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DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the(More)
Annotation of the functional sites on the surface of a protein has been the subject of many studies. In this regard, the search for attributes and features characterizing these sites is of prime consequence. Here, we present an implementation of a kernel-based machine learning protocol for identifying residues on a DNA-binding protein form the interface(More)
Proteinases play critical roles in both intra and extracellular processes by binding and cleaving their protein substrates. The cleavage can either be non-specific as part of degradation during protein catabolism or highly specific as part of proteolytic cascades and signal transduction events. Identification of these targets is extremely challenging.(More)
MOTIVATION Protein-lipid interactions play a central role in cellular signaling and membrane trafficking and at the core of these interactions are domains specialized in lipid binding and membrane targeting. Considering the importance of these domains, we have created MeTaDoR, a comprehensive resource dedicated to membrane targeting domains (MTDs). RESULT(More)
Male musk deer secrete musk from the musk gland located between their naval and genitals. Unmated male forest musk deer generate a greater amount of musk than mated males, potentially allowing them to attract a greater number of females. In this study, we used gas chromatography and mass spectrometry (GC/MS) to explore musk chemical composition of the musk(More)
Random Forest (RF) is a family of classifier ensemble methods that use randomization to produce a diverse pool of individual classifiers, as for Bagging [Breiman96] or Random Subspaces methods [Ho98]. Those methods have shown to be particularly competitive with one of the most efficient learning principles, i.e. boosting [Breiman01,CZ01,RKA06]. However, the(More)
A key component in protein structure prediction is the development of potentials that can discriminate native or near native structures from the wrong ones. Most previously developed statistical potentials are based on the preferred distances between any pair of residues. Here we explore the possible angle dependence between pairs of residues in addition to(More)
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