Structure and dynamics of nucleosomal DNA

The last five years have seen exciting advances in our understanding of the structure of the nucleosome core particle, the basic repeating unit in all eukaryotic chromatin. A picture emerges in which nucleosomal DNA, while distorted and compacted fivefold by tight interactions with the histone octamer core, is at the same time highly dynamic and adaptable. Here, we summarize the salient features from recent structural studies of nucleosome core particles (both published and unpublished) that concern the structure and dynamics of nucleosomal DNA, and the nature of protein-DNA interactions. Current mechanisms for chromatin remodeling and nucleosome sliding are discussed in light of new structural evidence. Finally, techniques to study nucleosome stability and ultimately dynamics are introduced.     

Alternative Modes of Binding of Poly(ADP-ribose) Polymerase 1 to Free DNA and Nucleosomes

Poly(ADP-ribose) polymerase 1 (PARP-1) is an abundant nuclear protein that binds chromatin and catalyzes the transfer of ADP-ribose groups to itself and to numerous target proteins upon interacting with damaged DNA. The molecular basis for the dual role of PARP-1 as a chromatin architectural protein and a first responder in DNA repair pathways remains unclear. Here, we quantified the interactions of full-length PARP-1 and its N-terminal half with different types of DNA damage and with defined nucleosome substrates. We found that full-length PARP-1 prefers nucleosomes with two linker DNA extensions over any other substrate (including several free DNA models) and that the C-terminal half of PARP-1 is necessary for this selectivity. We also measured the ability of various substrates to activate PARP-1 activity and found that the most important feature for activation is one free DNA end rather than tight interaction with the activating nucleic acid. Our data provide insight into the different modes of interaction of this multidomain protein with nucleosomes and free DNA.     

Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?

There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable, overfitting classification rules under small-sample situations. However, the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable, nonoverfitting classifiers, in the case of small-sample genomic and proteomic data sets. To investigate that question, we conducted a detailed empirical study, using publicly-available data sets from published genomic and proteomic studies. We observed that, under t-test and RELIEF filter-based feature selection, bagging generally does a good job of improving the performance of unstable, overfitting classifiers, such as CART decision trees and neural networks, but that improvement was not sufficient to beat the performance of single stable, nonoverfitting classifiers, such as diagonal and plain linear discriminant analysis, or 3-nearest neighbors. Furthermore, as expected, the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work.