Faculty Bibliography

Histone amino termini are post-translationally modified by both transcriptional coactivators and corepressors, but the extent to which the relevant histone modifications contribute to gene expression, and the mechanisms by which they do so, are incompletely understood. To address this issue, we have examined the contributions of the histone H3 and H4 amino termini, and of the coactivator and histone acetyltransferase Gcn5p, to activation of a small group of Gcn4p-activated genes. The histone H3 tail exerts a modest (about 2-fold) but significant effect on activation that correlates with a requirement for Gcn5p and is distributed over multiple lysine residues. The H4 tail also plays a positive role in activation of some of those genes tested, but this does not correlate as closely with Gcn5p coactivation. Microarray experiments did not reveal a close correspondence between those genes activated by Gcn4p and genes requiring the H3 or H4 tail, and analysis of published microarray data indicates that Gcn4p-regulated genes are not in general strongly dependent on Gcn5p. However, a large fraction of genes activated by Gcn4p were found to be repressed by the H3 and H4 amino termini under non-inducing conditions, indicating that one role for Gcn4p is to overcome repression mediated by the histone tails

Clusters of transcription factor binding sites (TFBSs) which direct gene expression constitute cis-regulatory modules (CRMs). We present a novel algorithm, based on Gibbs sampling, which locates, de novo, the cis features of these CRMs, their component TFBSs, and the properties of their spatial distribution. The algorithm finds 69% of experimentally reported TFBSs and 85% of the CRMs in a reference data set of regions upstream of genes differentially expressed in skeletal muscle cells. A discriminant procedure based on the output of the model specifically discriminated regulatory sequences in muscle-specific genes in an independent test set. Application of the method to the analysis of 2710 10-kb fragments upstream of annotated human genes identified 17 novel candidate modules with a false discovery rate </=0.05, demonstrating the applicability of the method to genome-scale data

Particle classification is an important component of multivariate statistical analysis methods that has been used extensively to extract information from electron micrographs of single particles. Here we describe a new Bayesian Gibbs sampling algorithm for the classification of such images. This algorithm, which is applied after dimension reduction by correspondence analysis or by principal components analysis, dynamically learns the parameters of the multivariate Gaussian distributions that characterize each class. These distributions describe tilted ellipsoidal clusters that adaptively adjust shape to capture differences in the variances of factors and the correlations of factors within classes. A novel Bayesian procedure to objectively select factors for inclusion in the classification models is a component of this procedure. A comparison of this algorithm with hierarchical ascendant classification of simulated data sets shows improved classification over a broad range of signal-to-noise ratios

To study local structures in proteins, we previously developed an autoassociative artificial neural network (autoANN) and clustering tool to discover intrinsic features of macromolecular structures. The hidden unit activations computed by the trained autoANN are a convenient low-dimensional encoding of the local protein backbone structure. Clustering these activation vectors results in a unique classification of protein local structural features called Structural Building Blocks (SBBs). Here we describe application of this method to a larger database of proteins, verification of the applicability of this method to structure classification, and subsequent analysis of amino acid frequencies and several commonly occurring patterns of SBBs. The SBB classification method has several interesting properties: 1) it identifies the regular secondary structures, alpha helix and beta strand; 2) it consistently identifies other local structure features (e.g., helix caps and strand caps); 3) strong amino acid preferences are revealed at some positions in some SBBs; and 4) distinct patterns of SBBs occur in the 'random coil' regions of proteins. Analysis of these patterns identifies interesting structural motifs in the protein backbone structure, indicating that SBBs can be used as 'building blocks' in the analysis of protein structure. This type of pattern analysis should increase our understanding of the relationship between protein sequence and local structure, especially in the prediction of protein structures