Kim T. Simons, Charles Kooperberg, Enoch Huang, and David Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209-225, 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... Some MCS methods use more sophisticated move sets (trial step generators) Again, our algorithm can be applied when the move sets do not change many DOFs simultaneously, which is in particular the case of the moves sets proposed in [2, 3] biasing the random torsion changes) and in [15] and [52] (moves based on fragment replacement) More computationally intensive step generators use the internal forces (the gradient of the energy function) to bias the choice of the next conformation (e.g. Force Biased MC [46] Smart MC [32] and MC plus minimization [40] For such step generators, the ....

K. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structure from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. of Molecular Biology, 268:209--225, 1997.

....alternative conformations have been referred to as decoys. The sets of decoys employed here are freely available from the URL http: dd.stanford.edu. Here, we used four different sets of decoys, which are called misfold [25] 4state reduced [5] lmds [Kesar and Levitt, unpublished] and fisa casp3 [38]. In the following, the first and last sets, respectively, will be simply referred to as 4state and casp3. The misfold set contains a single decoy for 26 chains obtained by gapless threading of the sequence onto another structure. Side chains have been placed using a Monte Carlo annealing ....

Simons KT, Kooperberg C, Huang E and Baker D Assembly of protein tertiary structures from fragment with similar local sequences using simulated annealing and bayesian scoring function. Mol Biol 1997, 268:209-225

.... conformations are generated by perturbing the existing conformation at an arbitrary residue by one of two methods: i) the torsion values for three residues with identical sequence from a known structure are used to modify the current conformation, similar in spirit to that of Baker and colleagues [39]; ii) one of possible 14 torsion ( values derived based on the most frequently occurring torsion values for a given residue in a database of known structures. The move sets were combined sequentially (i.e. where a certain number of iterations consisted of copying torsion values for ....

Simons K, Kooperberg C, Huang E, Baker D: Assembly of protein tertiary structures from fragments with similar local se- quences using simulated annealing and bayesian scoring functions. J Mol Biol 1997, 268:209-225

....tested in an exhaustive conformational search. For our study, we generated a large number of protein like decoy structures of length 60 residues using a hidden Markov model for local sequence structure correlations (HMMSTR[1] and a Monte Carlo simulation for assembling protein fragments (ROSETTA[2]) Distance matrices were compressed using two approaches: principal component analysis (PCA) and discrete Fourier transforms (FT) We conclude that decoy protein like structures are compressible. Is the same true of real proteins Can the compression schemes learned on decoys be applied to true ....

Simons, K. T., Kooperberg, C., Huang, E. & Baker, D. 1997. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268, pp. 209-25.

....mainchains for these decoys were generated using a simulated annealing procedure to assemble native like structures from fragments of unrelated protein structures with similar local sequences. Sidechains for these proteins were modelled with the SCWRL[9] computer program. A Bayesian score function [82] was used to guide the decoy structure assembly process. The score function is based on a distance dependent pairwise residue contact potential similar to expressions for potentials of mean force [87] Other additions to the score function include de nitions of environment and compactness. Since ....

....conformations which were more physically valid. The take home message is that one must ensure a proper coupling between the conformation generation process and the physical aspects of protein structure modelled by the score function. 169 6.5. 3 Implications of these ndings Many published works [93, 67, 41, 68, 27, 82, 102, 77, 46, 31, 54] have used the decoy sets mentioned in this thesis as feedback on the performance of new score functions and to compare di erent score functions. Since this is an active area of development, there are probably many more unpublished investigations that also use these decoy sets. The decoy set de ....

Kim T. Simons, Charles Kooperberg, Enoch Huang, and David Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209-225, 1997.

....A major limitation of the threading approach is that if an appropriate core is not already present in the structure library, correct prediction is obviously impossible. Some attempts have been made to assemble structure fragments into a novel core [Sippl et al. 1992] Kolinski et al. 1996] and [Simons et al. 1997]. A fundamental algorithmic complexity barrier is crossed if both variable alignments and pair interactions between sequence residue positions are modeled. With the inclusion of pair interactions (the 3D 3D case [Bowie et al. 1991] Luthy et al. 1992] the general problems of protein folding ....

