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Abstract: We have performed simulated tempering molecular dynamics simulations to study the thermodynamics of the headpiece of the Huntingtin (Htt) protein (N17Htt). With converged sampling, we found this peptide is highly helical, as previously proposed. Interestingly, this peptide is also found to adopt two different and seemingly stable states. The region from residue 4 (L) to residue 9 (K) has a strong helicity from our simulations, which is supported by experimental studies. However, contrary to what was initially proposed, we have found that simulations predict the most populated state as a two-helix bundle rather than a single straight helix, although a significant percentage of structures do still adopt a single linear helix. The fact that Htt aggregation is nucleation dependent infers the importance of a critical transition. It has been shown that N17Htt is involved in this rate-limiting step. In this study, we propose two possible mechanisms for this nucleating event stemming from the transition between two-helix bundle state and single-helix state for N17Htt and the experimentally observed interactions between the N17Htt and polyQ domains. More strikingly, an extensive hydrophobic surface area is found to be exposed to solvent in the dominant monomeric state of N17Htt. We propose the most fundamental role played by N17Htt would be initializing the dimerization and pulling the polyQ chains into adequate spatial proximity for the nucleation event to proceed. [Copyright &y& Elsevier]

RNA polymerase transcribes certain genomic loci with higher errors rates. These transcription error-enriched genomic loci (TEELs) have implications in disease. Current deep-sequencing methods cannot distinguish TEELs from post-transcriptional modifications, stochastic transcription errors, and technical noise, impeding efforts to elucidate the mechanisms linking TEELs to disease. Here, we describe background error model-coupled precision nuclear run-on circular-sequencing (EmPC-seq) to discern genomic regions enriched for transcription misincorporations. EmPC-seq innovatively combines a nuclear run-on assay for capturing nascent RNA before post-transcriptional modifications, a circular-sequencing step that sequences the same nascent RNA molecules multiple times to improve accuracy, and a statistical model for distinguishing error-enriched regions among stochastic polymerase errors. Applying EmPC-seq to the ribosomal RNA transcriptome, we show that TEELs of RNA polymerase I are not randomly distributed but clustered together, with higher error frequencies at nascent transcript 3′ ends. Our study establishes a reliable method of identifying TEELs with nucleotide precision, which can help elucidate their molecular origins. Unlabelled Image • EmPC-seq combines experimental, bioinformatics, and statistical methods to identify a high confidence set of transcriptional mutation-prone positions within genes, eliminating typically confounding factors arising from coverage biases, sequencing noise, alignment artifacts, and background mutations, • EmPC-seq affirmatively shows that transcriptional mutation-prone regions cluster together. • EmPC-seq allows single transcript analyses to investigate of the molecular origin of transcriptional errors, and we show that transcriptional mutations are enriched at the active site. • EmPC-seq can be widely applicable for the study of transcription mutations in various model systems including virus, bacteria, and mammalian cells. [ABSTRACT FROM AUTHOR]