August 26, 2011 Biochemistry 201 David Worthylake, 7152 MEB, x5176

1. Sequence Alignments and Database Searching August 26, 2011 Biochemistry 201 David Worthylake, 7152 MEB, x5176

2. Protein A of interest to you. ornithine decarboxylase? Why compare protein sequences? Significant sequence similarities allow associations based upon known functions.

3. Homology vs. similarity Possible for proteins to possess high sequence identity/ similarity between segments and not be homologous Homologous proteins (ie having similar structures) need not posess high sequence identity / similarity: S. griseus trypsin 36% S. griseus protease A 25% cytochrome c4, has reasonably high sequence identity/ similarity with trypsins, yet does not have common ancestor, nor common fold. subtilisin has same spatial arrangement of active site residues, but is not related to trypsins Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia

4. Homology vs. similarity • Homologous proteins always share a commonthree- • dimensional fold, often with common active or binding site. • Proteins that share a common ancestor are homologous. • Proteins that possess >25% identity across entirelength generally will be homologous (but there can be exceptions). • Proteins with <20% identity are not necessarily not homologous

5. Homology vs. similarity Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia Orthologous cyctochrome c isozymes Homologous sequences are either: 1) orthologous, or 2) paralogous Orthologs - sequence differences arises from divergence in different species (i.e. cyctochrome c) Paralogs - sequence differences arise after gene duplication within a given species (i.e. GPCRs, hemoglobins) Hemoglobins contain both orthologs and paralogs • For orthologs - sequence divergence and evolutionary relationships will agree. • For paralogs - no necessary linkage between sequence divergence and speciation.

6. We’ve all seen and/or used sequence alignments, but how are they accomplished? Sequence searches and alignments using DNA/RNA are usually not as informative as searches and alignments using protein sequences. However. DNA/RNA searches are intuitively easier to understand: AGGCTTAGCAAA........TCAGGGCCTAATGCG |||||||| ||| ||||||||||| ||| AGGCTTAGGAAACTTCCTAGTCAGGGCCTAAAGCG The above alignment could be scored giving a “1” for each identical nucleotide, A zero for a mismatch, and a -4 for “opening a “gap” and a -1 for each extension of the gap. So score = 25 – 11= 14

7. Protein sequence alignments are much more complicated. How would this alignment be scored? ARDTGQEPSSFWNLILMY.........DSCVIVHKKMSLEIRVH | | | | | ||| | | || ||| AKKSAEQPTSYWDIVILYESTDKNDSGDSCTLVKKRMSIQLRVH Unlike nucleotide sequence alignments, which are either identical or not identical at a given position, protein sequence alignments include “shades of grey” where one might acknowledge that a T is sort of equivalent to an S etc. But how equivalent? What number would you assign to an S-T mismatch? And what about gaps? Since alanine is a common amino acid, couldn’t the A-A match be by chance? Since Trp and Cys are uncommon, should those matches be given higher scores? Do you see that accurately aligning sequences and accurately finding related sequences are  the same problem?

8. Global versus local alignments BLAST Needleman-Wunsch • Global scores require alignment of entire sequence length. • Cannot be used to detect relationships between domains in mosaic proteins. Local alignments are necessary to detect domains within mosaic proteins, internal duplications. Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia

9. Databases Nucleotide: GenBank (NCBI), EMBL, DDBJ (Japan) Protein: SwissProt, TrEMBL, GenPept(GenBank) Huge databases – share much information. Many entries linked to other databases (e.g. PDB). SwissProt small but well “curated”. NCBI non-redundant (nr) protein sequence database is very large but sometimes confusing. These databases can be searched in a number of ways. Can search only human or metazoan sequences. Can eliminate entries made before a given Date. Etc.

10. We’ve got the lots of sequences, now how do we score/search? First, we need a way to assign numbers to “shades of grey” matches. Genetic code scoring system – This assumes that changes in protein sequence arise from mutations. If only one point mutation is needed to change a given AA to another (at a specific position in alignment), the two amino-acids are more closely related than if two point mutations were required. Physicochemical scoring system – a Thr is like a Ser, a Trp is not like an Ala…… These systems are seldom used because they have problems. Why try to second guess Nature? Since there are many related sequences out there, we can look at some (trusted) alignments to SEE which sub- stitutions have occurred and the frequency with which they occur.

