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Bioinformatica t3-scoring matrices
 

Bioinformatica t3-scoring matrices

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    Bioinformatica t3-scoring matrices Bioinformatica t3-scoring matrices Presentation Transcript

    • FBW 08-10-2012Wim Van Criekinge
    • Inhoud Lessen: Bioinformatica GEEN LES
    • Overview • Introduction – Short recap on databases – Definitions • Scoring Matrices – Theoretical – Empirial • PAM (pam-simulator.pl) • BLOSUM • Pairwise alignmentOverview – Dot-plots (dotplot-simulator.pl)
    • Major sites NCBI - The National Center for Biotechnology Information http://www.ncbi.nlm.nih.gov/ The National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), a part of the National Institutes of Health (NIH). ExPASy - Molecular Biology Server http://expasy.hcuge.ch/www/ Molecular biology WWW server of the Swiss Institute of Bioinformatics (SIB). This server is dedicated to the analysis of protein sequences and structures as well as 2-D PAGE EBI - European Bioinformatics Institute http://www.ebi.ac.uk/
    • Anno 2002 Anno 2003
    • Anno 2004
    • Anno 2005
    • Anno 2006
    • Anno 2007
    • Anno 2009
    • Anno 2010Anno 2010
    • Anno 2011
    • Anno 2012
    • Anno 2012
    • Overview • Introduction – Short recap on databases – Definitions • Scoring Matrices – Theoretical – Empirial • PAM (pam-simulator.pl) • BLOSUM • Pairwise alignmentOverview – Dot-plots (dotplot-simulator.pl)
    • Definitions Identity The extent to which two (nucleotide or amino acid) sequences are invariant. Homology Similarity attributed to descent from a common ancestor.RBP: 26 RVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWD- 84 + K ++ + + GTW++MA+ L + A V T + +L+ W+glycodelin: 23 QTKQDLELPKLAGTWHSMAMA-TNNISLMATLKAPLRVHITSLLPTPEDNLEIVLHRWEN 81
    • DefinitionsOrthologousHomologous sequences in different speciesthat arose from a common ancestral geneduring speciation; may or may not be responsiblefor a similar function.ParalogousHomologous sequences within a single speciesthat arose by gene duplication.
    • speciationduplication
    • Multiple sequence alignment ofglyceraldehyde- 3-phsophate dehydrogenasesfly GAKKVIISAP SAD.APM..F VCGVNLDAYK PDMKVVSNAS CTTNCLAPLAhuman GAKRVIISAP SAD.APM..F VMGVNHEKYD NSLKIISNAS CTTNCLAPLAplant GAKKVIISAP SAD.APM..F VVGVNEHTYQ PNMDIVSNAS CTTNCLAPLAbacterium GAKKVVMTGP SKDNTPM..F VKGANFDKY. AGQDIVSNAS CTTNCLAPLAyeast GAKKVVITAP SS.TAPM..F VMGVNEEKYT SDLKIVSNAS CTTNCLAPLAarchaeon GADKVLISAP PKGDEPVKQL VYGVNHDEYD GE.DVVSNAS CTTNSITPVAfly KVINDNFEIV EGLMTTVHAT TATQKTVDGP SGKLWRDGRG AAQNIIPASThuman KVIHDNFGIV EGLMTTVHAI TATQKTVDGP SGKLWRDGRG ALQNIIPASTplant KVVHEEFGIL EGLMTTVHAT TATQKTVDGP SMKDWRGGRG ASQNIIPSSTbacterium KVINDNFGII EGLMTTVHAT TATQKTVDGP SHKDWRGGRG ASQNIIPSSTyeast KVINDAFGIE EGLMTTVHSL TATQKTVDGP SHKDWRGGRT ASGNIIPSSTarchaeon KVLDEEFGIN AGQLTTVHAY TGSQNLMDGP NGKP.RRRRA AAENIIPTSTfly GAAKAVGKVI PALNGKLTGM AFRVPTPNVS VVDLTVRLGK GASYDEIKAKhuman GAAKAVGKVI PELNGKLTGM AFRVPTANVS VVDLTCRLEK PAKYDDIKKVplant GAAKAVGKVL PELNGKLTGM AFRVPTSNVS VVDLTCRLEK GASYEDVKAAbacterium GAAKAVGKVL PELNGKLTGM AFRVPTPNVS VVDLTVRLEK AATYEQIKAAyeast GAAKAVGKVL PELQGKLTGM AFRVPTVDVS VVDLTVKLNK ETTYDEIKKVarchaeon GAAQAATEVL PELEGKLDGM AIRVPVPNGS ITEFVVDLDD DVTESDVNAA
    • This power of sequence alignments • empirical finding: if two biological sequences are sufficiently similar, almost invariably they have similar biological functions and will be descended from a common ancestor. • (i) function is encoded into sequence, this means: the sequence provides the syntax and • (ii) there is a redundancy in the encoding, many positions in the sequence may be changed without perceptible changes in the function, thus the semantics of the encoding is robust.
    • Overview • Introduction – Short recap on databases – Definitions • Scoring Matrices – Theoretical – Empirial • PAM (pam-simulator.pl) • BLOSUM • Pairwise alignment Overview – Dot-plots (dotplot-simulator.pl)
    • A metric … It is very important to realize, that all subsequent results depend critically on just how this is done and what model lies at the basis for the construction of a specific scoring matrix. A scoring matrix is a tool to quantify how well a certain model is represented in the alignment of two sequences, and any result obtained by its application is meaningful exclusively in the context of that model.
