HUMIES presentation: Evolutionary design of energy functions  for protein structure prediction
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HUMIES presentation: Evolutionary design of energy functions for protein structure prediction

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HUMIES presentation: Evolutionary design of energy functions  for protein structure prediction HUMIES presentation: Evolutionary design of energy functions for protein structure prediction Presentation Transcript

  • Evolutionary design of energy functions for protein structure prediction Natalio Krasnogor nxk@cs.nott.ac.uk Paweł Widera, Jonathan Garibaldi 7th Annual HUMIES Awards 2010-07-09
  • Protein structure prediction From 1D sequence to 3D structure LFSKELRCMMYGFGDDQNPYTESVDILEDLVIEFITEMTHKAMSIFSEEQLNRYEMYRRSAFPKAA IKRLIQSITGTSVSQNVVIAMSGISKVFVGEVVEEALDVCEKWGEMPPLQPKHMREAVRRLKSKGQIP Protein basics 20 amino acid alphabet sequence encodes structure structure determines activity structures ratio sequences = 0.2% Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 2 / 15
  • The algorithm of folding Anfinsen’s thermodynamic hypothesis [Anfinsen, 1973] Refolding experiment folds to the same native state native state is energetically stable Energy funnel roll down free energy hill avoid local minima traps [Dill and Chan, 1997] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 3 / 15
  • The two aspects of folding Towards practical prediction Energy landscape all-atom force field statistical potential Search method random walk structure optimisation Folding@home 8.5 peta FLOPS 10 000 CPU days for 10µs of folding [Dill and Chan, 1997] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 4 / 15
  • The two aspects of folding Towards practical prediction Energy landscape all-atom force field statistical potential Search method random walk structure optimisation Folding@home 8.5 peta FLOPS 10 000 CPU days for 10µs of folding [Dill and Chan, 1997] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 4 / 15
  • Community wide prediction experiment Critical Assessment of techniques for protein Structure Prediction CASP facts biannual competition started in 1994 parallel prediction and experimental verification model assessment by human experts 9th edition of CASP 150 human groups 140 server groups Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 5 / 15
  • How to find good quality models? Correlation between energy and distance to the native structure energy Requirements energy reflects distance distance reflects similarity native state distance Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 6 / 15
  • How the best of CASP do it? Energy of models vs. distance to a target structure Similarity measure i=N 1 RMSD = δi2 N i=1 Decoys generated by I-TASSER [Wu et al., 2007] Robetta [Rohl et al., 2004] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 7 / 15
  • How the best of CASP do it? Energy of models vs. distance to a target structure Similarity measure i=N 1 RMSD = δi2 N i=1 Decoys generated by I-TASSER [Wu et al., 2007] Robetta [Rohl et al., 2004] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 7 / 15
  • How the energy function is designed? Weighted sum vs. free combination of terms Decision support F (T ) = w1 ∗ T1 + . . . wn ∗ Tn [Zhang et al., 2003] local numerical T ∗T 1 3 „ T4 −w2 ∗T1 « approximation F (T ) = w ∗log(T ) + sin T5 ∗exp(cos(w1 ∗T3 )) 1 2 GP input terminals: T1 , . . . , T8 functions: add sub mul div sin cos exp log random ephemerals in range [0,1] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 8 / 15
  • How the energy function is designed? Weighted sum vs. free combination of terms Decision support F (T ) = w1 ∗ T1 + . . . wn ∗ Tn [Zhang et al., 2003] local numerical T ∗T 1 3 „ T4 −w2 ∗T1 « approximation F (T ) = w ∗log(T ) + sin T5 ∗exp(cos(w1 ∗T3 )) 1 2 GP input terminals: T1 , . . . , T8 functions: add sub mul div sin cos exp log random ephemerals in range [0,1] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 8 / 15
  • How the energy function is designed? Weighted sum vs. free combination of terms Decision support F (T ) = w1 ∗ T1 + . . . wn ∗ Tn [Zhang et al., 2003] local numerical T ∗T 1 3 „ T4 −w2 ∗T1 « approximation F (T ) = w ∗log(T ) + sin T5 ∗exp(cos(w1 ∗T3 )) 1 2 GP input terminals: T1 , . . . , T8 functions: add sub mul div sin cos exp log [Widera et al., 2010] random ephemerals in range [0,1] Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 8 / 15
