title: Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable.
abstract: BACKGROUND
  By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.
  
  
  RESULTS
  First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly- or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.
  
  
  CONCLUSIONS
  By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy -- in some cases exceeding 95%.
authors.affiliation: Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov
authors.forename: Myron
authors.initials: M
authors.lastname: Peto
authors.forename: Andrzej
authors.initials: A
authors.lastname: Kloczkowski
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
authors.forename: Robert L
authors.initials: RL
authors.lastname: Jernigan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D008958:Models, Molecular; D015233:Models, Statistical; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D012372:ROC Curve; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins