Sukanya Manna Cheng-Yuan Liou National Taiwan University Department of Computer Science and Information E

1. IAENG_IMECS_ICB II, Room E 10:45~13:00, March 21, 2007, Hong KongPseudo-Reverse Approach in Genetic Evolution: An Empirical Study with Enzymes Sukanya Manna Cheng-Yuan Liou National Taiwan University Department of Computer Science and Information Engineering

2. NTU land size ~ 360 平方公里 • huge botanic garden inhigh mountains>3000meters 台大扁泥蟲 • eleven colleges, • 54 departments, • 96 graduate institutes (which offer 96 Master's programs and 83 doctoral programs), research centers: the Division of Population and Gender Studies, the Center for Condensed Matter Sciences, the Center for Biotechnology, Japanese Research Center, and the Biodiversity Center. • The number of students reached 29,877 in 2004, including the students from the division of Continuing Education & Professional development

3. Concepts used • Under neutral evolution • Rate of synonymous substitution = Rate of Nonsynonymous substitutions • Estimation of rate of synonymous and nonsynonymous substitutions has become an important subject in molecular evolution

4. Why? • ‘Draft’ theory: initial and intuitive evolution model • Part of evol based on a set of core systems. • They are relatively invariant (hard and strong) over evolution. • Qualitative changes occur as distinct systems are integrated. • Separate systems conjoin to produce distinctively patterns of evol change. • This model provides evol flexiblity.

5. Assumptions • For comparative genomics • nondistantly related species like human and mouse share the vast majority of their genes • amino acid sequences obtained for each enzymes share a great similarity like homologous genes

6. Our Approach Amino acid sequences for each enzyme proteins. Overview of the steps undertaken Least Mismatch between two aa sequences, and selection of trio Generating the nucleotide (nt) sequences for the aa sequences from the trio. Perform dn/ds ratio test among the pair of species with randomly generated nt sequences.

7. Our Approach (contd.) AATGATTGTCAAGAGCATAAG TTT TAT Nt to AA AA to nt NDCQEHKFY R E V E R S E AATGATTGTCAAGAGCATAAG TTT TAT AACGATTGCCAAGAACATAAG TTT TAT AATGACTGTCAGGAGCACAAG TTC TAT All possible combinations, Infeasible, High space and time complexity … … …

8. Basic Concepts • Nucleotides • A,G,T,C (DNA) • A,G,U,C(mRNA) • Amino acid • 20 naturally occurring • Coded by a triplet of nucleotide bases (referred as a codon) • Synonymous/Nonsynonymous substitution • A substitution of a base within the codon that does not / does change the type amino acid it represents. 43=64 codons code for 20 amino acids 3 of the 64 codons are stop codons that marks the end of a gene section (ie. end of exon)

9. Model Used • Jukes and Cantor (one parameter method) • Assumes rate of substitution between all pairs of A,T,C,G is the same. • where p is either ps or pn (result is ds and dn respectively) • ps = Sd/S • pn = Nd/N • Sd / Nd – total # of synonymous / nonsynonymous difference for all codons compared • S / N – numbers of synonmous / nonsynonmous sites

10. Our Approach (contd.) • Normally, we have seen that the amino acids sequences are obtained from nucleotide sequences by using the universal genetic mapping table. • Generating the nucleotide sequences from the amino acid sequences is a concept of reverse process. • For a particular amino acid sequences, there can be numerous nucleotide sequences for all the possible combination of codons. • But generation of all sequences is infeasible because of very large time and space complexity. • We use here this reverse mechanism, to match the closely related nucleotide sequences of the respective amino acids. • The next slide will show, what method we have used to proceed with this situation.

11. Our Approach (contd.) Calculated the total frequency of codons from each genome Calculated cumulative probability of the codons from these frequencies

12. Our Approach (contd.) • Generated the random sequences using the cumulative probability: • Best matched pairs • Generate sequences for trio • All pairs with least mismatch • Generate sequences only with the all pairs

13. Our Approach (contd.) A = [a1, a2,…an] aa sequences for HUMAN B = [b1, b2,…bm] aa sequences for MOUSE C = [c1, c2,…ck] aa sequences for RAT Calculate all possible mismatch between AB, BC and CA a1b1, a1b3, a2b2, a1r2, a2r5, a1r1, b1r1, b1r2, b2r6 Selecting the best matched pair Choose randomly such that three pairs will be: a1b1, b1r2and a1r2 aa sequences with least mismatch a1b1r2 is the trio

14. Our Approach (contd.) • Least mismatch means maximum similarity in their sequences. • Let A, B, C be the amino acid sequences for human, mouse and rat respectively. • We compare the two sequences with one amino acids at a time. • Calculated the possible mismatches between all sequences . • Separated out the ones with least mismatch. • Here the example is shown for the amino acid sequences for one particular enzyme.

