Skip to main content
Article thumbnail
Location of Repository

Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

By Myron Peto, Andrzej Kloczkowski, Vasant Honavar and Robert L Jernigan
Topics: Research Article
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2655094
Provided by: PubMed Central

Suggested articles

Citations

  1. (2003). Compact polymers. Macromolecules
  2. (2004). Designability and thermal stability of protein structures. Polymer
  3. (2002). Designability of protein structures: a lattice-model study using the Miyazawa-Jernigan matrix. PROTEINS: Struct, Funct Genetics
  4. (2006). Designable Structures Are Easy to Unfold. Phys Rev E Stat Nonlin Soft Matter Phys
  5. (1984). DJ: Compact self-avoiding circuits on two dimensional lattices.
  6. (2000). Ejtehadi MR: Geometry selects highly designable structures.
  7. (1999). Ejtehadi MR: Protein ground state candidates in a simple model: An enumeration study. Phys Rev E
  8. (1996). Emergence of Preferred Structures in a Simple Model of Protein Folding.
  9. (1996). Enting IG: Solvability of some statistical mechanical systems. Physical Review Letters
  10. (2003). Enumeration of compact self-avoiding walks.
  11. (2000). Enumeration of cubic lattice walks by contact class.
  12. (2002). Fast Tree Search for Enumeration of a Lattice Model of Protein Folding.
  13. (2005). Frank E: "Data Mining: Practical machine learning tools and techniques". 2nd edition.
  14. (1999). Geometrically reduced number of protein ground state candidates. Phys Rev Letts
  15. (1990). Gutin A: Enumeration of all Compact Conformations of Copolymers with Random Sequnce of Links.
  16. (1993). Gutin AM: Engineering of stable and fast folding sequences of model proteins. P r o c N a t l A c a d S c i U S A
  17. (1998). Highly designable protein structures and inter-monomer interactions.
  18. (1992). Inverse protein folding problem: designing polymer sequences.
  19. (1989). Jannink G: Polymers in solution Oxford,
  20. (1997). Machine learning
  21. (1996). Modeling protein folding: The beauty and power of simplicity. Fold Design
  22. (1997). NS: Nature of driving force for protein folding: A result from analyzing the statistical potential. Phys Rev Letts
  23. (1990). Origins of structure in globular proteins.
  24. (1994). Proteins with selected sequences fold into unique native conformation. Phys Rev Letts
  25. (1997). RL: Computer generation and enumeration of compact self-avoiding walks within simple geometries on lattices. Comput Theoret Polymer Sci
  26. (1990). RL: Conformations of Folded Proteins in Restricted Spaces. Biochemistry
  27. (1997). RL: Efficient method to count and generate compact protein lattice conformations. Macromolecules
  28. (2007). RL: Shape-dependent designability studies of lattice proteins.
  29. (1998). RL: Transfer matrix method for enumeration and generation of compact self-avoiding walks. 1. Square lattices.
  30. (1998). RL: Transfer matrix method for enumeration and generation of compact self-avoiding walks. II. Cubic lattice.
  31. (2003). Shahknovich EI: Natural selection of more designable folds: A mechanism for thermophilic adaptation. Proc Natl Acad Sci USA
  32. (2005). Shahknovich EI: Physics and evolution of thermophilic adaptation.
  33. (1995). Shakhnovich EI: Evolution-like selection of fast-folding model proteins. Proc Natl Acad Sci USA
  34. (2005). Shakhnovich EI: Protein structure and evolutionary history determine sequence space topology. Genome Res
  35. Slade G: The self-avoiding walk.
  36. Stability of preferable structures for a hydrophobic-polar model of protein folding.
  37. (1998). Statistical Learning Theory
  38. (1986). The effect of noise on concept learning.
  39. (1990). The effects of internal constraints on the configurations of chain molecules.
  40. (2006). Unbiased sampling of lattice Hamiltonian path ensembles.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.