11. PAM (Point Accepted Mutation) matrices • Are derived from studying global alignments of well-characterized protein families. • PAM1 = only 1% of residues has changed (ie short evolutionary distance) • Raise this to 250 power to get 250% change of two sequences (greater • evolutionary distance), or about 20% sequence identity. • Therefore, • a PAM 30 would be used to analyze more closely related proteins, • a PAM 400 is used for finding and analyzing very distantly related proteins. • PAMx = PAM1x • (Dayhoff, Atlas of Protein Sequence and Structure, vol. 5, suppl 3, p 345-352)

12. Block substitution matrices (BLOSUM) Arederived from studying local alignments (blocks) of sequences from related proteins that differ by no more than X%. (Henikoff & Henikoff, PNAS ‘92, 89, p10915-10919) In other words, one might use the portions of aligned sequences from related proteins that have no more than 62% identity (in the portions or blocks) to derive the BLOSUM 62 scoring matrix. One might use only the blocks that have <80% identity to derive the BLOSUM 80 matrix. 3) BLOSUM and PAM substitution matrices have the opposite effects: The higher the number of the BLOSUM matrix (BLOSUM X), the more closely related proteins you are looking for. The higher the number of the PAM matrix (PAM X), the more distantly related proteins you are looking for.

13. Amino acid substitution matrices • Negative scores - unlikely substitutions Note that for identical matches, scores vary depending upon observed frequencies. That is, rare amino acid (i.e. Trp) that are not substituted have high scores; frequently occuring amino acids (i.e. Ala) are down-weighted because of the high probability of aligning by chance. PAM250 matrix Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia

14. Gap penalties – Intuitively one recognizes that there should be a penalty for introducing (requiring) a gap during identification/alignment of a given sequence. But if two sequences are related, the gaps may well be located in loop regions which are more tolerant of mutational events and probably have little impact on structure. Therefore, a new gap should be penalized, but extending an existing gap should be penalized very little. Filtering – many proteins and nucleotides contain simple repeats or regions of low sequence complexity. These must be excluded from searches and alignments. Why? Significance of a “hit” during a search - More important than an arbitrary score is an estimation of the likelihood of finding a hit through pure chance. Ergo the “Expectation value” or E-value. E-values can be as low as 0 for Identical (long) match (e.g. a 250 AA protein finding itself in search).

15. E-value So, for sufficiently large databases (so one can apply statistics): E = Kmne-S m- query length n - database length E - expectation value K - scale factor for query sequence (AA composition)  - scale factor for scoring system (e.g. PAM250) S - score, dependent on substitution matrix, gap-penalties, etc. Doubling either m or n doubles number of sequences returned with a given expectation value; similarly, double the score and expectation value decreases exponentially Expectation value - probability that given score will occur by chance given the query AND database “strings”

16. Removing length bias from scoring statistics • Must account for increases in similarity score due to increase in sequence length searched. • Scaling the sequence length allows the detection of distantly-related sequences. • solids = individual sequences • opens = average score Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia

17. Global versus local alignments • Global scores require alignment of entire sequence length. • Cannot be used to detect relationships between domains in mosaic proteins. Local alignments are necessary to detect domains within mosaic proteins, internal duplications. Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia

18. Basic local alignment search tool (BLAST) • Break query up into “words” e.g. ASTGHKDLLV • AST • WORDS STG • TGH • 2) Generate expanded list of words that would match with (i.e. PAM250) • a score of at least T – You’re acknowledging that you may not have any • exact matches with original list of words. • 3) Use expanded list of words to search database for exact matches. • 4) Extend alignments from where word(s) found exact match. Heuristic algorithm – Uses guesses. Increases speed without a great loss of accuracy (BLASTP, FASTA (local Hueristic), S-W (local rigorous), Needleman-Wunsch (global, rigorous)

19. Pictorial representation of BLASTp algorithm (Basic Local Alignment Search Tool proteins). Query sequence Words (they overlap) Expand list of words (each word (left) has “similar” words) Search database, find hits, extend alignments Report sorted list of hits

20. BLAST ATCGCCATGCTTAATTGGGCTT CATGCTTAATT exact word match one hit Nucleotide BLAST looks for exact matches Protein BLAST (BLASTp) requires two hits GTQITVEDLFYNI SQI YYN neighborhood words NCBI two hits

