Prediction of DNA-binding proteins from relational features

Prediction of DNA-Binding Proteins from Structural Features

We use logic-based machine learning to distinguish DNAbinding proteins from non-binding proteins. We combine previously suggested coarse-grained features (such as the dipole moment) with automatically constructed structural (spatial) features. Prediction based only on structural features already improves on the state-of-the-art predictive accuracies achieved in previous work with coarse-grained...

Prediction of DNA-binding residues from sequence

MOTIVATION Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is kn...

Efficient Prediction of DNA-Binding Proteins Using Machine Learning

DNA-binding proteins are a class of proteins which have a specific or general affinity to DNA and include three important components: transcription factors; nucleases, and histones. DNA-binding proteins also perform important roles in many types of cellular activities. In this paper we describe machine learning systems for the prediction of DNAbinding proteins where a Support Vector Machine and...

Prediction of DNA-binding sites in proteins using evolutionary profiles

Systematic prediction of control proteins and their DNA binding sites

We present here the results of a systematic bioinformatics analysis of control (C) proteins, a class of DNA-binding regulators that control time-delayed transcription of their own genes as well as restriction endonuclease genes in many type II restriction-modification systems. More than 290 C protein homologs were identified and DNA-binding sites for approximately 70% of new and previously know...