©CMBI 2005 Transfer of information The main topic of this course is transfer of information. A month in the lab can easily save you an hour in front of the computer. Nothing is impossible for a man who doesn’t have to do it himself. But, to err is human, but to really screw things up, you need a computer.

©CMBI 2005 Transfer of information The main topic of this course is transfer of information. In the protein world that leads to the questions: 1)From which protein can I transfer information 2)How do I transfer what information from where to wher Today’s answer is BLAST…

©CMBI 2005 Database Searching with BLAST Database searching with BLAST involves a series of topics we will deal with today: Database Searching Sequence Alignment Scoring Matrices Significance of an alignment and: BLAST, algorithm BLAST, parameters BLAST, output

©CMBI 2005 Database Searching Identify similarities between: your query sequence likely with unknown structure and function database subject sequences with elucidated structures and function

©CMBI 2005 Database searching concept The query sequence is compared/aligned with every subject sequence in the database. High-scoring database sequences are assumed to be evolutionary related to the query sequence. If sequences are related by divergence from a common ancestor, there are said to be homologous. We can only transfer information between homologs. (And we will learn later that that is because structure is maintained longer during evolution than sequence).

©CMBI 2005 Transfer of information We want to be able to say things like “this serine is phorphorylated in the database protein, so in my homologous protein the corresponding serine is likely to be phosphorylated too”. That requires that the green serine and the purple serine both come from a common ancestor that was phosphorylated too. And that, in turn, requires that both serines are located at the same location in their respective structures.

©CMBI 2005 Equivalent structural positions To know if positions in two different proteins are equivalent, we need to know both protein structures and compare them with protein structure comparison software. But by the time you have solved one or two protein structures the four years of your PhD period are over... So, we need a short-cut, and that, ladies and gentleman, will be a sequence alignment (i.e. Blast +...).

©CMBI 2005 Sequence alignment Sequence alignment is a simple concept. You only have to find out which pairs of residues in two homologous sequences are derived from the same residue in the common ancestor. TTSASDFRTRTTHIKILLMRL STSATSYRTRSTHLRLMLMRI seems easy, but: ASDFTHGTREWDSTYHLIMNV LTEYSHNSKDFETSFNILLQL looks very hard... (Still, both alignments seem correct to me, and four weeks from now, you will agree, I hope).

©CMBI 2005 Sequence alignment is easy: You only need three things: 1)A computer program that produces all possible alignments, and 2)A computer program that gives each alignment a score, and, the simplest, 3)A computer program that selects the highest scoring alignment from the very large number you tried. (The next two weeks you will learn that only point 2 is difficult)

©CMBI 2005 Scoring Matrix/Substitution Matrix To score the quality of an alignment you need ‘something’ that compares amino acids, a matrix. Contains scores for pairs of residues So, for protein/protein comparisons we need a 20 x 20 matrix of similarity scores where identical amino acids and those of similar character give higher scores compared to those of different character. (And next week you will learn which residues are similar)

©CMBI 2005 Substitution Matrices Not all amino acids are equal Residues mutate more easily to similar ones Residues at surface mutate more easily Aromatics mutate preferably into aromatics Mutations tend to favor some substitutions Core tends to be hydrophobic Selection tends to favor some substitutions Cysteines are dangerous at the surface Cysteines in bridges seldom mutate

©CMBI 2005 PAM250 Matrix

©CMBI 2005 Scoring example Score of an alignment is the sum of the scores of all pairs of residues in the alignment sequence 1: TCCPSIVARSN sequence 2: SCCPSISARNT => score = 46

©CMBI 2005 Dayhoff Matrix (1) The group of Dayhoff created a scoring matrix from a dataset of closely similar protein sequences that could be aligned unambiguously. Then they counted all mutations (and non-mutations) and calculated the mutation frequencies With a bit of math, they converted these frequencies into the famous Dayhoff matrix (also called PAM matrix).

©CMBI 2005 Given the frequency of Leu and Val in my sequences, and the frequency of mutations,, do I see more mutations of V  L than I would expect by chance alone? Score of mutation A  B = log ( observed a  b mutation / expected a  b mutations ) This is called a log odd and can be negative, zero, or positive. Zero means no information, no contribution to the score of the alignment. When using a log odds matrix, the total score of the alignment is given by the sum of the scores for each aligned pair of residues. Dayhoff Matrix (2)

©CMBI 2005 Dayhoff Matrix (3) This log odds matrix is called PAM 1. An evolutionary distance of 1 PAM (point accepted mutation) means there has been 1 point mutation per 100 residues PAM 1 may be used to generate matrices for greater evolutionary distances by multiplying it repeatedly by itself. PAM250: –2,5 mutations per residue. –equivalent to 20% matches remaining between two sequences, i.e. 80% of the amino acid positions are observed to have changed (one or more times). –is default in many analysis packages.

©CMBI 2005 BLOSUM Matrix Limit of Dayhoff matrix: Matrices based on the Dayhoff model of evolutionary rates are derived from alignments of sequences that are at least 85% identical; that might not be optimal… An alternative approach has been developed by Henikoff and Henikoff using local multiple alignments of more distantly related sequences. All matrices are symmetrical...

