Nucleosomes are barriers to transcription in vitro; however, their effects on RNA polymerase in vivo are unknown. Here we describe a simple and general strategy to comprehensively map the positions of elongating and arrested RNA polymerase II (RNAPII) at nucleotide resolution. We find that the entry site of the first (+1) nucleosome is a barrier to RNAPII for essentially all genes, including those undergoing regulated pausing farther upstream. In contrast to the +1 nucleosome, gene body nucleosomes are low barriers and cause RNAPII stalling both at the entry site and near the dyad axis. The extent of the +1 nucleosome barrier correlates with nucleosome occupancy but anticorrelates with enrichment of histone variant H2A.Z. Importantly, depletion of H2A.Z from a nucleosome position results in a higher barrier to RNAPII. Our results suggest that nucleosomes present significant, context-specific barriers to RNAPII in vivo that can be tuned by the incorporation of H2A.Z.

SUMMARY Previous studies suggested that expression of most yeast mRNAs is dominated by either transcription factor TFIID or SAGA. We reexamined the role of TFIID by rapid depletion of S. cerevisiae TFIID subunits and measurement of changes in nascent transcription. We find that transcription of nearly all mRNAs is strongly dependent on TFIID function. Degron-dependent depletion of Tafs 1,2,7,11, and 13 showed similar transcription decreases for genes in the Taf1-depleted, Taf1-enriched, TATA-containing and TATA-less gene classes. The magnitude of TFIID-dependence varies with growth conditions, although this variation is similar genome-wide. Many studies have suggested differences in gene regulatory mechanisms between TATA and TATA-less genes and these differences have been attributed in part to differential dependence on SAGA or TFIID. Our work indicates that TFIID participates in expression of nearly all yeast mRNAs and that differences in regulation between these two gene categories is due to other properties.

Until now it has been impractical to observe protein folding in silico for proteins larger than 50 residues. Limitations of both force field accuracy and computational efficiency make the folding problem very challenging. Here we employ discrete molecular dynamics (DMD) simulations with an all-atom force field to fold fast-folding proteins. We extend the DMD force field by introducing long-range electrostatic interactions to model salt-bridges and a sequence-dependent semi-empirical potential accounting for natural tendencies of certain amino acid sequences to form specific secondary structures. We enhance the computational performance by parallelizing the DMD algorithm. Using a small number of commodity computers, we achieve sampling quality and folding accuracy comparable to the explicit-solvent simulations performed on high-end hardware. We demonstrate that DMD can be used to observe equilibrium folding of villin headpiece and WW domain, study two-state folding kinetics and sample near-native states in ab initio folding of proteins of ~100 residues.

Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two non-bonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared to other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.