergent evolution – homologous proteins –proteins have same structure and “same-ish” function
Convergent evolution – analogous proteins – different structure but same function
Question: can homologous proteins change structure (and function)?

6. How to evolve Important distinction:
Orthologues: homologous proteins in different species (all deriving from same ancestor)
Paralogues: homologous proteins in same species (internal gene duplication)
In practice: to recognise orthology, bi-directional best hit is used in conjunction with database search program

7. How to evolve
By addition of domains (at either end of protein sequence) – Lesk book page 108
Often through gene duplication followed by divergence

8. Structural domain organisation can be nasty…
Pyruvate kinase
Phosphotransferase
b barrel regulatory domain
a/b barrel catalytic substrate binding domain
a/b nucleotide binding domain
1 continuous + 2 discontinuous domains

9. The DEATH Domain
Present in a variety of Eukaryotic proteins involved with cell death.
Six helices enclose a tightly packed hydrophobic core.
Some DEATH domains form homotypic and heterotypic dimers.

10. Flavodoxin fold
5() fold

11. Rules of thumb when looking at a multiple alignment (MA)
Hydrophobic residues are internal
Gly (Thr, Ser) in loops
MA: hydrophobic block -> internal -strand
MA: alternating (1-1) hydrophobic/hydrophilic => edge -strand
MA: alternating 2-2 (or 3-1) periodicity => -helix
MA: gaps in loops
MA: Conserved column => functional? => active site

12. Rules of thumb when looking at a multiple alignment (MA)
Active site residues are together in 3D structure
Helices often cover up core of strands
Helices less extended than strands => more residues to cross protein
-- motif is right-handed in >95% of cases (with parallel strands)
MA: ‘inconsistent’ alignment columns and match errors!
Secondary structures have local anomalies, e.g. -bulges

13. Burried and Edge strands
Parallel -sheet
Anti-parallel -sheet

14. Periodicity patterns
Burried -strand
Edge -strand
-helix

15. Genome Science (Genomics)
Aims:
To assemble physical and genetic maps of the genome
To generate and order genomic and expressed sequences
To identify and annotate the complete set of genes encoded within a genome
To compile atlases of gene expression
To accumulate functional data, including biochemical and phenotypic properties of genes
To characterise DNA sequence diversity
To provide resources for comparison with other genomes (bioinformatics)
To establish an integrated web-based database and research interface (bioinformatics)

16. Assembling physical and genetic maps of the genome
Genetic map gives relative order of genetic markers in linkage groups
Distance is expressed as units of recombination
Genetic markers are mostly physical DNA attributes; including sequence tags, simple repeats, and restriction enzyme polymorphisms
Can also be phenotypes associated with Mendelian loci

17. Assembling physical and genetic maps of the genome
In diploid organisms, co-segregation is used in pedigrees or progeny of controlled crosses
Standard unit of genetic distance is centiMorgan (cM)
1 cM denotes recombination frequency of 0.01
In human, 1 cM ~= 1000 kB (kilo Base), in Drosophila 1cM ~= 500 kB
Recombination frequency of 0.5 (50 cM) is threshold for assigning markers to same linkage groups

18. M1 M2 M3 M4 M5 M6 m1 m2 m3 m4 m5 m6 ×

19. Sequence database searching – Homology searching
Dynamic Programming (DP) too slow for repeated database searches.
Therefore fast heuristic methods:
FASTA
BLAST and PSI-BLAST
QUEST
HMMER
SAM-T98
Fast heuristics
Hidden Markov modelling

20. FASTA
Compares a given query sequence with a library of sequences and calculates for each pair the highest scoring local alignment
Speed is obtained by delaying application of the dynamic programming technique to the moment where the most similar segments are already identified by faster and less sensitive techniques
FASTA routine operates in four steps:

21. FASTA Operates in four steps:
Rapid searches for identical words of a user specified length occurring in query and database sequence(s) (Wilbur and Lipman, 1983, 1984). For each target sequence the 10 regions with the highest density of ungapped common words are determined.
These 10 regions are rescored using Dayhoff PAM-250 residue exchange matrix (Dayhoff et al., 1983) and the best scoring region of the 10 is reported under init1 in the FASTA output.
Regions scoring higher than a threshold value and being sufficiently near each other in the sequence are joined, now allowing gaps. The highest score of these new fragments can be found under initn in the FASTA output.
full dynamic programming alignment (Chao et al., 1992) over the final region which is widened by 32 residues at either side, of which the score is written under opt in the FASTA output.

22. FASTA output example
DE METAL RESISTANCE PROTEIN YCF1 (YEAST CADMIUM FACTOR 1)
SCORES Init1: 161 Initn: 161 Opt: 162 z-score: E(): 3.4e-06
Smith-Waterman score: 162; 35.1% identity in 57 aa overlap
test.seq MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLE
:| :|::| |:::||:|||::|: |
YCFI_YEAST CASILLLEALPKKPLMPHQHIHQTLTRRKPNPYDSANIFSRITFSWMSGLMKTGYEKYLV
test.seq LSDIYQIPSVDSADNLSEKLEREWDRE
:|:|::| |:::||:|||::|: |
YCFI_YEAST EADLYKLPRNFSSEELSQKLEKNWENELKQKSNPSLSWAICRTFGSKMLLAAFFKAIHDV

23. FASTA (1) Rapid identical word searches:
Searching for k-tuples of a certain size within a specified bandwidth along search matrix diagonals.
For not-too-distant sequences (> 35% residue identity), little sensitivity is lost while speed is greatly increased.
Technique employed is known as hash coding or hashing: a lookup table is constructed for all words in the query sequence, which is then used to compare all encountered words in each database sequence.

