Boosting the prediction and understanding of DNA-binding domains from sequence.
DNA-binding proteins perform vital functions related to transcription, repair and replication. We have developed a new sequence-based machine learning protocol to identify DNA-binding proteins. We compare our method with an extensive benchmark of previously published structure-based machine learning methods as well as a standard sequence alignment technique, BLAST. Furthermore, we elucidate important feature interactions found in a learned model and analyze how specific rules capture general mechanisms that extend across DNA-binding motifs. This analysis is carried out using the malibu machine learning workbench available at http://proteomics.bioengr.uic.edu/malibu and the corresponding data sets and features are available at http://proteomics.bioengr.uic.edu/dna.
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60612, USA.
Robert E
RE
Langlois
Hui
H
Lu
D001185:Artificial Intelligence; D004268:DNA-Binding Proteins / Q000737:chemistry*; D017434:Protein Structure, Tertiary; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
D004268:DNA-Binding Proteins