21. FASTA Instead of breaking up query into words (and then generating a list of similar words), find all sequences in the database that contain short sequences that are exact or nearly exact matches for sequences within the query. Score these and sort. Sort of reverse methodology to BLAST Database sequence Query sequence

23. Protein database

25. mouse over

26. sorted by e values λS - lnK S’ = 5 x e-98 ln2 link to entrez E = mn 2-S’ = Kmne-λS Gene

27. Identifying distant homologies (use several different query sequences) Also remember - If A is homologous to B, and B to C, then A should be homologous to C Examine output carefully. A lack of statistical significance doesn’t necessarily mean a lack of homology! Extracted from ISMB2000 tutorial, WR Pearson, U. of Virginia

28. PSI-BLASTp Very sensitive, but must not include a non-member sequence! • Regular BLASTp search • Sequences above a certain threshold (< specified E-value) are • included. Assumed to be related proteins. This group of sequences • is used to define a “profile” that contains the sequence “essence” of • the protein family. • Now with the important sequence positions highlighted, can look • for more distantly related sequences that should still have the “essence” • of the protein family. • Inclusion of more distantly related sequences modifies the profile • further (further defines the essence) and allows for identification of • even more distantly related sequences. Etc. Note: PSI-BLASTp may find and then subsequently lose a homologous sequence during the iteration process! “Drifting” of the program, would be the gradual loss of distant homologs during the iteration process.

29. >gi|113340|sp|P03958|ADA_MOUSE ADENOSINE DEAMINASE (ADENOSINE AMINOH MAQTPAFNKPKVELHVHLDGAIKPETILYFGKKRGIALPADTVEELRNIIGMDKPLSLPGFLAKFDYY VIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVDPMPWNQTEGDVTPDDVVDLVNQGLQ EQAFGIKVRSILCCMRHQPSWSLEVLELCKKYNQKTVVAMDLAGDETIEGSSLFPGHVEAYEGAVKNG RTVHAGEVGSPEVVREAVDILKTERVGHGYHTIEDEALYNRLLKENMHFEVCPWSSYLTGAWDPKTTH VRFKNDKANYSLNTDDPLIFKSTLDTDYQMTKKDMGFTEEEFKRLNINAAKSSFLPEEEKKELLERLY PSI-BLAST: initial run NCBI e value cutoff for inclusion

30. PSI-BLAST: initial run NCBI

31. PSI-BLAST: first PSSM search Other purine nucleotide metabolizing enzymes not found by ordinary BLAST Note: These E-values are different from usual BLASTp because of position-specific scoring matrix (later). NCBI

32. PSI-BLAST: importance of original query (remember, if A is like B….) iteration 1 iteration 2 PSI-Blast of human Tiam1

33. PSI-BLAST: importance of original query iteration 1 iteration 2 PSI-Blast of mouse Tiam2 (~90% identity with human Tiam1) Ras-binding domains iteration 3