    • Importance of scoring matrices Scoring matrices appear in all analysis involving sequence comparison. The choice of matrix can strongly influence the outcome of the analysis. Scoring matrices implicitly represent a particular theory of evolution. Understanding theories underlying a given scoring matrix can aid in making proper choice. • Nucleic acid and Protein Scoring Matrices
    • Nucleic Acid Scoring Matrices• Identity matrix (similarity) BLAST matrix (similarity) A T C G A T C G A 1 0 0 0 A 5 -4 -4 -4 T 0 1 0 0 T -4 5 -4 -4 C 0 0 1 0 C -4 -4 5 -4 G 0 0 0 1 G -4 -4 -4 5• Transition/Transversion Matrix A T C G A 0 5 5 1 T 5 0 1 5 C 5 1 0 5 G 1 5 5 0 A and T purine -pyrimidine G and C purine-pyrimidine
    • Transition/Transversion Matrix A T C G • Nucleotide bases fall into twoA 0 5 5 1 categories depending on the ringT 5 0 1 5C 5 1 0 5 structure of the base. PurinesG 1 5 5 0 (Adenine and Guanine) are two ring bases, pyrimidines (Cytosine and Thymine) are single ring bases. Mutations in DNA are changes in which one base is replaced by another. • A mutation that conserves the ring number is called a transition (e.g., A -> G or C -> T) a mutation that changes the ring number are called transversions. (e.g. A -> C or A -> T and so on).
    • Transition/Transversion Matrix A T C G • Although there are more ways toA 0 5 5 1 create a transversion, the numberT 5 0 1 5C 5 1 0 5 of transitions observed to occur inG 1 5 5 0 nature (i.e., when comparing related DNA sequences) is much greater. Since the likelihood of transitions is greater, it is sometimes desireable to create a weight matrix which takes this propensity into account when comparing two DNA sequences. • Use of a Transition/Transversion Matrix reduces noise in comparisons of distantly related sequences.
    • The Genome Chose Its Alphabet With Care • Of all the nucleotide bases available, why did nature pick the four we know as A, T, G, and C for the genomic alphabet ? • The choice of A, T, G, and C incorporates a tactic for minimizing the occurrence of errors in the pairing of bases, in the same way that error- coding systems are incorporated into ISBNs on books, credit card numbers, bank accounts, and airline tickets.
    • The Genome Chose Its Alphabet With Care • In the error-coding theory first developed in 1950 by Bell Telephone Laboratories researcher Richard Hamming, a so-called parity bit is added to the end of digital numbers to make the digits add up to an even number. For example, when transmitting the number 100110, you would add an extra 1 onto the end (100110,1), and the number 100001 would have a zero added (100001,0). The most likely transmission error is a single digit changed from 1 to 0 or vice versa. Such a change would cause the sum of the digits to be odd, and the recipient of that number can assume that it was incorrectly transmitted.
    • The Genome Chose Its Alphabet With Care • Represent each nucleotide as a four-digit binary number. • The first three digits represent the three bonding sites that each nucleotide presents to its partner. Each site is either a hydrogen donor or acceptor; a nucleotide offering donor- acceptor-acceptor sites would be represented as 100 and would bond only with an acceptor- donor-donor nucleotide, or 011. • The fourth digit is 1 if the nucleotide is a single-ringed pyrimidine type and 0 if it is a double-ringed purine type. • Nucleotides readily bond with members of the other type.
    • The Genome Chose Its Alphabet With Care • The final digit acted as a parity bit: The four digits of A, T, G, and C all add up to an even number. • Nature restricted its choice to nucleotides of even parity because "alphabets composed of nucleotides of mixed parity would have catastrophic error rates. • For example, nucleotide C (100,1) binds naturally to nucleotide G (011,0), but it might accidentally bind to the odd parity nucleotide X (010,0), because there is just one mismatch. Such a bond would be weak compared to C-G but not impossible. However, C is highly unlikely to bond to any other even-parity nucleotides, such as the idealized amino-adenine (101,0), because there are two mismatches • So, nature has avoided such mistakes by banishing all odd-parity nucleotides from the DNA alphabet.
    • Protein Scoring Matrices: Unitary Matrix • The simplest metric in use is the identity metric. • If two amino acids are the same, they are given one score, if they are not, they are given a different score - regardless, of what the replacement is. • One may give a score of 1 for matches and 0 for mismatches - this leads to the frequently used unitary matrix
    • Protein Scoring Matrices: Unitary Matrix A R N D C Q E G H I L K M F P S T W Y V A 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 R 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 D 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Q 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 E 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 G 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 H 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 I 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 L 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 K 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 M 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 F 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 S 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 W 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 Y 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 V 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
    • Protein Scoring Matrices: Unitary Matrix • The simplest matrix: – High scores for Identities – Low scores for non-identities • Works for closely related proteins • Or one could assign +6 for a match and -1 for a mismatch, this would be a matrix useful for local alignment procedures, where a negative expectation value for randomly aligned sequences is required to ensure that the score will not grow simply from extending the alignment in a random way.
    • Genetic Code Matrix A very crude model of an evolutionary relationship could be implemented in a scoring matrix in the following way: since all point-mutations arise from nucleotide changes, the probability that an observed amino acid pair is related by chance, rather than inheritance should depend on the number of point mutations necessary to transform one codon into the other. A metric resulting from this model would define the distance between two amino acids by the minimal number of nucleotide changes required.
