Introduction to Bioinformatics

1. Introduction to Bioinformatics Lecture 12: Iterative homology searching and Protein Structure-Function Relationships Centre for Integrative Bioinformatics VU (IBIVU)

2. PSI (Position Specific Iterated) BLAST • basic idea • use results from BLAST query to construct a profile matrix • search database with profile instead of query sequence • iterate

3. A Profile Matrix (Position Specific Scoring Matrix – PSSM) This is the same as a profile without position-specific gap penalties

4. PSI BLAST • Searching with a Profile • aligning profile matrix to a simple sequence • like aligning two sequences • except score for aligning a character with a matrix position is given by the matrix itself • not a substitution matrix

5. PSI BLAST:Constructing the Profile Matrix Figure from: Altschul et al. Nucleic Acids Research 25, 1997

6. PSI BLAST:Determining Profile Elements • the value for a given element of the profile matrix is given by: • where the probability of seeing amino acid aiin column j is estimated as: Observed frequency Pseudocount e.g.  = number of sequences in profile, =1

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

8. 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 (hits) found, constructs a multiple alignment (master-slave alignment) and abstracts a position-specific scoring matrix (PSSM) from this alignment. • Rescan the database in a subsequent round, using the PSSM, to find more homologous sequences. Iteration continues until user decides to stop or search has converged

11. 1 - This portion of each description links to the sequence record for a particular hit. 2 - Score or bit score is a value calculated from the number of gaps and substitutions associated with each aligned sequence. The higher the score, the more significant the alignment. Each score links to the corresponding pairwise alignment between query sequence and hit sequence (also referred to as subject sequence). 3 - E Value (Expect Value) describes the likelihood that a sequence with a similar score will occur in the database by chance. The smaller the E Value, the more significant the alignment. For example, the first alignment has a very low E value of e-117 meaning that a sequence with a similar score is very unlikely to occur simply by chance. 4 - These links provide the user with direct access from BLAST results to related entries in other databases. ‘L’ links to LocusLink records and ‘S’ links to structure records in NCBI's Molecular Modeling DataBase.

12. ‘X’ residues denote low-complexity sequence fragments that are ignored

13. PSI-BLAST output example

14. Alignment Bit Score B = (S – ln K) / ln 2 • S is the raw alignment score • The bit score (‘bits’) B has a standard set of units • The bit score B is calculated from the number of gaps and substitutions associated with each aligned sequence. The higher the score, the more significant the alignment • and K and are the statistical parameters of the scoring system (BLOSUM62 in Blast). • See Altschul and Gish, 1996, for a collection of values for  and K over a set of widely used scoring matrices. • Because bit scores are normalized with respect to the scoring system, they can be used to compare alignment scores from different searches based on different scoring schemes (a.a. exchange matrices)

15. Normalised sequence similarity The p-valueis defined as theprobability 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)

16. Normalised sequence similarityStatistical 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) For example, 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.

17. A model for database searching score probabilities • Scores resulting from searching with a query sequence against a database follow the Extreme Value Distribution (EDV) (Gumbel, 1955). • Using the EDV, the raw alignment scores are converted to a statistical score (E value) that keeps track of the database amino acid composition and the scoring scheme (a.a. exchange matrix)

18. Extreme Value Distribution y = 1 – exp(-e-(x-)) Probability density function for the extreme value distribution resulting from parameter values  = 0 and  = 1, [y = 1 – exp(-e-x)], where  is the characteristic value and  is the decay constant.

19. Extreme Value Distribution (EDV) EDV approximation real data You know that an optimal alignment of two sequences is selected out of many suboptimal alignments, and that a database search is also about selecting the best alignment(s). This bodes well with the EDV which has a right tail that falls off more slowly than the left tail. Compared to using the normal distribution, when using the EDV an alignment has to score further away from the expected mean value to become a significant hit.

20. Extreme Value Distribution The probability of a score S to be larger than a given value x can be calculated following the EDV as: E-value: P(S x) = 1 – exp(-e -(x-)), where  =(ln Kmn)/, and K a constant that can be estimated from the background amino acid distribution and scoring matrix (see Altschul and Gish, 1996, for a collection of values for  and K over a set of widely used scoring matrices).

21. Extreme Value Distribution Using the equation for  (preceding slide), the probability for the raw alignment score S becomes P(S x) = 1 – exp(-Kmne-x). In practice, the probability P(Sx) is estimated using the approximation 1 – exp(-e-x) e-x, which is valid for large values of x. This leads to a simplification of the equation for P(Sx): P(S  x)  e-(x-) = Kmne-x. The lower the probability (E value) for a given threshold value x, the more significant the score S.

