1. Alignments Why do Alignments?

2. Detecting Selection Evolution of Drug Resistance in HIV

3. Selection on Amino Acid Properties TreeSAAP (2003) Wu Method (Sainudiin et al. 2005)

4. TreeSAAP Properties Alpha-helical tendencies Average number of surrounding residues Beta-structure tendencies Bulkiness Buriedness Chromatographic Index Coil tendencies Composition Compressibility Equilibrium constant (ionization of COOH) Helical contact area Hydropathy Isoelectric point Long-range non-bonded energy Mean r.m.s. fluctuation displacement Molecular volume Molecular weight Normalized consensus hydrophobicity Partial specific volume Polar requirement Polarity Power to be at the C-terminal Power to be at the middle of alpha- helix Power to be at the N-terminal Refractive index Short and medium range non-bonded energy Solvent accessible reduction ratio Surrounding hydrophobicity Thermodynamic transfer hydrophobicity Total non-bonded energy Turn tendencies

5. TreeSAAP

6. Rhinoviruses

7. Selected Sites

8. 3D Mapping

9. PHENOTYPE GENOTYPE ENVIRONMENT OPSIN: Model System for Molecular Evolution Wavelength (nm) 400500600700 UVIR CRLAKIAMTTVALWFIAWT PYLLINWVGMFARSYLSPV YTIWGYVFAKANAVYNPIV YAISHPKYRAAMEKKLPCL SCKTESDDVSESASTTTSS

10. Is max Correlated with Ecological Differences? microscopic thin beam of spectral light INPUTOUTPUT INPUT – OUTPUT = pigment absorbance Detect light not absorbed by the photopigment 400 – 700 nm at 1nm intervals

12. Coil Tendencies, Compressibility, Alpha-Helix

13. Amino acid alignment number -2 0 2 4 6 102030405060708090100110120130140150160170180190200210220230240250260 Coil Tendencies -2 0 2 4 6 102030405060708090100110120130140150160170180190200210220230240250260 Compressibility -2 0 2 4 6 1020 30 4050 60 70 8090100110120130140150160 170 180190 200 210220 230 240250260 Power to be at mid alpha -2 0 2 4 6 8 10 0 2030405060708090100110120130140150160170180190200210220230240250260 Refractive Index Z-score TMI TMIITMIIITMIVTMVTMVI TreeSAAP

15. Homology

16. Homology definitions Homology is an evolutionary relationship that either exists or does not. It cannot be partial. An ortholog is a homolog that arose through a speciation event A paralog is a homolog that arose through a gene duplication event. Paralogs often have divergent function. Similarity is a measure of the quality of alignment between two sequences. High similarity is evidence for homology. Similar sequences may be orthologs or paralogs.

17. One More Homology type Xenology – similarity due to horizontal gene transfer (HGT) How do you discover this?

18. Alignment Problem (Optimal) pairwise alignment consists of considering all possible alignments of two sequences and choosing the optimal one. Sub-optimal (heuristic) alignment algorithms are also very important: eg BLAST

19. Key Issues Types of alignments (local vs. global) The scoring system The alignment algorithm Measuring alignment significance

20. Types of Alignment Global—sequences aligned from end- to-end. Local—alignments may start in the middle of either sequence Ungapped—no insertions or deletions are allowed Other types: overlap alignments, repeated match alignments

21. Local vs. Global Pairwise Alignments A global alignment includes all elements of the sequences and includes gaps. A global alignment may or may not include "end gap" penalties. Global alignments are better indicators of homology and take longer to compute. A local alignment includes only subsequences, and sometimes is computed without gaps. Local alignments can find shared domains in divergent proteins and are fast to compute

22. How do you compare alignments? Scoring scheme What events do we score? Matches Mismatches Gaps What scores will you give these events? What assumptions are you making? Score your alignment

23. Scoring Matrices How do you determine scores? What is out there already for your use? DNA versus Amino Acids? TTACGGAGCTTC CTGAGATCC

24. Multiple Sequence Alignment Global versus Local Alignments Progressive alignment Estimate guide tree Do pairwise alignment on subtrees ClustalX

25. Improvements Consistency-based Algorithms T-Coffee - consistency-based objective function to minimize potential errors Generates pair-wise global (Clustal) Local (Lalign) Then combine, reweight, progressive alignment

26. Iterative Algorithms Estimate draft progressive alignment (uncorrected distances) Improved progressive (reestimate guide tree using Kimura 2-parameter) Refinement - divide into 2 subtrees, estimate two profiles, then re-align 2 profiles Continue refinement until convergence

27. Software Clustal T-Coffee MUSCLE (limited models) MAFFT (wide variety of models)

28. Comparisons Speed Muscle>MAFFT>CLUSTALW>T-COFFEE Accuracy MAFFT>Muscle>T-COFFEE>CLUSTALW Lots more work to do here!