    • Genetic Code Matrix The table is generated by calculating the minimum number of base changes required to convert an amino acid in row i to an amino acid in column j. Note Met->Tyr is the only change that requires all 3 codon positions to change. A S G L K V T P E D N I Q R F Y C H M W Z B X Ala = A O 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 Ser = S 1 O 1 1 2 2 1 1 2 2 1 1 2 1 1 1 1 2 2 1 2 2 2 Gly = G 1 1 0 2 2 1 2 2 1 1 2 2 2 1 2 2 1 2 2 1 2 2 2 Leu = L 2 1 2 0 2 1 2 1 2 2 2 1 1 1 1 2 2 1 1 1 2 2 2 Lys = K 2 2 2 2 0 2 1 2 1 2 1 1 1 1 2 2 2 2 1 2 1 2 2 Val = V 1 2 1 1 2 0 2 2 1 1 2 1 2 2 1 2 2 2 1 2 2 2 2 Thr = T 1 1 2 2 1 2 0 1 2 2 1 1 2 1 2 2 2 2 1 2 2 2 2 Pro = P 1 1 2 1 2 2 1 0 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 Glu - E 1 2 1 2 1 1 2 2 0 1 2 2 1 2 2 2 2 2 2 2 1 2 2 Asp = D 1 2 1 2 2 1 2 2 1 O 1 2 2 2 2 1 2 1 2 2 2 1 2 Asn = N 2 1 2 2 1 2 1 2 2 1 O 1 2 2 2 1 2 1 2 2 2 1 2 Ile = I 2 1 2 1 1 1 1 2 2 2 1 0 2 1 1 2 2 2 1 2 2 2 2 Gln = Q 2 2 2 1 1 2 2 1 1 2 2 2 0 1 2 2 2 1 2 2 1 2 2 Arg = R 2 1 1 1 1 2 1 1 2 2 2 1 1 0 2 2 1 1 1 1 2 2 2 Phe = F 2 1 2 1 2 1 2 2 2 2 2 1 2 2 0 1 1 2 2 2 2 2 2 Tyr = Y 2 1 2 2 2 2 2 2 2 1 1 2 2 2 1 O 1 1 3 2 2 1 2 Cys = C 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 0 2 2 1 2 2 2 His = H 2 2 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 0 2 2 2 1 2 Met = M 2 2 2 1 1 1 1 2 2 2 2 1 2 1 2 3 2 2 0 2 2 2 2 Trp = W 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 0 2 2 2 Glx = Z 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 Asx = B 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2 ??? = X 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
    • Genetic Code Matrix This genetic code matrix already improves sensitivity and specificity of alignments from the identity matrix. The fact that the genetic code matrix works to align related proteins, in the same way that matrices derived from amino-acid properties work says something very interesting about the genetic code: namely that it appears to have evolved to minimize the effects of point mutations.
    • Genetic Code Matrix
    • Overview • Simple identity, which scores only identical amino acids as a match. • Genetic code changes, which scores the minimum number of nucieotide changes to change a codon for one amino acid into a codon for the other. • Chemical similarity of amino acid side chains, which scores as a match two amino acids which have a similar side chain, such as hydrophobic, charged and polar amino acid groups.
    • Amino Acid Residues All proteins are polymers of the 20 naturally occuring amino acids. They are listed here along with their abbreviations :- Alanine Ala A Cysteine Cys C Aspartic AciD Asp D Glutamic Acid Glu E Phenylalanine Phe F Glycine Gly G Histidine His H Isoleucine Ile I Lysine Lys K Leucine Leu L Methionine Met M AsparagiNe Asn N Proline Pro P Glutamine Gln Q ARginine Arg R Serine Ser S Threonine Thr T Valine Val V Tryptophan Trp W TYrosine Tyr Y
    • Amino Acid Residues All amino acids have the same general formula
    • Amino Acid Residues • Hydrophobic-aliphatic amino acids: Their side chains consist of non-polar methyl- or methylene- groups. – These amino acids are usually located on the interior of the protein as they are hydrophobic in nature. – All except for alanine are bifurcated. In the cases of Val and Ile the bifurcation is close to the main chain and can therefore restrict the conformation of the polypeptide by steric hindrance. – red and blue atoms represent polar main chain groups
    • Amino Acid Residues
    • Amino Acid Residues • Hydrophobic-aromatic: Only phenylalanine is entirely non-polar. Tyrosines phenolic side chain has a hydroxyl substituent and tryptophan has a nitrogen atom in its indole ring sytem. – These residues are nearly always found to be largely buried in the hydrophobic interior of a proteins as they are prdeominantly non-polar in nature. – However, the polar atoms of tyrosine and tryptophan allow hydrogen bonding interactions to be made with other residues or even solvent molecules
    • Amino Acid Residues
    • Amino Acid Residues Neutral-polar side chains: a number of small aliphatic side chains containing polar groups which cannot ionize readily. – Serine and threonine possess hydroxyl groups in their side chains and as these polar groups are close to the main chain they can form hydrogen bonds with it. This can influence the local conformation of the polypeptide, – Residues such as serine and asparagine are known to adopt conformations which most other amino acids cannot. – The amino acids asparagine and glutamine posses amide groups in their side chains which are usually hydrogen-bonded whenever they occur in the interior of a protein.
    • Amino Acid Residues
    • Amino Acid Residues • Acidic amino acids: Aspartate and glutamate have carboxyl side chains and are therefore negatively charged at physiological pH (around neutral). – The strongly polar nature of these residues means that they are most often found on the surface of globular proteins where they can interact favourably with solvent molecules. – These residues can also take part in electrostatic interactions with positively charged basic amino acids. – Aspartate and glutamate also can take on catalytic roles in the active sites of enzymes and are well known for their metal ion binding abilities
    • Amino Acid Residues
    • Amino Acid Residues • Basic amino acids: – histidine has the lowest pKa (around 6) and is therefore neutral at around physiological pH. • This amino acid occurs very frequently in enzyme active sites as it can function as a very efficient general acid-base catalyst. • It also acts as a metal ion ligand in numerous protein families. – Lysine and arginine are more strongly basic and are positively charged at physiological pHs. They are generally solvated but do occasionally occur in the interior of a protein where they are usually involved in electrostatic interactions with negatively charged groups such as Asp or Glu. • Lys and Arg have important roles in anion-binding proteins as they can interact electrostatically with the ligand.
