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computer modeling in molecular biology.pdf

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5 Modell<strong>in</strong>g Nucleic Acids 129simulations should help enormously our <strong>in</strong>sight and understand<strong>in</strong>g of the <strong>in</strong>teractionsand dynamics of water molecules around nucleic acids.The present <strong>in</strong>tractability of modell<strong>in</strong>g such an overwhelm<strong>in</strong>g amount ofmutually coupled <strong>in</strong>teractions led us to favour <strong>in</strong>teractive graphics techniques formodell<strong>in</strong>g large RNA molecules despite the unavoidable heavy reliance on humanjudgments for select<strong>in</strong>g local conformations lead<strong>in</strong>g to global compactness. Withmodell<strong>in</strong>g conceived as a tool, one might as well take advantage of the capability ofthe human m<strong>in</strong>d to th<strong>in</strong>k globally and act locally. Energy m<strong>in</strong>imization andrestra<strong>in</strong>ed least-squares are local techniques which perform only with<strong>in</strong> a smallradius of convergence. Although a more global technique, distance geometry, wasrecently <strong>in</strong>troduced <strong>in</strong> RNA modell<strong>in</strong>g [55], it leads to improbable tangled or knottedstructures that the algorithm cannot remove from the set of solutions. Malhotra etal. [56] have proposed an automatic RNA fold<strong>in</strong>g procedure <strong>in</strong> which a nucleotideis represented as a pseudoatom located at the phosphate atom. Such an approachneglects all the f<strong>in</strong>ely gra<strong>in</strong>ed <strong>in</strong>teractions between helices or loops and helices whichgovern and stabilize the three-dimensional fold<strong>in</strong>g [57]. A technique based on a constra<strong>in</strong>tsatisfaction algorithm has been put forward [58]. For small systems, this approachcould be useful, although the software and CPU requirements are heavy(10 hours CPU time on a sophisticated workstation for fold<strong>in</strong>g a T-loop). Thedrawbacks of build<strong>in</strong>g manually RNA models on graphics systems are real: the processis laborious and can be subjective, s<strong>in</strong>ce it depends on the judgment of themodeller which is itself based on his knowledge of RNA structures. However, up tonow, it is still the most successful method for large RNAs (see for example themodels of 5s rRNA [59, 601 and the model for the core of group I <strong>in</strong>trons [61-631,especially when modell<strong>in</strong>g is viewed as produc<strong>in</strong>g three-dimensional hypothesesdest<strong>in</strong>ed to be confronted to experimental verifications via directed mutagenesis andchemical or enzymatic prob<strong>in</strong>g.References[l] Konnert, J. H., Hendrickson, W. A., Actu Crystullogr. Sect. A 1980, 36, 344-349.[2] Northrup, S. H., Pear, M. R., McCammon, J. A., Karplus, M., Takano, T., Nuture1980,287, 659-660.[3] Levy, R. M., Sheridan, R. P., Keepers, J. W., Dubey, G. S., Swam<strong>in</strong>athan, S., Karplus,M., Biophys. J 1985, 48, 509-518.[4] Brooks, C. L., Karplus, M., Pettitt, B. M., Adv. Chem. Phys. 1988, LXXZ.[5] Ott<strong>in</strong>g, G., Liep<strong>in</strong>sh, E., Wiithrich, K., Science 1991, 254, 974-980.[6] Frey, M., <strong>in</strong>: Water and Biological Macromolecules, Westhof, E. (ed.), Macmillan Press,London 1993, pp. 98-147.[7] Case, D. A., Karplus, M., J. Mol. Biol. 1979, 132, 343-368.

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