The widely used method to monitor the aggregation process of amyloid peptide is thioflavin T (ThT) assay, while the detailed molecular mechanism is still not clear. In this work, we report here the direct identification of the binding modes of ThT molecules with the prion peptide GNNQQNY by using scanning tunneling microscopy (STM). The assembly structures of GNNQQNY were first observed by STM on a graphite surface, and the introduction of ThT molecules to the surface facilitated the STM observations of the adsorption conformations of ThT with peptide strands. ThT molecules are apt to adsorb on the peptide assembly with β-sheet structure and oriented parallel with the peptide strands adopting four different binding modes. This effort could benefit the understanding of the mechanisms of the interactions between labeling species or inhibitory ligands and amyloid peptides, which is keenly needed for developing diagnostic and therapeutic approaches.

Over the last decade, dramatic advances have been made in developing methods for large-scale phylogeny estimation, so that it is now feasible for investigators with moderate computational resources to obtain reasonable solutions to maximum likelihood and maximum parsimony, even for datasets with a few thousand sequences. There has also been progress on developing methods for multiple sequence alignment, so that greater alignment accuracy (and subsequent improvement in phylogenetic accuracy) is now possible through automated methods. However, these methods have not been tested under conditions that reflect properties of datasets confronted by large-scale phylogenetic estimation projects. In this paper we report on a study that compares several alignment methods on a benchmark collection of nucleotide sequence datasets of up to 78,132 sequences. We show that as the number of sequences increases, the number of alignment methods that can analyze the datasets decreases. Furthermore, the most accurate alignment methods are unable to analyze the very largest datasets we studied, so that only moderately accurate alignment methods can be used on the largest datasets. As a result, alignments computed for large datasets have relatively large error rates, and maximum likelihood phylogenies computed on these alignments also have high error rates. Therefore, the estimation of highly accurate multiple sequence alignments is a major challenge for Tree of Life projects, and more generally for large-scale systematics studies.

Background: Longer CAG repeat length is associated with faster clinical progression in Huntington disease, although the effect of higher repeat length on brain atrophy is not well documented. Method: Striatal volumes were obtained from MRI scans of 720 individuals with prodromal Huntington disease. Striatal volume was plotted against age separately for groups with CAG repeat lengths of 38–39, 40, 41, 42, 43, 44, 45, 46, and 47–54. Results: Slopes representing the association between age and striatal volume were significantly steeper as CAG repeat length increased. Discussion: Although cross-sectional, these data suggest that striatal atrophy, like clinical progression, may occur faster with higher CAG repeat lengths.

Over the last decade, dramatic advances have been made in developing methods for large-scale phylogeny estimation, so that it is now feasible for investigators with moderate computational resources to obtain reasonable solutions to maximum likelihood and maximum parsimony, even for datasets with a few thousand sequences. There has also been progress on developing methods for multiple sequence alignment, so that greater alignment accuracy (and subsequent improvement in phylogenetic accuracy) is now possible through automated methods. However, these methods have not been tested under conditions that reflect properties of datasets confronted by large-scale phylogenetic estimation projects. In this paper we report on a study that compares several alignment methods on a benchmark collection of nucleotide sequence datasets of up to 78,132 sequences. We show that as the number of sequences increases, the number of alignment methods that can analyze the datasets decreases. Furthermore, the most accurate alignment methods are unable to analyze the very largest datasets we studied, so that only moderately accurate alignment methods can be used on the largest datasets. As a result, alignments computed for large datasets have relatively large error rates, and maximum likelihood phylogenies computed on these alignments also have high error rates. Therefore, the estimation of highly accurate multiple sequence alignments is a major challenge for Tree of Life projects, and more generally for large-scale systematics studies.