    • Amino Acid Residues
    • Amino Acid Residues Conformationally important residues: Glycine and proline are unique amino acids. They appear to influence the conformation of the polypeptide. • Glycine essentially lacks a side chain and therefore can adopt conformations which are sterically forbidden for other amino acids. This confers a high degree of local flexibility on the polypeptide. – Accordingly, glycine residues are frequently found in turn regions of proteins where the backbone has to make a sharp turn. – Glycine occurs abundantly in certain fibrous proteins due to its flexibility and because its small size allows adjacent polypeptide chains to pack together closely. • In contrast, proline is the most rigid of the twenty naturally occurring amino acids since its side chain is covalently linked with the main chain nitrogen
    • Amino Acid Residues
    • Amino Acid Residues Here is one list where amino acids are grouped according to the characteristics of the side chains: Aliphatic - alanine, glycine, isoleucine, leucine, proline, valine, Aromatic - phenylalanine, tryptophan, tyrosine, Acidic - aspartic acid, glutamic acid, Basic - arginine, histidine, lysine, Hydroxylic - serine, threonine Sulphur-containing - cysteine, methionine Amidic (containing amide group) - asparagine, glutamine
    • Hydrophobicity matrix R K D E B Z S N Q G X T H A C M P V L I Y F WArg = R 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0Lys = K 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0Asp = D 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1Glu = E 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1Asx = B 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3Glx = Z 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3Ser = S 6 6 7 7 8 8 10 10 10 10 9 9 9 9 8 8 7 7 7 7 6 6 4Asn = N 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4Gln = Q 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4Gly = G 5 5 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 8 7 7 6 6 5??? = X 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5Thr = T 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5His = H 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5Ala = A 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5Cys = C 4 4 5 5 6 6 8 8 8 8 9 9 9 9 10 10 9 9 9 9 8 8 5Met = M 3 3 4 4 6 6 8 8 8 8 9 9 9 9 10 10 10 10 9 9 8 8 7Pro = P 3 3 4 4 6 6 7 8 8 8 8 8 9 9 9 10 10 10 9 9 9 8 7Val = V 3 3 4 4 5 5 7 7 7 8 8 8 8 8 9 10 10 10 10 10 9 8 7Leu = L 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8Ile = I 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8Tyr = Y 2 2 3 3 4 4 6 6 6 6 7 7 7 7 8 8 9 9 9 9 10 10 8Phe = F 1 1 2 2 4 4 6 6 6 6 7 7 7 7 8 8 8 8 9 9 10 10 9Trp = W 0 0 1 1 3 3 4 4 4 5 5 5 5 5 6 7 7 7 8 8 8 9 10 •Physical/Chemical characteristics: Attempt to quantify some physical or chemical attribute of •the residues and arbitrarily assign weights based on similarities of the residues in this chosen property.
    • Other similarity scoring matrices might be constructed fromany property of amino acids that can be quantified- partition coefficients between hydrophobic and hydrophilic phases- charge- molecular volumeUnfortunately, …
    • AAindexAmino acid indices and similarity matrices (http://www.genome.ad.jp/dbget/aaindex.html)List of 494 Amino Acid Indices in AAindex ver.6.0• ANDN920101 alpha-CH chemical shifts (Andersen et al., 1992)• ARGP820101 Hydrophobicity index (Argos et al., 1982)• ARGP820102 Signal sequence helical potential (Argos et al., 1982)• ARGP820103 Membrane-buried preference parameters (Argos et al., 1982)• BEGF750101 Conformational parameter of inner helix (Beghin-Dirkx, 1975)• BEGF750102 Conformational parameter of beta-structure (Beghin-Dirkx, 1975)• BEGF750103 Conformational parameter of beta-turn (Beghin-Dirkx, 1975)• BHAR880101 Average flexibility indices (Bhaskaran-Ponnuswamy, 1988)• BIGC670101 Residue volume (Bigelow, 1967)• BIOV880101 Information value for accessibility; average fraction 35% (Biou et al., 1988)• BIOV880102 Information value for accessibility; average fraction 23% (Biou et al., 1988)• BROC820101 Retention coefficient in TFA (Browne et al., 1982)• BROC820102 Retention coefficient in HFBA (Browne et al., 1982)• BULH740101 Transfer free energy to surface (Bull-Breese, 1974)• BULH740102 Apparent partial specific volume (Bull-Breese, 1974)
    • Protein Eng. 1996 Jan;9(1):27-36.
    • Overview • Simple identity, which scores only identical amino acids as a match. • Genetic code changes, which scores the minimum number of nucieotide changes to change a codon for one amino acid into a codon for the other. • Chemical similarity of amino acid side chains, which scores as a match two amino acids which have a similar side chain, such as hydrophobic, charged and polar amino acid groups. • The Dayhoff percent accepted mutation (PAM) family of matrices, which scores amino acid pairs on the basis of the expected frequency of substitution of one amino acid for the other during protein evolution.
    • Dayhoff Matrix • In the absence of a valid model derived from first principles, an empirical approach seems more appropriate to score amino acid similarity. • This approach is based on the assumption that once the evolutionary relationship of two sequences is established, the residues that did exchange are similar.
    • Overview Model of Evolution: “Proteins evolve through a succesion of independent point mutations, that are accepted in a population and subsequently can be observed in the sequence pool.” Definition: The evolutionary distance between two sequences is the (minimal) number of point mutations that was necessary to evolve one sequence into the other
    • Principle • The model used here states that proteins evolve through a succesion of independent point mutations, that are accepted in a population and subsequently can be observed in the sequence pool. • We can define an evolutionary distance between two sequences as the number of point mutations that was necessary to evolve one sequence into the other.
    • Overview • M.O. Dayhoff and colleagues introduced the term "accepted point mutation" for a mutation that is stably fixed in the gene pool in the course of evolution. Thus a measure of evolutionary distance between two sequences can be defined: • A PAM (Percent accepted mutation) is one accepted point mutation on the path between two sequences, per 100 residues.
    • Principles of Scoring Matrix Construction First step: finding “accepted mutations” In order to identify accepted point mutations, a complete phylogenetic tree including all ancestral sequences has to be constructed. To avoid a large degree of ambiguities in this step, Dayhoff and colleagues restricted their analysis to sequence families with more than 85% identity.
    • Overview Identification of accepted point mutations: •Collection of correct (manual) alignments • 1300 sequences in 72 families • closely related in order not to get multiply changes at the same position • Construct a complete phylogenetic tree including all ancestral sequences. • Dayhoff et al restricted their analysis to sequence families with more than 85% identity. • Tabulate into a 20x20 matrix the amino acid pair exchanges for each of the observed and inferred sequences.