©CMBI 2005 BLOSUM Matrix (2) The BLOSUM matrices (BLOcks SUbstitution Matrix) are based on the BLOCKS database. The BLOCKS database utilizes the concept of blocks (un-gapped amino acid pattern), that act as signatures of a family of proteins. Substitution frequencies for all pairs of amino acids were then calculated and this used to calculate a log odds BLOSUM matrix. Different matrices are obtained by varying the identity threshold. For example, BLOSUM80 was derived using blocks of 80% identity.

Which Matrix to use? Close relationships (Low PAM, high Blosum) Distant relationships (High PAM, low Blosum) Often used defaults are: PAM250, BLOSUM62 BLOSUM 80BLOSUM 62BLOSUM 45 PAM 20PAM 120PAM 250 More conservedMore variable

©CMBI 2005 Significance of alignment (1) When is an alignment statistically significant? In other words: How much different is the alignment score found from scores obtained by aligning any odd sequences to the query sequence? Or: What is the probability that an alignment with this score could have arisen by chance?

©CMBI 2005 Significance of alignment (2) Database size= 20 x 10 6 amino acids peptide#hits A1 x 10 6 AP50000 IAP2500 LIAP125 WLIAP6 KWLIAP0,3 KWLIAPY0,015

©CMBI 2005 BLAST Question: What database sequences are most similar to (or contain the most similar regions to) my own sequence? BLAST finds the highest scoring locally optimal alignments between a query sequence and all database sequences. Very fast algorithm Can be used to search extremely large databases Sufficiently sensitive and selective for most purposes Robust – the default parameters can usually be used

©CMBI 2005 BLAST – Algorithme Step 1: Read/understand user query sequence. Step 2: Use hashing technology to select several thousand likely candidates. Step 3: Do a real alignment between the query sequence and those likely candidate. ‘Real alignment’ is a main topic of this course. Step 4: Present output to user.

©CMBI 2005 BLAST Algorithm, Step 2 The program first looks for series of short, highly similar fragment, it extends these matching segments in both directions by adding residues. Residues will be added until the incremental score drops below a threshold.

©CMBI 2005 Basic BLAST Algorithms ProgramQueryDatabase BLASTPProtein BLASTNDNA BLASTXtranslatedDNAprotein TBLASTNproteintranslatedDNA TBLASTXtranslatedDNA

©CMBI 2005 PSI-BLAST Position-Specific Iterated BLAST Distant relationships are often best detected by motif or profile searches rather than pair-wise comparisons PSI-BLAST first performs a BLAST search. PSI-BLAST uses the information from significant BLAST alignments returned to construct a position specific score matrix, which replaces the query sequence for the next round of database searching. PSI-BLAST may be iterated until no new significant alignments are found.

©CMBI 2005 BLAST Input Steps in running BLAST: Entering your query sequence (cut-and-paste) Select the database(s) you want to search And, optionally: Choose output parameters Choose alignment parameters (scoring matrix, filters,….) Example query= >something AFIWLLSCYALLGTTFGCGVNAIHPVLTGLSKIVNGEEAVPGTWPWQVTLQDRSGFHFC GGSLISEDWVVTAAHCGVRTSEILIAGEFDQGSDEDNIQVLRIAKVFKQPKYSILTVNND ITLLKLASPARYSQTISAVCLPSVDDDAGSLCATTGWGRTKYNANKSPDKLERAALPLLT NAECKRSWGRRLTDVMICGAASGVSSCMGDSGGPLVCQKDGAYTLVAIVSWASDTCSASS GGVYAKVTKIIPWVQKILSSN

©CMBI 2010 BLAST Output A high score indicates a likely relationship A low probability indicates that a match is unlikely to have arisen by chance

©CMBI 2010 BLAST Output Low scores with high probabilities suggest that matches have arisen by chance

©CMBI 2005 Alignment Significance in BLAST P-value (probability) Relates the score for an alignment to the likelihood that it arose by chance. The closer to zero, the greater the confidence that the hit is real. E-value (expect value) The number of alignments with E that would be expected by chance in that database (e.g. if E=10, 10 matches with scores this high are expected to be found by chance). A match will be reported if its E is below the threshold. Lower E thresholds are more stringent, and report fewer matches.

©CMBI 2010 BLAST result: easy

©CMBI 2010 BLAST result: less easy

©CMBI 2010 BLAST result: very difficult

©CMBI 2005 Low complexity filter Many sequences contain repeats or stretches that consist predominantly of one type of amino acid. E.g. Many nuclear proteins have a poly-asparagine tail, membrane proteins often consist of mainly hydrophobic amino acids, or many binding proteins have proline rich stretches. ASDFGTRGHPPPPPPPPPPP NPPPPPPPPPLTSSDFRGT Are NOT homologs, but analogs.

NNNNNNNN ©CMBI 2010 BLAST - Low complexity filter Filter ON Filter OFF NNNNNNNN Your BLAST query sequence will look like this:

©CMBI 2005 Demo IJs, CNCZ, en het internet dienende komt nu een demo…