Recognition models to predict DNA-binding specificities of homeodomain proteins.
BACKGROUND
Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes.


RESULTS
Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.
Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.
Ryan G
RG
Christensen
Metewo Selase
MS
Enuameh
Marcus B
MB
Noyes
Michael H
MH
Brodsky
Scot A
SA
Wolfe
Gary D
GD
Stormo
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry*; D004330:Drosophila; D018398:Homeodomain Proteins / Q000737:chemistry*; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D016415:Sequence Alignment; D060388:Support Vector Machine; D014157:Transcription Factors / Q000737:chemistry; D016335:Zinc Fingers
D018398:Homeodomain Proteins; D014157:Transcription Factors; D004247:DNA