    • Overview ACGH DBGH ADIJ CBIJ / / / / B - C / A - D B - D / A - C / / / / ABGH ABIJ / I - G / J - H / / / | | |
    • Dayhoff’s PAM1 mutation probability matrix (Transition Matrix) A R N D C Q E G H I Ala Arg Asn Asp Cys Gln Glu Gly His Ile 9867 2 9 10 3 8 17 21 2 6 A 1 9913 1 0 1 10 0 0 10 3 R 4 1 9822 36 0 4 6 6 21 3 N 6 0 42 9859 0 6 53 6 4 1 D 1 1 0 0 9973 0 0 0 1 1 C 3 9 4 5 0 9876 27 1 23 1 Q 10 0 7 56 0 35 9865 4 2 3 E 21 1 12 11 1 3 7 9935 1 0 G 1 8 18 3 1 20 1 0 9912 0 H 2 2 3 1 2 1 2 0 0 9872 I
    • PAM1: Transition Matrix Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val A R N D C Q E G H I L K M F P S T W Y V Ala A 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18 Arg R 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1 Asn N 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1 Asp D 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1 Cys C 1 1 0 0 9973 0 0 0 1 1 0 0 0 0 1 5 1 0 3 2 Gln Q 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1 Glu E 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2 Gly G 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5 His H 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1 Ile I 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33 Leu L 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15 Lys K 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1 Met M 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4 Phe F 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0 Pro P 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2 Ser S 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2 Thr T 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9 Trp W 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0 Tyr Y 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1 Val V 13 2 1 1 3 2 2 3 3 57 11 1 17 1 3 2 10 0 2 9901
    • PAM1: Transition Matrix Numbers of accepted point mutations (x10) accumulated from closely related sequences. Fractional exchanges result when ancestral sequences are ambiguous: the probabilities are distributed equally among all possibilities. The total number of exchanges tallied was 1,572. Note that 36 exchanges were never observed. The Asp-Glu pair had the largest number of exchanges
    • Principles of Scoring Matrix Construction Second step: Frequencies of Occurence If the properties of amino acids differ and if they occur with different frequencies, all statements we can make about the average properties of sequences will depend on the frequencies of occurence of the individual amino acids. These frequencies of occurence are approximated by the frequencies of observation. They are the number of occurences of a given amino acid divided by the number of amino-acids observed. The sum of all is one.
    • Amino acid frequencies Second step: Frequencies of Occurence 1978 1991 L 0.085 0.091 A 0.087 0.077 G 0.089 0.074 S 0.070 0.069 V 0.065 0.066 E 0.050 0.062 T 0.058 0.059 K 0.081 0.059 I 0.037 0.053 D 0.047 0.052 R 0.041 0.051 P 0.051 0.051 N 0.040 0.043 Q 0.038 0.041 F 0.040 0.040 Y 0.030 0.032 M 0.015 0.024 H 0.034 0.023 C 0.033 0.020 W 0.010 0.014
    • Principles of Scoring Matrix Construction Third step: Relative Mutabilities • To obtain a complete picture of the mutational process, the amino-acids that do not mutate must be taken into account too. • We need to know: what is the chance, on average, that a given amino acid will mutate at all. This is the relative mutability of the amino acid. • It is obtained by multiplying the number of observed changes by the amino acids frequency of occurence.
    • OverviewCompute amino acid mutability, mj, i.e., the propabilityof a given amino acid, j, to be replaced. Aligned A D A Sequences A D B Amino Acids A B D Observed Changes 1 1 0 Frequency of Occurence 3 1 2 (Total Composition) Relative Mutability .33 1 0
    • Principles of Scoring Matrix Construction 1978 1991 A 100 100 C 20 44 D 106 86 E 102 77 F 41 51 G 49 50 H 66 91 I 96 103 K 56 72 L 40 54 M 94 93 N 134 104 P 56 58 Q 93 84 R 65 83 S 120 117 T 97 107 V 74 98 W 18 25 Y 41 50
    • Principles of Scoring Matrix Construction Fourth step: Mutation Probability Matrix • With these data the probability that an amino acid in row i of the matrix will replace the amino acid in column j can be calculated: it is the mutability of amino acid j, multiplied by the relative pair exchange frequency (the pair exchange frequency for ij divided by the sum of all pair exchange frequencies for amino acid i). ADB ADA j A D B Mij= The mutation probability matrix gives the A probability, that an amino acid i will replace an amino i D acid of type j in a given evolutionary interval, in two B related sequences
    • Principles of Scoring Matrix Construction Fifth step: The Evolutionary Distance • Since the represent the probabilites for amino acids to remain conserved, if we scale all cells of our matrix by a constant factor we can scale the matrix to reflect a specific overall probability of change. We may chose so that the expected number of changes is 1 %, this gives the matrix for the evolutionary distance of 1 PAM.
    • Principles of Scoring Matrix Construction 6. Relatedness Odds • By comparison, the probability that that same event is observed by random chance is simply given by the frequency of occurence of amino acid i • Rij = probability that j replaces i in related proteins • Piran = probability that j replaces I by chance (eg unrelated proteins) • Piran = fi = the frequency of occurance of amino acid i
    • Principles of Scoring Matrix Construction Last step: the log-odds matrix • Since multiplication is a computationally expensive process, it is preferrable to add the logarithms of the matrix elements. This matrix, the log odds matrix, is the foundation of quantitative sequence comparisons under an evolutionary model. • Since the Dayhoff matrix was taken as the log to base 10, a value of +1 would mean that the corresponding pair has been observed 10 times more frequently than expected by chance. A value of -0.2 would mean that the observed pair was observed 1.6 times less frequently than chance would predict.
