Strand-switching mechanism of Pif1 helicase induced by its collision with a G-quadruplex embedded in dsDNA

G-rich sequences found at multiple sites throughout all genomes may form secondary structures called G-quadruplexes (G4), which act as roadblocks for molecular motors. Among the enzymes thought to process these structures, the Pif1 DNA helicase is considered as an archetypical G4-resolvase and its absence has been linked to G4-related genomic instabilities in yeast. Here we developed a single-molecule assay to observe Pif1 opening a DNA duplex and resolving the G4 in real time. In support of former enzymological studies, we show that the helicase reduces the lifetime of G4 from hours to seconds. However, we observe that in presence of a G4, Pif1 exhibits a strong strand switching behavior, which can lead to Pif1 escaping G4 resolution, depending on the structural context surrounding the substrate. This behavior is also detected in presence of other roadblocks (LNA or RNA). We propose that the efficiency of Pif1 to remove a roadblock (G4 or other) is affected by its strand switching behavior and depends on the context surrounding the obstacle. We discuss how this switching behavior may explain several aspects of Pif1 substrate preference and affect its activity as a G4 resolvase in vivo.

LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences

Background The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. Results We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. Conclusions With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases.

The Metaxin Mitochondrial Import Proteins: Multiple Metaxin-like Proteins in Fungi

Multiple metaxin-like proteins are shown to exist in fungi, as also found for the metaxin proteins of vertebrates and invertebrates. In vertebrates, metaxins 1 and 2 are mitochondrial membrane proteins that function in the import of proteins into mitochondria. Fungal metaxin-like proteins were identified by criteria including their homology with human metaxins and the presence of characteristic GST_N_Metaxin, GST_C_Metaxin, and Tom37 protein domains. Fungi in different taxonomic divisions (phyla) were found to possess multiple metaxin-like proteins. These include the Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, Neocallimastigomycota, and Zoopagomycota divisions. Most fungi with multiple metaxin-like proteins contain two proteins, designated MTXa and MTXb. Amino acid sequence alignments show a high degree of homology among MTXa proteins, with over 60% amino acid identities, and also among MTXb proteins of fungi in the same division. But very little homology is observed in aligning MTXa with MTXb proteins of the same or different fungi. Both the MTXa proteins and MTXb proteins have the protein domains that characterize the metaxins and metaxin-like proteins: GST_N_Metaxin, GST_C_Metaxin, and Tom37. The metaxins and metaxin-like proteins of vertebrates, invertebrates, plants, protists, and bacteria all possess these domains. The secondary structures of MTXa and MTXb proteins are both dominated by similar patterns of α-helical segments, but extensive β-strand segments are absent. Nine highly conserved α-helical segments are present, the same as other metaxins and metaxin-like proteins. Phylogenetic analysis reveals that MTXa and MTXb proteins of fungi form two separate and distinct groups. These groups are also separate from the groups of vertebrate metaxins, metaxin-related Sam37 proteins of yeasts, and metaxin-like FAXC proteins.

Secondary structure specific simpler prediction models for protein backbone angles

Motivation Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way. Results The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles $$\phi$$ ϕ , $$\psi$$ ψ , $$\theta$$ θ , and $$\tau$$ τ . Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results. Availability SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss.

A Multidentate Polymer Microreactor Route for Green Massive Fabrication of Mesoporous NaYF4 Clusters

We report a ‘‘multidentate polymer microreactor’’ method for the creation of secondary structures of colloidal nanocrystals. Using NaYF4: Yb, Er as an example, we demonstrate that the use of sodium...

Exploration of α/β/γ-peptidomimetics design for BH3 helical domain.

Systematic incorportation of ring-constrained β- and γ-amino acid residues to α-helix mimetics engenders stable helical secondary structures. In this paper, functional α/β/γ-helical peptidomimetics were explored for the mimicry of BH3...