.... Conditional probability and Bayesian methods have been applied to protein threading [Bryant Lawrence, 1993] Bryant Altschul, 1995] Madej et al. 1995] protein secondary structure [Arnold et al. 1992] Stultz et al. 1995] side chain packing [Dunbrack Cohen, 1997] fragment assembly [Simons et al. 1997], solvent exposure prediction [Thompson Goldstein, 1996] motif discovery [Lawrence et al. 1993] and structure classification [Stultz et al. 1995] Hunter States, 1992] White et al. 1994b] all with good results. In this paper, the Bayesian analysis provides a compact account of the ....

Simons, K.T., Kooperberg, C., Huang, E., Baker, D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268:209--225, 1997.

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K.T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209--225, 1997.

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K.T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209--225, 1997.

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Simons, K.S., Kooperberg, C., Huang, E., Baker, D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol. 268:209--225, 1997. 171 LOCAL STRUCTURE PREDICTION USING I-SITES LIBRARY

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Simons KT, Kooperberg C, Huang E, Baker D.Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997;268:209--225.

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Simons KT, Kooperberg C, Huang E, Baker D.Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J Mol Biol 1997;268:209--225.

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K.T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209#225, 1997.

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Simons, K.T., Kooperberg, C., Huang, E., Baker, D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268:209--225, 1997.

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Simons K, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997;268:209--225. 198 R. SAMUDRALA ET AL.

....In this regard, the use of distance geometry has shown promise when accurate inter residue distances are available. We describe a method by which distance geometry restraints are culled from sets of 500 protein like conformations for four small helical proteins generated by the method of Simons et al. 1997). A consensus based approach was applied in which every inter Calpha distance was measured, and the most frequently occurring distances were used as input restraints for distance geometry. For each protein, a structure with lower coordinate root mean square (RMS) error than the mean of the ....

.... 1994; Hinds Levitt, 1992, 1994; Kolinski Skolnick, 1994; Vieth et al. 1994) or sampling in torsion space (Wilson Doniach, 1989; Bowie Eisenberg, 1994; Dandekar Argos, 1994, 1996; Monge et al. 1995; Mumenthaler Braun, 1995; Srinivasan Rose, 1995; Sun et al. 1995, Yue Dill, 1996; Simons et al. 1997). However, reduction of the search space also decreases the fidelity with which the native fold can be represented. This is problematic, since knowledge based scoring functions that recognize native folds have difficulty separating near native folds from non native folds (Park et al. 1997) ....

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Simons KT, Kooperberg C, Huang E, Baker D. 1997. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268:209-225.

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K.T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209--225, 1997.

.... to compile the parameters can also skew the extracted scores [17] To circumvent the need for the assumptions surrounding the conversion of database statistics to true energies, some methods rely instead on the Bayesian 5 formalism (i.e. conditional probabilities) to formulate a scoring function [21, 22]. The two formalisms are analogous and follow the same methodology in practice. We therefore refer to all the knowledge based functions discussed in this chapter as scoring or objective functions. Given that there are a multitude of scoring functions designed for protein structure prediction ....

....must be monitored by at least one component of the scoring function. The problem of secondary structure formation may be surmounted by imposing native secondary structure assignments. Otherwise, a combinatorial explosion in the search process is averted by biased sampling of conformational space [21] or by ad hoc terms in the energy function favoring secondary structure formation (for instance, via hydrogen bonding) The implementation of these terms are typically specific to a given 15 structural representation (e.g. lattice models with a certain geometry and spacing) so we will not ....

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Simons, K.T., Kooperberg, C., Huang, E. & Baker, D. (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268, 209-225.

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K.T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209--225, 1997.

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Kim T. Simons, Charles Kooperberg, Enoch Huang, and David Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J. Mol. Biol., 268:209-225, 1997.

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Simons, K.T., Kooperberg, C., Haung, E., and Baker, D., Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring function, J. Mol. Biol., 286(2):209--225, 1999.

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Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol (1997) 268:209-225.

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K. T. Simons, C. Kooperberg, E. Huang, and D. Baker, "Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions," J. Molec. Biol., vol. 268, no. 1, pp. 209--25, 1997.

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K.T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. Journal of Molecular Biology, 268(1), 209-25, 1997.

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Simons KT, Kooperberg C, Huang E, Baker D: Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997, 268:209-225.

No context found.

Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J Mol Biol 1997;268:209 --225.

No context found.

Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997;268:209 --225.

No context found.

Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. J Mol Biol 1997;268:209 --225.

No context found.

Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997;268:209--225.

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