    • • http://www.bio.brandeis.edu/InterpGenes/Proj ect/align12.htm
    • PAM 1 Scoring Matrix A B C D E F G H I K L M N P Q R S T V W Y Z0.4 0.0 -0.4 0.0 0.0 -0.8 0.2 -0.2 -0.2 -0.2 -0.4 -0.2 0.0 0.2 0.0 -0.4 0.2 0.2 0.0 -1.2 -0.6 0.0 A 0.5 -0.9 0.6 0.4 -1.0 0.1 0.3 -0.4 0.1 -0.7 -0.5 0.4 -0.2 0.3 -0.1 0.1 0.0 -0.4 -1.1 -0.6 0.4 B 2.4 -1.0 -1.0 -0.8 -0.6 -0.6 -0.4 -1.0 -1.2 -1.0 -0.8 -0.6 -1.0 -0.8 0.0 -0.4 -0.4 -1.6 0.0 -1.0 C 0.8 0.6 -1.2 0.2 0.2 -0.4 0.0 -0.8 -0.6 0.4 -0.2 0.4 -0.2 0.0 0.0 -0.4 -1.4 -0.8 0.5 D 0.8 -1.0 0.0 0.2 -0.4 0.0 -0.6 -0.4 0.2 -0.2 0.4 -0.2 0.0 0.0 -0.4 -1.4 -0.8 0.6 E 1.8 -1.0 -0.4 0.2 -1.0 0.4 0.0 -0.8 -1.0 -1.0 -0.8 -0.6 -0.6 -0.2 0.0 1.4 -1.0 F 1.0 -0.4 -0.6 -0.4 -0.8 -0.6 0.0 -0.2 -0.2 -0.6 0.2 0.0 -0.2 -1.4 -1.0 -0.1 G 1.2 -0.4 0.0 -0.4 -0.4 0.4 0.0 0.6 0.4 -0.2 -0.2 -0.4 -0.6 0.0 -0.4 H 1.0 -0.4 0.4 0.4 -0.4 -0.4 -0.4 -0.4 -0.2 0.0 0.8 -1.0 -0.2 -0.4 I 1.0 -0.6 0.0 0.2 -0.2 0.2 0.6 0.0 0.0 -0.4 -0.6 -0.8 0.1 K 1.2 0.8 -0.6 -0.6 -0.4 -0.6 -0.6 -0.4 0.4 -0.4 -0.2 -0.5 L 1.2 -0.4 -0.4 -0.2 0.0 -0.4 -0.2 0.4 -0.8 -0.4 -0.3 M 0.4 -0.2 0.2 0.0 0.2 0.0 -0.4 -0.8 -0.4 0.2 N 1.2 0.0 0.0 0.2 0.0 -0.2 -1.2 -1.0 -0.1 P 0.8 0.2 -0.2 -0.2 -0.4 -1.0 -0.8 0.6 Q 1.2 0.0 -0.2 -0.4 0.4 -0.8 0.6 R 0.4 0.2 -0.2 -0.4 -0.6 -0.1 S 0.6 0.0 -1.0 -0.6 -0.1 T 0.8 -1.2 -0.4 -0.4 V 3.4 0.0 -1.2 W 2.0 -0.8 Y 0.6 Z
    • Overview • Some of the properties go into the makeup of PAM matrices are - amino acid residue size, shape, local concentrations of electric charge, van der Waals surface, ability to form salt bridges, hydrophobic interactions, and hydrogen bonds. – These patterns are imposed principally by natural selection and only secondarily by the constraints of the genetic code. – Coming up with one’s own matrix of weights based on some logical features may not be very successful because your logical features may have been over- written by other more important considerations.
    • Principles of Scoring Matrix Construction • Two aspects of this process cause the evolutionary distance to be unequal in general to the number of observed differences between the sequences: – First, there is a chance that a certain residue may have mutated, than reverted, hiding the effect of the mutation. – Second, specific residues may have mutated more than once, thus the number of point mutations is likely to be larger than the number of differences between the two sequences..
    • Similarity ve. distance
    • Experiment: pam-simulator.pl• Initialize: – Generate Random protein (1000 aa)• Simulate evolution (eg 250 for PAM250) – Apply PAM1 Transition matrix to each amino acid – Use Weighted Random Selection• Iterate – Measure difference to orginal protein
    • Dayhoff’s PAM1 mutation probability matrix (Transition Matrix) A R N D C Q E G H I Ala Arg Asn Asp Cys Gln Glu Gly His Ile 9867 2 9 10 3 8 17 21 2 6 A 1 9913 1 0 1 10 0 0 10 3 R 4 1 9822 36 0 4 6 6 21 3 N 6 0 42 9859 0 6 53 6 4 1 D 1 1 0 0 9973 0 0 0 1 1 C 3 9 4 5 0 9876 27 1 23 1 Q 10 0 7 56 0 35 9865 4 2 3 E 21 1 12 11 1 3 7 9935 1 0 G 1 8 18 3 1 20 1 0 9912 0 H 2 2 3 1 2 1 2 0 0 9872 I
    • Weighted Random Selection• Ala => Xxx (%) A R N D C Q E G H I L K M F P S T W Y V
    • PAM-Simulator PAM-simulator 120 100 80 %identity 60 40 20 0 0 50 100 150 200 250 300 PAM
    • PAM-Simulator PAM-Simulator 100 90 80 70 60 % identity 50 40 30 20 10 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 PAM
    • Some PAM values and their corresponding observed distances PAM Value Distance(%) 80 50 100 60 200 75 250 85 <- Twilight zone 300 92 (From Doolittle, 1987, Of URFs and ORFs, University Science Books)•When the PAM distance value between two distantly related proteins nears the value 250 itbecomes difficult to tell whether the two proteins are homologous, or that they are two atrandomly taken proteins that can be aligned by chance. In that case we speak of the twilightzone.•The relation between the observed percentage in distance of two sequences versus PAMvalue. Two randomly diverging sequences change in a negatively exponential fashion. Afterthe insertion of gaps to two random sequences, it can be expected that they will be 80 - 90 %dissimilar (from Doolittle, 1987 ).