Enrichment of non-B-form DNA at D. melanogaster centromeres

Centromeres are essential chromosomal regions that mediate the accurate inheritance of genetic information during eukaryotic cell division. Despite their conserved function, centromeres do not contain conserved DNA sequences and are instead epigenetically marked by the presence of the centromere-specific histone H3 variant CENP-A (centromeric protein A). The functional contribution of centromeric DNA sequences to centromere identity remains elusive. Previous work found that dyad symmetries with a propensity to adopt non-canonical secondary DNA structures are enriched at the centromeres of several species. These findings lead to the proposal that such non-canonical DNA secondary structures may contribute to centromere specification. Here, we analyze the predicted secondary structures of the recently identified centromere DNA sequences from Drosophila melanogaster. Although dyad symmetries are only enriched on the Y centromere, we find that other types of non-canonical DNA structures, including DNA melting and G-quadruplexes, are common features of all D. melanogaster centromeres. Our work is consistent with previous models suggesting that non-canonical DNA secondary structures may be conserved features of centromeres with possible implications for centromere specification.

Characteristics, origin, and potential for cancer diagnostics of ultrashort plasma cell-free DNA

Current evidence suggests that plasma cell-free DNA (cfDNA) is fragmented around a mode of 166 bp. Data supporting this view has been mainly acquired through the analysis of double-stranded cfDNA. The characteristics and diagnostic potential of single-stranded and damaged double-stranded cfDNA in healthy individuals and cancer patients remain unclear. Here, through a combination of high-affinity magnetic bead-based DNA extraction and single-stranded DNA sequencing library preparation (MB-ssDNA), we report the discovery of a large proportion of cfDNA fragments centred at ~50 bp. We show that these 'ultrashort' cfDNA fragments have a greater relative abundance in plasma of healthy individuals (median = 19.1% of all sequenced cfDNA fragments, n = 28) than in plasma of patients with cancer (median = 14.2%, n = 21, P < 0.0001). The ultrashort cfDNA fragments map to accessible chromatin regions of blood cells, particularly in promoter regions with the potential to adopt G-quadruplex (G4) DNA secondary structures. G4-positive promoter chromatin accessibility is significantly enriched in ultrashort plasma cfDNA fragments from healthy individuals relative to patients with cancers (P < 0.0001), in whom G4-cfDNA enrichment is inversely associated with copy number aberration-inferred tumor fractions. Our findings redraw the landscape of cfDNA fragmentation by identifying and characterizing a novel population of ultrashort plasma cfDNA fragments. Sequencing of MB-ssDNA libraries could facilitate the characterization of gene regulatory regions and DNA secondary structures via liquid biopsy. Our data underline the diagnostic potential of ultrashort cfDNA through classification for cancer patients.

Caveats to deep learning approaches to RNA secondary structure prediction

Machine learning (ML) and in particular deep learning techniques have gained popularity for predicting structures from biopolymer sequences. An interesting case is the prediction of RNA secondary structures, where well established biophysics based methods exist. These methods even yield exact solutions under certain simplifying assumptions. Nevertheless, the accuracy of these classical methods is limited and has seen little improvement over the last decade. This makes it an attractive target for machine learning and consequently several deep learning models have been proposed in recent years. In this contribution we discuss limitations of current approaches, in particular due to biases in the training data. Furthermore, we propose to study capabilities and limitations of ML models by first applying them on synthetic data that can not only be generated in arbitrary amounts, but are also guaranteed to be free of biases. We apply this idea by testing several ML models of varying complexity. Finally, we show that the best models are capable of capturing many, but not all, properties of RNA secondary structures. Most severely, the number of predicted base pairs scales quadratically with sequence length, even though a secondary structure can only accommodate a linear number of pairs.

Collinearity of alpha-helices or beta strands in membrane proteins causes a characteristic peak centred on 4.9 Å resolution in diffraction intensity profiles, inducing higher diffraction anisotropy

Diffraction anisotropy is a phenomenon that impacts more specifically membrane proteins, compared to soluble ones, but the reasons for this discrepancy remained unclear. Often, it is referred to a difference in resolution limits between highest and lowest diffraction limits as a signature for anisotropy. We show in this article that there is no simple correlation between anisotropy and difference in resolution limits, with notably a substantial number of structures displaying various anisotropy with no difference in resolution limits. We further investigated diffraction intensity profiles, and observed a peak centred on 4.9Å resolution more predominant in membrane proteins. Since this peak is in the region corresponding to secondary structures, we investigated the influence of secondary structure ratio. We showed that secondary structure content has little influence on this profile, while secondary structure collinearity in membrane proteins correlate with a stronger peak. Finally, we could further show that the presence of this peak is linked to higher diffraction anisotropy.SynopsisMembrane protein diffraction anisotropy originates from a peak at 4.9 Å resolution in intensity profiles, due to secondary structure collinearity.