  • What is the GP objective? Beyond simple symbolic regression 1 construction of the reference ranking RR (structures sorted by distance to the native) 3 1 5 0 4 7 6 2 2 construction of the evolutionary ranking RE (structures sorted by the evolved energy function) 5 1 3 4 0 2 6 7 3 rankings comparison — RR vs. RE 4 total fitness = average distance for m proteins m 1 F = d(RRi , REi ) m i=1 Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 9 / 15
  • Can GP improve over a weighted sum of terms? Nelder-Mead downhill simplex optimisation spearman-sigmoid correlation method d-100 all d-100 all simplex 0.734 0.638 0.650 0.166 GP 0.835 0.714 *0.740 *0.200 Computational cost of experiments 55 proteins, 1000-2000 structures for each 5 different ranking distance measures 20 different configurations of GP parameters total of 150 CPU days Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 10 / 15
  • Can GP improve over a weighted sum of terms? Nelder-Mead downhill simplex optimisation spearman-sigmoid correlation method d-100 all d-100 all simplex 0.734 0.638 0.650 0.166 GP 0.835 0.714 *0.740 *0.200 Computational cost of experiments 55 proteins, 1000-2000 structures for each 5 different ranking distance measures 20 different configurations of GP parameters total of 150 CPU days Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 10 / 15
  • Criteria for human-competitivness CRITERION F result >= past achievement in the field CRITERION E result >= most recent human-created solution to a long-standing problem CRITERION H result holds its own in a competition involving human contestants Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 11 / 15
  • Criteria for human-competitivness CRITERION F result >= past achievement in the field CRITERION E result >= most recent human-created solution to a long-standing problem CRITERION H result holds its own in a competition involving human contestants Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 11 / 15
  • Criteria for human-competitivness CRITERION F result >= past achievement in the field CRITERION E result >= most recent human-created solution to a long-standing problem CRITERION H result holds its own in a competition involving human contestants Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 11 / 15
  • Comparison to the human made solution 1 automated method to discover the best combination of the energy terms 2 human-competitive improvement to the solution of a long-standing problem 3 challenge weighted sum of terms with expert-picked weights Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 12 / 15
  • Potential impact 1 automated energy design using a free functional combination of terms haven’t been used before 2 energy functions determines the search landscape and its smoothness is a key to the efficient prediction 3 long-term effects in protein science that the improvement in prediction quality could bring Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 13 / 15
  • Why this is the best entry? 1 innovates the field with a novel approach to a long-standing problem 2 could be a step towards more accurate prediction and in a long-term improve drug design and identification of disease-causing mutations 3 represent a new and difficult challange for GP http://www.infobiotics.org/gpchallenge/ Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 14 / 15
  • References Anfinsen, C. (1973). Principles that Govern the Folding of Protein Chains. Science, 181(4096):223–30. Dill, K. A. and Chan, H. S. (1997). From Levinthal to pathways to funnels. Nat Struct Mol Biol, 4(1):10–19. Rohl, C. A., Strauss, C. E. M., Misura, K. M. S., and Baker, D. (2004). Protein Structure Prediction Using Rosetta. In Brand, L. and Johnson, M. L., editors, Numerical Computer Methods, Part D, volume Volume 383 of Methods in Enzymology, pages 66–93. Academic Press. Widera, P., Garibaldi, J., and Krasnogor, N. (2009). Evolutionary design of the energy function for protein structure prediction. In IEEE Congress on Evolutionary Computation 2009, pages 1305–1312, Trondheim, Norway. Widera, P., Garibaldi, J., and Krasnogor, N. (2010). GP challenge: evolving energy function for protein structure prediction. Genetic Programming and Evolvable Machines, 11(1):61–88. Wu, S., Skolnick, J., and Zhang, Y. (2007). Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol, 5(1):17. Zhang, Y., Kolinski, A., and Skolnick, J. (2003). TOUCHSTONE II: A New Approach to Ab Initio Protein Structure Prediction. Biophys. J., 85(2):1145–1164. Natalio Krasnogor Evolutionary design of energy functions for PSP HUMIES 2010 15 / 15