    • Overview • Creation of a pam series from evolutionary simulations • pam2=pam1^2 • pam3=pam1^3 • And so on… • pam30,60,90,120,250,300 • low pam - closely related sequences – high scores for identity and low scores for substitutions - closer to the identity matrix • high pam - distant sequences – at pam2000 all information is degenerate except for cysteins • pam250 is the most popular and general – one amino acid in five remains unchanged (mutability varies among the amino acids)
    • Overview250 PAM evolutionary distance A R N D C Q E G H I L K M F PAla A 13 6 9 9 5 8 9 12 6 8 6 7 7 4 11Arg R 3 17 4 3 2 5 3 2 6 3 2 9 4 1 4Asn N 4 4 6 7 2 5 6 4 6 3 2 5 3 2 4Asp D 5 4 8 11 1 7 10 5 6 3 2 5 3 1 4Cys C 2 1 1 1 52 1 1 2 2 2 1 1 1 1 2Gln Q 3 5 5 6 1 10 7 3 7 2 3 5 3 1 4Glu E 5 4 7 11 1 9 12 5 6 3 2 5 3 1 4Gly G 12 5 10 10 4 7 9 27 5 5 4 6 5 3 8His H 2 5 5 4 2 7 4 2 15 2 2 3 2 2 3Ile I 3 2 2 2 2 2 2 2 2 10 6 2 6 5 2Leu L 6 4 4 3 2 6 4 3 5 15 34 4 20 13 5Lys K 6 18 10 8 2 10 8 5 8 5 4 24 9 2 6Met M 1 1 1 1 0 1 1 1 1 2 3 2 6 2 1Phe F 2 1 2 1 1 1 1 1 3 5 6 1 4 32 1Pro P 7 5 5 4 3 5 4 5 5 3 3 4 3 2 20Ser S 9 6 8 7 7 6 7 9 6 5 4 7 5 3 9Thr T 8 5 6 6 4 5 5 6 4 6 4 6 5 3 6Trp W 0 2 0 0 0 0 0 0 1 0 1 0 0 1 0Tyr Y 1 1 2 1 3 1 1 1 3 2 2 1 2 15 1Val V 7 4 4 4 4 4 4 4 5 4 15 10 4 10 5[column on left represents the replacement amino acid]Mutation probability matrix for the evolutionary distance of 250 PAMs. To simplify the appearance, the elements are shown multiplied by 100.In comparing two sequences of average amino acid frequency at this evolutionary distance, there is a 13% probability that a position containing Ala in the first sequence will contain Ala in the second. There is a 3% chance that it will contain Arg, and so forth.
    • A brief history of time (BYA)Origin of Earliest Origin of Fungi/animal life fossils eukaryotes Plant/animal insects 4 3 2 1 0 BYA
    • Margaret Dayhoff’s 34 protein superfamilies Protein PAMs per 100 million years Ig kappa chain 37 Kappa casein 33 Lactalbumin 27 Hemoglobin 12 Myoglobin 8.9 Insulin 4.4 Histone H4 0.10 Ubiquitin 0.00
    • Sources of error Many sequences depart from average composition. Rare replacements were observed too infrequently to resolve relative probabilities accurately (for 36 pairs no replacements were observed!). Errors in 1PAM are magnified in the extrapolation to 250PAM. Distantly related sequences usually have islands (blocks) of conserved residues. This implies that replacement is not equally probable over entire sequence.
    • Overview • Simple identity, which scores only identical amino acids as a match. • Genetic code changes, which scores the minimum number of nucieotide changes to change a codon for one amino acid into a codon for the other. • Chemical similarity of amino acid side chains, which scores as a match two amino acids which have a similar side chain, such as hydrophobic, charged and polar amino acid groups. • The Dayhoff percent accepted mutation (PAM) family of matrices, which scores amino acid pairs on the basis of the expected frequency of substitution of one amino acid for the other during protein evolution. • The blocks substitution matrix (BLOSUM) amino acid substitution tables, which scores amino acid pairs based on the frequency of amino acid substitutions in aligned sequence motifs called blocks which are found in protein families
    • BLOSUM: Blocks Substitution Matrix • Henikoff & Henikoff (Henikoff, S. & Henikoff J.G. (1992) PNAS 89:10915- 10919) • asking about the relatedness of distantly related amino acid sequences ? • They use blocks of sequence fragments from different protein families which can be aligned without the introduction of gaps. These sequence blocks correspond to the more highly conserved regions.
    • BLOSUM (BLOck – SUM) scoring Block = ungapped alignent Eg. Amino Acids D N V A S = 3 sequences W = 6 aa N= (W*S*(S-1))/2 = 18 pairs a b c d e f 1 DDNAAV 2 DNAVDD 3 NNVAVV
    • A. Observed pairs a b c d e f 1 DDNAAV 2 DNAVDD f fij D N A V 3 NNVAVV D N 1 4 1 A 1 1 1 V 3 1 4 1 Relative frequency table gij D N A V D .056 Probability of obtaining a pair /18 N .222 .056 if randomly choosing pairs A .056 .056 .056 from block V .167 .056 .222 .056
    • B. Expected pairs A Pi DDDDD 5/18 DDNAAV NNNN 4/18 DNAVDD AAAA 4/18 NNVAVV VVVVV 5/18 P{Draw DN pair}= P{Draw D, then N or Draw M, then D} P{Draw DN pair}= PDPN + PNPD = 2 * (5/18)*(4/18) = .123 Random rel. frequency table eij D N A V D .077 Probability of obtaining a pair of N .123 .049 each amino acid drawn A .154 .123 .049 independently from block V .123 .099 .123 .049
    • C. Summary (A/B) sij = log2 gij/eij (sij) is basic BLOSUM score matrix Notes: • Observed pairs in blocks contain information about relationships at all levels of evolutionary distance simultaneously (Cf: Dayhoffs’s close relationships) • Actual algorithm generates observed + expected pair distributions by accumalution over a set of approx. 2000 ungapped blocks of varrying with (w) + depth (s)
    • The BLOSUM Series • blosum30,35,40,45,50,55,60,62,65,70,75,80,85,90 • transition frequencies observed directly by identifying blocks that are at least – 45% identical (BLOSUM-45) – 50% identical (BLOSUM-50) – 62% identical (BLOSUM-62) etc. • No extrapolation made • High blosum - closely related sequences • Low blosum - distant sequences • blosum45  pam250 • blosum62  pam160 • blosum62 is the most popular matrix
    • Overview
    • • Church of the Flying Spaghetti Monster• http://www.venganza.org/about/open-letter
    • Overview • Which matrix should I use? – Matrices derived from observed substitution data (e.g. the Dayhoff or BLOSUM matrices) are superior to identity, genetic code or physical property matrices. – Schwartz and Dayhoff recommended a mutation data matrix for the distance of 250 PAMs as a result of a study using a dynamic programming procedure to compare a variety of proteins known to be distantly related. • The 250 PAM matrix was selected since in Monte Carlo studies matrices reflecting this evolutionary distance gave a consistently higher significance score than other matrices in the range 0.750 PAM. The matrix also gave better scores when compared to the genetic code matrix and identity scoring.