24. FASTA
The k-tuple length is user-defined and is usually 1 or 2 for protein sequences (i.e. either the positions of each of the individual 20 amino acids or the positions of each of the 400 possible dipeptides are located).
For nucleic acid sequences, the k-tuple is 5-20, and should be longer because short k-tuples are much more common due to the 4 letter alphabet of nucleic acids. The larger the k-tuple chosen, the more rapid but less thorough, a database search.

25. BLAST
blastp compares an amino acid query sequence against a protein sequence database
blastn compares a nucleotide query sequence against a nucleotide sequence database
blastx compares the six-frame conceptual protein translation products of a nucleotide query sequence against a protein sequence database
tblastn compares a protein query sequence against a nucleotide sequence database translated in six reading frames
tblastx compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

26. BLAST
Generates all tripeptides from a query sequence and for each of those the derivation of a table of similar tripeptides: number is only fraction of total number possible.
Quickly scans a database of protein sequences for ungapped regions showing high similarity, which are called high-scoring segment pairs (HSP), using the tables of similar peptides. The initial search is done for a word of length W that scores at least the threshold value T when compared to the query using a substitution matrix.
Word hits are then extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of S, and as far as the cumulative alignment score can be increased.

27. BLAST Extension of the word hits in each direction are halted
when the cumulative alignment score falls off by the quantity X from its maximum achieved value
the cumulative score goes to zero or below due to the accumulation of one or more negative-scoring residue alignments
upon reaching the end of either sequence
The T parameter is the most important for the speed and sensitivity of the search resulting in the high-scoring segment pairs
A Maximal-scoring Segment Pair (MSP) is defined as the highest scoring of all possible segment pairs produced from two sequences.

28. PSI-BLAST
Query sequences are first scanned for the presence of so-called low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition likely to lead to spurious hits; are excluded from alignment.
The program then initially operates on a single query sequence by performing a gapped BLAST search
Then, the program takes significant local alignments found, constructs a multiple alignment and abstracts a position specific scoring matrix (PSSM) from this alignment.
Rescan the database in a subsequent round to find more homologous sequences Iteration continues until user decides to stop or search has converged

29. PSI-BLAST iteration Q Q Database hits PSSM PSSM Database hits
Query sequence
xxxxxxxxxxxxxxxxx
Gapped BLAST search Q
Query sequence
xxxxxxxxxxxxxxxxx
Database hits A C D . Y
PSSM Pi Px
Gapped BLAST search A C D . Y
PSSM Pi Px
Database hits

30. PSI-BLAST output example

31. Multiple alignment profiles Gribskov et al
Multiple alignment profiles Gribskov et al A way to represent multiple alignment consensus i A C D  W Y
0.3
0.1 
Gap
penalties
1.0
0.5
Position dependent gap penalties

32. Normalised sequence similarity
The p-value is defined as the probability of seeing at least one unrelated score S greater than or equal to a given score x in a database search over n sequences.
This probability follows the Poisson distribution (Waterman and Vingron, 1994):
P(x, n) = 1 – e-nP(S x),
where n is the number of sequences in the database
Depending on x and n (fixed)

33. Normalised sequence similarity Statistical significance
The E-value is defined as the expected number of non-homologous sequences with score greater than or equal to a score x in a database of n sequences:
E(x, n) = nP(S  x)
if E-value = 0.01, then the expected number of random hits with score S  x is 0.01, which means that this E-value is expected by chance only once in 100 independent searches over the database.
if the E-value of a hit is 5, then five fortuitous hits with S  x are expected within a single database search, which renders the hit not significant.

34. Normalised sequence similarity Statistical significance
Database searching is commonly performed using an E-value in between 0.1 and
Low E-values decrease the number of false positives in a database search, but increase the number of false negatives, thereby lowering the sensitivity of the search.

35. HMM-based homology searching
Most widely used HMM-based profile searching tools currently are SAM-T98 (Karplus et al., 1998) and HMMER2 (Eddy, 1998)
formal probabilistic basis and consistent theory behind gap and insertion scores
HMMs good for profile searches, bad for alignment
HMMs are slow

36. The HMM algorithms
Forward:
(i) = P(observed sequence, ending in state i at base t)
Backward:
ß (i) = P(obs. after t | ending in state i at base t)
Viterbi:
 (i) = max P(obs. , ending in state i at base t) t
Questions:
What is the most likely die (predicted) sequence? Viterbi
What is the probability of the observed sequence? Forward
What is the probability that the 3rd state is B, given the observed sequence? Backward

37. HMM-based homology searching
Transition probabilities and Emission probabilities
Gapped HMMs also have insertion and deletion states

38. Profile HMM: m=match state, I-insert state, d=delete state; go from left to right. I and m states output amino acids; d states are ‘silent”. d1 d2 d3 d4 I0 I2 I3 I4 I1 m0 m1 m2 m3 m4 m5
Start
End

39. Homology-derived Secondary Structure of Proteins
(HSSP)
Sander & Schneider, 1991