22. Normalised sequence similarityStatistical significance • Database searching is commonly performed using an E-value in between 0.1 and 0.001. • 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.

23. Words of Encouragement • “There are three kinds of lies: lies, damned lies, and statistics” – Benjamin Disraeli • “Statistics in the hands of an engineer are like a lamppost to a drunk – they’re used more for support than illumination” • “Then there is the man who drowned crossing a stream with an average depth of six inches.” – W.I.E. Gates

24. Protein structure-functionrelationships

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

26. 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)

27. Protein function groups • Catalysis (enzymes) • Binding – transport (active/passive) • Protein-DNA/RNA binding (e.g. histones, transcription factors) • Protein-protein interactions (e.g. antibody-lysozyme) • Protein-fatty acid binding (e.g. apolipoproteins) • Protein – small molecules (drug interaction, structure decoding) • Structural component (e.g. -crystallin) • Regulation • Transcription regulation • Signalling • Immune system • Motor proteins (actin/myosin)

28. 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

29. 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)?

30. 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 (this is called an operational definition)

31. How to evolve By addition of domains (at either end of protein sequence) – Lesk book page 108 Often through gene duplication followed by divergence Multi-domain proteins are result of gene fusion

32. Protein structure evolution Insertion/deletion of secondary structural elements can ‘easily’ be done at loop sites

33. Flavodoxin fold 5() fold

34. Flavodoxin family - TOPS diagrams (Flores et al., 1994) 4 3 2 5 4 3 1 2 5 1

35. Protein structure evolution Insertion/deletion of structural domains can ‘easily’ be done at loop sites N C

36. The basic functional unit of a protein is the domain A domain is a: • Compact, semi-independent unit (Richardson, 1981). • Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973). • Recurring functional and evolutionary module (Bork, 1992). • “Nature is a ‘tinkerer’ and not an inventor” (Jacob, 1977).

37. Delineating domains is essential for: • Obtaining high resolution structures (x-ray, NMR) • Sequence analysis • Multiple sequence alignment methods • Prediction algorithms (SS, Class, secondary/tertiary structure) • Fold recognition and threading • Elucidating the evolution, structure and function of a protein family (e.g. ‘Rosetta Stone’ method) • Structural/functional genomics • Cross genome comparative analysis

38. 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

39. Complex protein functions are a result of multiple domains • An example is the so-called swivelling domain in pyruvate phosphatedikinase (Herzberg et al., 1996), which brings an intermediate enzymatic product overabout 45 Å from the active site of one domain to that of another. • This enhances the enzymatic activity

40. 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. http://www.mshri.on.ca/pawson

42. Globin fold  protein myoglobin PDB: 1MBN

43.  sandwich  protein immunoglobulin PDB: 7FAB

44. TIM barrel  /  protein Triose phosphate IsoMerase PDB: 1TIM

45. A fold in  + protein ribonuclease A PDB: 7RSA The red balls represent waters that are ‘bound’ to the protein based on polar contacts

47. 434 Cro protein complex (phage) PDB: 3CRO

48. Zinc finger DNA recognition (Drosophila) PDB: 2DRP ..YRCKVCSRVY THISNFCRHY VTSH...

49. Zinc-finger DNA binding protein family Characteristics of the family: Function: The DNA-binding motif is found as part of transcription regulatory proteins. Structure: One of the most abundant DNA-binding motifs. Proteins may contain more than one finger in a single chain. For example Transcription Factor TF3A was the first zinc-finger protein discovered to contain 9 C2H2 zinc-finger motifs (tandem repeats). Each motif consists of 2 antiparallel beta-strands followed by by an alpha-helix. A single zinc ion is tetrahedrally coordinated by conserved histidine and cysteine residues, stabilising the motif.

50. Zinc-finger DNA binding protein family Characteristics of the family: Binding: Fingers bind to 3 base-pair subsites and specific contacts are mediated by amino acids in positions -1, 2, 3 and 6 relative to the start of the alpha-helix. Contacts mainly involve one strand of the DNA. Where proteins contain multiple fingers, each finger binds to adjacent subsites within a larger DNA recognition site thus allowing a relatively simple motif to specifically bind to a wide range of DNA sequences. This means that the number and the type of zinc fingers dictates the specificity of binding to DNA