1. Hyun, Bora

2. Contents Introduction Background & Motivation PreSPI++ Evaluation of PreSPI++ Method DCPPW++ Evaluation Conclusion 2ISI LABORATORY

3. Introduction Domain combination based approach Uses domain combination and domain combination pair information for the prediction of the protein interactions. PreSPI++ A protein interaction prediction system based the Interaction Significance(IS) matrix which quantified an influence of domain combination pair on a protein interaction. 3ISI LABORATORY

4. Motivation & Objective In PreSPI++, Consider Possibilities of domain collaboration and, Weighted Domain Combination Pair(WDCP) Consider being the main body on a protein interaction Domain Combination Pair’s coupling Power(DCPPW) However, No explanation and evaluation of relationship between DCPPW and physical interaction structures 4ISI LABORATORY

5. Motivation & Objective Verify whether value of DCPPW is given consistently based on PDB crystal structures, especially in protein interactions which have multi-domain interactions Propose advanced weighting method for domain combination pairs considering PDB crystal structures We can provide more reliable and meaningful weighted domain combination information for prediction of protein-protein interaction 5ISI LABORATORY

6. PreSPI++ IS matrix contains Possibilities of domain collaboration Using all-confidence 6ISI LABORATORY

7. PreSPI++ IS matrix contains Possibilities of domain collaboration Possibilities of being main body on interaction WDCP 7ISI LABORATORY a b A e D a c B e D a d C e D Weight of = Weight of,, ?

8. PreSPI++ IS matrix contains Possibilities of domain collaboration Possibilities of being main body on interaction WDCP DCPPW Using frequency information as relative power 8ISI LABORATORY

9. Evaluation of PreSPI++ Using PDB crystal structure information PPIs have single domain interaction Among 169 pairs, 146 pair correctly predicted 9 DCPPW Compare with PDB crystal structure MatchUn-Match 0.0< DCPPW<0.1 84.73%00.00% 0.1< DCPPW<0.2 84.73%63.55% 0.2< DCPPW<0.3 52.96%31.78% 0.3< DCPPW<0.4 105.92%00.00% 0.4< DCPPW<0.5 84.73%63.55% 0.5< DCPPW<0.6 42.37%00.00% 0.6< DCPPW<0.7 1710.06%10.59% 0.7< DCPPW<0.8 21.18%00.00% 0.8< DCPPW<0.9 63.55%10.59% 0.9< DCPPW<1.0 1710.06%10.59% DCPPW=1 6136.09%52.96% Total 14686%2314%

10. Evaluation of PreSPI++ Using PDB crystal structure information PPIs have multi domain interaction Among 56 pairs, only 1 pair correctly predicted 10ISI LABORATORY DCPPW Compare with PDB crystal structure MatchUn-Match 0.0< DCPPW<0.1 00%0%3358.93% 0.1< DCPPW<0.2 00%0%1832.14% 0.2< DCPPW<0.3 11.79%00%0% 0.3< DCPPW<0.4 00%0%47.14% Total 11.79%5598.21%

11. Evaluation of PreSPI++ WDCP Since the weight of single domain is always one, the weight of a DC made by two or more domains is not higher than one of single domain. DCPPW Freq ({a,b,c}) ≤ Freq ({a}), Freq({b}), Freq({c}) So, DCPPW({a,b,c}) ≤ DCPPW({a}), DCPPW ({b}), DCPPW({c}) It is rational because PDB has only few interaction pairs caused by multi-domain interaction 11ISI LABORATORY

12. Method We need additional processing and weight scheme to compensate DCPPW for multi-domain interactions Pre-processing Filter out DCs have low all-confidence value Weight scheme Reduce effect caused by difference of all-confidence and frequency between single and multi domain pairs All coefficient will experimentally determined. 12ISI LABORATORY

13. Method Using rooted all-confidence value to compensate large difference between single and multi DC Distribution of all-confidence by DC size change 13ISI LABORATORY

14. Method Using size of DC information Multiply of each size of DC Sum of each size of DC C=Coefficient 14ISI LABORATORY

15. Evaluation Data (iPfam) PPI with multi-domain interaction : 65 개 PPI with single-domain interaction : 169 개 Evaluation Coefficient change from 0 to 30 All-confidence + multiply Rooted all-confidence + multiply Rooted all-confidence + sum Pre-processing (filter out all-confidence is lower than 0.2, 0.3, 0.4) 15ISI LABORATORY

16. Evaluation PPIs have Multi-domain interaction Best : Rooted AC +sum Worst: AC>0.4 + multiply 16ISI LABORATORY

17. Evaluation PPIs have single-domain interaction Best : AC>0.4 + multiply Worst : Rooted AC + sum 17ISI LABORATORY

18. Future works Effect of modified learning method is not significant Even coefficient is 100, there are 13-15 matched results Matched results in single DDI reduced by coefficient increase About +13 / -24 Need other processing Directly using PDB information to weight their DC Using all inter/intra DC How much larger than other DC? How to evaluate? 18ISI LABORATORY

19. Future works Need to update domain data Need more evaluation 19ISI LABORATORY