1. Profile-profile alignment using hidden Markov models Wing Wong

2. Outline  Introduction  Steps in a profile-profile alignment  Use of hidden Markov models  Some scoring methods  Conclusion

3. Introduction  What is a profile? position-specific statistical model of a multiple sequence alignment = vectors of amino acid distribution at each alignment column

4. Introduction  Why profile-profile alignment? Detection of homologous proteins Use of broader information about protein families More sensitive fold recognition and better alignments

5. Steps  Preparation of multiple alignment  Construction of numerical profile  Alignment and scoring  Statistical evaluation of alignment results

6. Use of HMMs - sample HMM topology

7. Scoring methods  Dot product scoring  Information theoretic measure – combine divergence and significance score  Log odds based scoring – extension of substitution matrix scoring

8. Conclusion  Better at detecting distantly related proteins and provide better alignments  Difference in performance between different scoring schemes quite small  Probabilistic scoring methods perform very well  Want to incorporate secondary structure component into scoring system