34. Weakly conserved serine Active site serine Position specific scoring matrix (PSSM) (learning from your “hits”) NCBI

35. Position specific scoring matrix (PSSM) A R N D C Q E G H I L K M F P S T W Y V 206 D 0 -2 0 2 -4 2 4 -4 -3 -5 -4 0 -2 -6 1 0 -1 -6 -4 -1 207 G -2 -1 0 -2 -4 -3 -3 6 -4 -5 -5 0 -2 -3 -2 -2 -1 0 -6 -5 208 V -1 1 -3 -3 -5 -1 -2 6 -1 -4 -5 1 -5 -6 -4 0 -2 -6 -4 -2 209 I -3 3 -3 -4 -6 0 -1 -4 -1 2 -4 6 -2 -5 -5 -3 0 -1 -4 0 210 S -2 -5 0 8 -5 -3 -2 -1 -4 -7 -6 -4 -6 -7 -5 1 -3 -7 -5 -6 211 S 4 -4 -4 -4 -4 -1 -4 -2 -3 -3 -5 -4 -4 -5 -1 4 3 -6 -5 -3 212 C -4 -7 -6 -7 12 -7 -7 -5 -6 -5 -5 -7 -5 0 -7 -4 -4 -5 0 -4 213 N -2 0 2 -1 -6 7 0 -2 0 -6 -4 2 0 -2 -5 -1 -3 -3 -4 -3 214 G -2 -3 -3 -4 -4 -4 -5 7 -4 -7 -7 -5 -4 -4 -6 -3 -5 -6 -6 -6 215 D -5 -5 -2 9 -7 -4 -1 -5 -5 -7 -7 -4 -7 -7 -5 -4 -4 -8 -7 -7 216 S -2 -4 -2 -4 -4 -3 -3 -3 -4 -6 -6 -3 -5 -6 -4 7 -2 -6 -5 -5 217 G -3 -6 -4 -5 -6 -5 -6 8 -6 -8 -7 -5 -6 -7 -6 -4 -5 -6 -7 -7 218 G -3 -6 -4 -5 -6 -5 -6 8 -6 -7 -7 -5 -6 -7 -6 -2 -4 -6 -7 -7 219 P -2 -6 -6 -5 -6 -5 -5 -6 -6 -6 -7 -4 -6 -7 9 -4 -4 -7 -7 -6 220 L -4 -6 -7 -7 -5 -5 -6 -7 0 -1 6 -6 1 0 -6 -6 -5 -5 -4 0 221 N -1 -6 0 -6 -4 -4 -6 -6 -1 3 0 -5 4 -3 -6 -2 -1 -6 -1 6 222 C 0 -4 -5 -5 10 -2 -5 -5 1 -1 -1 -5 0 -1 -4 -1 0 -5 0 0 223 Q 0 1 4 2 -5 2 0 0 0 -4 -2 1 0 0 0 -1 -1 -3 -3 -4 224 A -1 -1 1 3 -4 -1 1 4 -3 -4 -3 -1 -2 -2 -3 0 -2 -2 -2 -3 Serine scored differently in these two positions Active site nucleophile NCBI

36. Multiple sequence alignments (MSAs) In this example, an MSA is used to identify regions of high sequence conservation presumably reflecting structural and functional constraints. Useful for delimiting known domains and potential new functional regions (e.g. the Ras-binding domain in yellow and the blue box of currently unknown function).

37. Fun with MSA... MSA used to locate functional residues and domain boundaries in homologs of Dbl-proteins with known structure (Dbs and Tiam1). Red amino acids directly interact with GTPases. Blue residues directly interact with phosphoinositides.

38. Phyre uses a 3-dimensional Position Sensitive Scoring Matrix!

39. Hidden Markhov Models – devices for generating folds HMM is created using some examples and general rules. The examples are defined folds. For instance, 60 PH domains might be used to create an HMM for PH domains. An HMM can assign a probability that it generated a given sequence (e.g. does this sequence represent a PH domain?)

40. A very simple HMM for a protein with 4 amino acids The square boxes are called “match states” – these will emit a amino acid with a set probability for each AA. Diamond boxes are for insertions between match states, and the circles are for deletions of match states. There are probabilities associated with all of the arrows. There are many possible paths through the Model! These are the “rules” learned from the examples (e.g. PH domains you used). Random transitions through the Model and emissions from the states are guided by probabilities. All you see at the end is the generated sequence. The model that generated the sequence is “hidden”. But the resulting sequence is related to those sequences used to construct the model.Again, IT IS POSSIBLE TO CALCULATE THE PROBABILITY THAT A GIVEN SEQUENCE WAS GENERATED BY THE MODEL!

41. What you should know Homology If two proteins are homologous, they have a common fold and a common ancestor If two proteins have >25% identity across their entire length, they are likely to be homologs. However, sometimes true homologs have quite low sequence identity! Orthologs Homologous (and equivalent) proteins from different species. Arise from speciation. Paralogs Homologous (and equivalent) proteins found in same species. Divergence of sequences NOT from speciation (gene duplication). Alignments How to score? Minimum # of mutations?, Physicochemical properties (as perceived by us)?, Or learn from nature? Scoring schemes PAM, BLOSUM Algorithms - BLASTp, FASTA, Smith-Watermann, Needleman-Wunsch BLAST vs. PSI-BLAST

42. E values What it means in words E = Kmne -λS Alignment algorithms BLAST (Local, heuristic) FASTA (Local, heuristic) Smith-Waterman (Local, rigorous) Needleman-Wunsch (Global, rigorous) Why use local alignment algorithm? Why use global alignment?