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Protein Loop Structure Prediction Methods

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Encyclopedia of Optimization

Article Outline

Introduction

Method and Applications

  Method

  Applications

References

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References

  1. Bower MJ, Cohen FE, Dunbrack RL (1997) Prediction of protein side chain rotamers from a backbone dependent rotamer library: a new homology modeling tool. J Mol Biol 267:1268–1282

    Article  Google Scholar 

  2. Bruccoleri RE, Karplus M (1987) Prediction of the folding of short polypeptide segments by uniform conformational sampling. Biopolym 26:137–168

    Article  Google Scholar 

  3. de Bakker PIW, DePristo MA, Burke DF, Blundell TL (2003) Ab initio construction of polypeptide fragments: Accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the generalized Born solvation model. Proteins: Struct Funct Bioinform 51:21–40

    Google Scholar 

  4. DePristo MA, de Bakker PIW, Lovell SC, Blundell TL (2003) Ab initio construction of polypeptide fragments: Efficient generation of accurate, representative ensembles. Proteins: Struct Funct Bioinform 51:41–55

    Google Scholar 

  5. Fiser A, Do RKG, Sali A (2000) Modeling of loops in protein structures. Protein Sci 9:1753–1773

    Google Scholar 

  6. Gill PE, Murray W, Saunders MA, Wright MH (1986) NPSOL 4.0 User's Guide. Systems Optimization Laboratory, Dept. of Operations Research, Stanford University, CA

    Google Scholar 

  7. Hartigan JA, Wong MA (1979) Algorithm AS 136: A K-means clustering algorithm. Appl Stat 28:100–108

    Article  MATH  Google Scholar 

  8. Jacobson MP, Pincus DL, Rappa CS, Day TJF, Honig B, Shaw DE, Friesner RA (2004) A hierarchical approach to all-atom protein loop prediction. Proteins: Struct Funct Bioinform 55:351–367

    Google Scholar 

  9. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features. Biopolym 22:2577–2637

    Article  Google Scholar 

  10. Klepeis JL, Floudas CA (1999) Free energy calculations for peptides via deterministic global optimization. J Chem Phys 110:7491–7512

    Article  Google Scholar 

  11. Klepeis JL, Floudas CA (2002) Ab initio prediction of helical segments in polypeptides. J Comput Chem 23:245–266

    Article  Google Scholar 

  12. Klepeis JL, Floudas CA (2003) Ab initio tertiary structure prediction of proteins. J Glob Optim 25:113–140

    Article  MathSciNet  MATH  Google Scholar 

  13. Klepeis JL, Floudas CA (2003) ASTRO-FOLD: A combinatorial and global optimization framework for ab initio prediction of three‐dimensional structures of proteins from the amino acid sequence. Biophys J 85:2119–2146

    Google Scholar 

  14. Klepeis J, Floudas C (2003) Prediction of beta-sheet topology and disulfide bridges in polypeptides. J Comput Chem 24(2):191–208

    Article  Google Scholar 

  15. Klepeis JL, Floudas CA (2005) Analysis and prediction of loop segments in protein structure. Comput Chem Eng 29(3):423–436

    Article  Google Scholar 

  16. Klepeis JL, Floudas CA, Morikis D, Lambris JD (1999) Predicting peptide structures using nmr data and deterministic global optimization. J Comput Chem 20:1354–1370

    Article  Google Scholar 

  17. Klepeis JL, Pieja MT, Floudas CA (2003) A new class of hybrid global optimization algorithms for peptide structure prediction: Integrated hybrids. Comput Phys Commun 151:121–140

    Article  Google Scholar 

  18. Klepeis JL, Pieja MT, Floudas CA (2003) Hybrid global optimization algorithms for protein structure prediction: Alternating hybrids. Biophys J 84:869–882

    Google Scholar 

  19. Klepeis J, Wei Y, Hecht M, Floudas C (2005) Ab initio prediction of the 3‑dimensional structure of a de novo designed protein: A double blind case study. Proteins: Struct Funct Bioinform 58:560–570

    Google Scholar 

  20. Mönnigmann M, Floudas CA (2005) Protein loop structure prediction with flexible stem geometries. Proteins: Struct Funct Bioinform 61(4):748–762

    Google Scholar 

  21. Némethy G, Gibson KD, Palmer KA, Yoon CN, Paterlini G, Zagari A, Rumsey S, Scheraga HA (1992) Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline‐containing peptides. J Phys Chem 96:6472–6484

    Article  Google Scholar 

  22. Xiang Z, Soto C, Honig B (2002) Evaluating conformational free energies: the colony energy and its application to the problem of loop prediction. Proceedings of the National Academy of Sciences of the United States of America, vol 99. pp 7432–7437

    Google Scholar 

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Mönnigmann, M., Floudas, C.A. (2008). Protein Loop Structure Prediction Methods . In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74759-0_530

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