From Protein Structure to Function with Bioinformatics.pdf

74 A. Fiserfunctions delivers improving loop modelling methods. Two recent loop modellingprocedures have been introduced that are utilizing the effective statisticalpair potential that is encoded in DFIRE (Soto et al. 2008; Zhang et al. 2004).Another method is developed to predict very long loops using the ROSETTAapproach, essentially performing a mini folding exercise for the loop segments.(Rohl et al. 2004). In the Prime program large numbers of loops are generated byusing a dihedral angle-based building procedure followed by iterative cycles ofclustering, side chain optimization, and complete energy minimization of selectedloop structures using a full atom molecular mechanic force field (OPLS) withimplicit solvation model (Jacobson et al. 2004).3.2.4.3 Refining ModelsComparative models are constructed with the best possible set of restraints available,which is a usually a combination of various template structure dependent distanceand angle restraints combined with molecular mechanic force field terms andrestraints imposed by a variety of statistical potential functions. Because of thelarge number of available restraints the problem is overdefined. The model buildingstep is relatively straightforward and primarily focuses on resolving the conflictingrestraints. In case of MODELLER this is achieved by a combination of conjugategradient minimization and molecular dynamics simulation, and concludes a modeltypically just within a few minutes. Because of the dominance of template dependentrestraints it is often difficult to generate a model that is more similar on thebackbone accuracy level to the target protein than to the actual template (if oneassumes no alignment errors). It is a difficult task to further refine models becauseof the fact that the most accurate restraints and forcefield terms were already usedin model building. It essentially poses the same task as an ab initio modelling problem,since any novel refinement should take place in a template independent style.Various studies and a recent survey suggested that most refinements decrease theaccuracy of models (Summa and Levitt 2007). There was only one molecularmechanic energy function that was able to improve the initial model but by a smallmargin only and a slightly better performance was achieved by using a statisticalpotentials.Other promising refinement approaches try to intelligently restrict the conformationalsearch space around the high quality initial model. This can be achieved bysimply defining a certain maximum deviation that is allowed for the backbonemovements during sampling (Kolinski et al. 2001). A more recent promisingapproach identifies Evolutionary and Vibrational Armonics subspace, a reducedsampling subspace that consists of a combination of evolutionarily favoured directions,defined by the principal components of the structural variation within ahomologous family, plus topologically favoured directions, derived from the lowfrequency normal modes of the vibrational dynamics, up to 50 dimensions. Thissubspace is accurate enough so that the cores of most proteins can be representedwithin 1 Å accuracy, and reduced enough so that effective optimization approaches,