    • Which matrix should I use? • When comparing sequences that were not known in advance to be related, for example when database scanning: – default scoring matrix used is the BLOSUM62 matrix – if one is restricted to using only PAM scoring matrices, then the PAM120 is recommended for general protein similarity searches • When using a local alignment method, Altschul suggests that three matrices should ideally be used: PAM40, PAM120 and PAM250, the lower PAM matrices will tend to find short alignments of highly similar sequences, while higher PAM matrices will find longer, weaker local alignments.
    • Rat versus Rat versusmouse RBP bacterial lipocalin
    • Overview – Henikoff and Henikoff have compared the BLOSUM matrices to PAM by evaluating how effectively the matrices can detect known members of a protein family from a database when searching with the ungapped local alignment program BLAST. They conclude that overall the BLOSUM 62 matrix is the most effective. • However, all the substitution matrices investigated perform better than BLOSUM 62 for a proportion of the families. This suggests that no single matrix is the complete answer for all sequence comparisons. • It is probably best to compliment the BLOSUM 62 matrix with comparisons using 250 PAMS, and Overington structurally derived matrices. – It seems likely that as more protein three dimensional structures are determined, substitution tables derived from structure comparison will give the most reliable data.
    • Overview • Introduction – Short recap on databases – Definitions • Scoring Matrices – Theoretical – Empirial • PAM (pam-simulator.pl) • BLOSUM • Pairwise alignmentOverview – Dot-plots (dotplot-simulator.pl)
    • Dotplots• What is it ? – Graphical representation using two orthogonal axes and “dots” for regions of similarity. – In a bioinformatics context two sequence are used on the axes and dots are plotted when a given treshold is met in a given window.• Dot-plotting is the best way to see all of the structures in common between two sequences or to visualize all of the repeated or inverted repeated structures in one sequence
    • Dot Plot ReferencesGibbs, A. J. & McIntyre, G. A. (1970). The diagram method for comparing sequences. its use with amino acid and nucleotide sequences. Eur. J. Biochem. 16, 1-11.Staden, R. (1982). An interactive graphics program for comparing and aligning nucleic-acid and amino-acid sequences. Nucl. Acid. Res. 10 (9), 2951-2961.
    • Visual Alignments (Dot Plots)• Matrix – Rows: Characters in one sequence – Columns: Characters in second sequence• Filling – Loop through each row; if character in row, col match, fill in the cell – Continue until all cells have been examined
    • Dotplot-simulator.plprint " $seq1n";for(my $teller=0;$teller<=$seq2_length;$teller++){ print substr($seq2,$teller,1); $w2=substr($seq2,$teller,$window); for(my $teller2=0;$teller2<=$seq_length;$teller2++){ $w1=substr($seq1,$teller2,$window); if($w1 eq $w2){print "*";}else{print " ";} } print"n"; }
    • Overview Window size = 1, stringency 100%
    • Noise in Dot Plots• Nucleic Acids (DNA, RNA) – 1 out of 4 bases matches at random• Stringency – Window size is considered – Percentage of bases matching in the window is set as threshold
    • Reduction of Dot Plot Noise Self alignment of ACCTGAGCTCACCTGAGTTA
    • Dotplot-simulator.plExample: ZK822 Genomic and cDNAGene prediction: How many exons ? Confirm donor and aceptor sites ?Remember to check the reverse complement !
    • Chromosome Y self comparison
    • Overview • Regions of similarity appear as diagonal runs of dots • Reverse diagonals (perpendicular to diagonal) indicate inversions • Reverse diagonals crossing diagonals (Xs) indicate palindromes • A gap is introduced by each vertical or horizontal skip
    • Overview • Window size changes with goal of analysis – size of average exon – size of average protein structural element – size of gene promoter – size of enzyme active site
    • Overview Rules of thumb Dont get too many points, about 3- 5 times the length of the sequence is about right (1-2%) Window size about 20 for distant proteins 12 for nucleic acid Check sequence vs. itself Check sequence vs. sequence Anticipate results (e.g. “in-house” sequence vs genomic, question)
    • Available Dot Plot Programs Dotlet (Java Applet) http://www.isrec.isb- sib.ch/java/dotlet/Dotlet. html
    • Available Dot Plot Programs Dotter (http://www.cgr.ki.se/cgr/groups/sonnhammer/Dotter.html)
    • Available Dot Plot Programs EMBOSS DotMatcher, DotPath,DotUp
    • Weblems • W3.1: Why does 2 PAM, i.e. 1 PAM multiplied with itself, not correspond to exactly 2% of the amino acids having mutated, but a little less than 2% ? Or, in other words, why does a 250 PAM matrix not correspond to 250% accepted mutations ? • W3.2: Is it biologically plausible that the C-C and W-W entries in the scoring matrices are the most prominent ? Which entries (or groups of entries) are the least prominent ? • W3.3: What is OMIM ? How many entries are there ? What percentage of OMIM listed diseases has no known (gene) cause ? • W3.4: Pick one disease mapped to chromosome Y from OMIM where only a mapping region is known. How many candidate genes can you find in the locus using ENSEMBL ? Can you link ontology terms for the candidates to the disease phenotype ?