15. Our Approach (contd.) • Generalized algorithm • Pathway analysis by model of Nei and Gojobori • No transition matrix used here • No phylogenetic tree for codon comparison • Sliding buffer of 3 characters used for codon comparison. • Used Jukes and Cantor’s model for multiple nucleotide substitution correction.

16. Our Approach (contd.) AATGATTGTCAAGAGCATAAG TTT TAT AATGACTGTCAGGAGCACAAG TTC TAT Use Nei and Goobori’s model to calculate the pathways and Jukes and Cantor’s model to get dn/ds. Sliding buffer compares codons for each sequences each time

17. Experimental Results (Best matched pairs) dn/ds Ratio of the Human-Mouse, Mouse-Rat and Human-Rat Comparison for the Enzymes Common in all. Numbers in brackets is the length of sequence compared.

18. Experimental Results (contd.)(Best matched pairs) dn/ds Ratio of Human-Mouse and Mouse-Rat Comparison for the Enzymes not Common in them.

19. Experimental Results (contd.)(Best matched pairs) Valid dn/ds Ratio of the Mouse-Rat Comparison for the Enzymes found only in these two species but not Human

20. Experimental Results (contd.)(All pairs with least mismatch) dn/ds Ratio of the Human-Mouse, Mouse-Rat and Human-Rat Comparison for the Enzymes Common in all. This graph shows the enzymes with only one least mismatch sequence pair for each species pair.

21. Experimental Results (contd.)(All pairs with least mismatch) Carboxylesterase Transaldolase For all three species comparison, enzymes with more than one least mismatch. dn/ds ratio of human-mouse, mouse-rat and human-rat comparison for the enzymes common in all. The graphs show the enzymes with multiple least mismatch sequence pair for each species pair. The label in x-axis indicates the sequence pair number and is insignificant.

22. Experimental Results (contd.)(All pairs with least mismatch) Enzymes found only for Human-mouse comparison

23. Experimental Results (contd.)(All pairs with least mismatch) Enzymes found only for Mouse-rat comparison

24. Experimental Results (contd.)(All pairs with least mismatch) Enzymes found only for human-rat comparison

25. Experimental Results (contd.)(All pairs with least mismatch) Estimated time for aa substitution per for the enzymes

26. Experimental Results (contd.)(All pairs with least mismatch) Estimated time for aa substitution per for the enzymes common in all three species

27. Summary • Rate of synonymous substitution varies considerably from gene to gene • Many enzymes, inspite of being proteins in nature, do not provide the valid results • Accuracy rate is about 50% to 55%. • Nonsynonymous sites were too high for some cases, so no valid result.

28. Summary (contd.) • In cases of enzymes, the variation is high in comparison to the ordinary proteins as mentioned in the case study with ordinary proteins by Prof Li. • Enzymes possess restoration capability after chemical reactions, that means it can resist many mutations.

29. Summary (contd.) • Here, in this work, estimated time for mutation is around 5 times more (~400 Myr). • We can say that they are 5 times stronger than ordinary proteins.

30. Summary (contd.) Comparison between already Established Result and Our Approach (NVR – No Valid Results, H-Human, M-Mouse, R-Rat)

31. Summary (contd.) • None of the values can be considered to be accurate. • All may vary with the parameters or the assumption taken into account. • We can just observe the nature of selection – whether neutral or purifying or diversifying. • In this table, the variations have occurred , but we don’t know which pair of genes have been taken by Prof Li. • For our case, the random sequence generated might have varied a lot from what the nucleotide sequence for that gene should have been originally. • NVR means- not valid result. • In these cases the ratio could not be calculated as the value of ds obtained was not a valid number that could be computed.

32. Thank YouSuppl. Materials in website.Evol model is Hairy model.