We have assembled a collection of web pages that contain benchmark datasets and software tools to enable the evaluation of the accuracy and scalability of computational methods for estimating evolutionary relationships. They provide a resource to the scientific community for development of new alignment and tree inference methods on very difficult datasets. The datasets are intended to help address three problems: multiple sequence alignment, phylogeny estimation given aligned sequences, and supertree estimation. Datasets from our work include empirical datasets with carefully curated alignments suitable for testing alignment and phylogenetic methods for large-scale systematics studies. Links to other empirical datasets, lacking curated alignments, are also provided. We also include simulated datasets with properties typical of large-scale systematics studies, including high rates of substitutions and indels, and we include the true alignment and tree for each simulated dataset. Finally, we provide links to software tools for generating simulated datasets, and for evaluating the accuracy of alignments and trees estimated on these datasets. We welcome contributions to the benchmark datasets from other researchers.

Over the last decade, dramatic advances have been made in developing methods for large-scale phylogeny estimation, so that it is now feasible for investigators with moderate computational resources to obtain reasonable solutions to maximum likelihood and maximum parsimony, even for datasets with a few thousand sequences. There has also been progress on developing methods for multiple sequence alignment, so that greater alignment accuracy (and subsequent improvement in phylogenetic accuracy) is now possible through automated methods. However, these methods have not been tested under conditions that reflect properties of datasets confronted by large-scale phylogenetic estimation projects. In this paper we report on a study that compares several alignment methods on a benchmark collection of nucleotide sequence datasets of up to 78,132 sequences. We show that as the number of sequences increases, the number of alignment methods that can analyze the datasets decreases. Furthermore, the most accurate alignment methods are unable to analyze the very largest datasets we studied, so that only moderately accurate alignment methods can be used on the largest datasets. As a result, alignments computed for large datasets have relatively large error rates, and maximum likelihood phylogenies computed on these alignments also have high error rates. Therefore, the estimation of highly accurate multiple sequence alignments is a major challenge for Tree of Life projects, and more generally for large-scale systematics studies.

The templated synthesis of nucleic acids has previously been achieved through the backbone ligation of preformed nucleotide monomers or oligomers. In contrast, here we demonstrate templated nucleic acid synthesis using a base-filling approach in which individual bases are added to abasic sites of a peptide nucleic acid (PNA). Because nucleobase substrates in this approach are not self-reactive, a base-filling approach may reduce the formation of nontemplated reaction products. Using either reductive amination or amine acylation chemistries, we observed efficient and selective addition of each of the four nucleobases to an abasic site in the middle of the PNA strand. We also describe the addition of single nucleobases to the end of a PNA strand through base filling, as well as the tandem addition of two bases to the middle of the PNA strand. These findings represent an experimental foundation for nonenzymatic information transfer through base filling.

In vitro selection is a key component of efforts to discover functional nucleic acids and small molecules from libraries of DNA, RNA, and DNA-encoded small molecules. Such selections have been widely used to evolve RNA and DNA catalysts and, more recently, to discover new reactions from DNA-encoded libraries of potential substrates. While effective, current strategies for selections of bond-forming and bond-cleaving reactivity are generally indirect, require the synthesis of biotin-linked substrates, and involve multiple solution-phase and solid-phase manipulations. In this work we report the successful development and validation of reactivity-dependent PCR (RDPCR), a new method that more directly links bond formation or bond cleavage with the amplification of desired sequences and that obviates the need for solid-phase capture, washing, and elution steps. We show that RDPCR can be used to select for bond formation in the context of reaction discovery and for bond cleavage in the context of protease activity profiling.

Cyanine dyes have been shown to undergo reversible photoswitching, where the fluorophore can be switched between a fluorescent state and a dark state upon illumination at different wavelengths. The photochemical mechanism by which switching occurs has yet to be elucidated. In this study, we have determined the mechanism of photoswitching by characterizing the kinetics of dark state formation and the spectral and structural properties of the dark state. The rate of switching to the dark state depends on the concentration of the primary thiol in the solution and the solution pH in a manner quantitatively consistent with the formation of an encounter complex between the cyanine dye and ionized thiol prior to their conjugation. Mass spectrometry suggests that the photoconversion product is a thiol−cyanine adduct in which covalent attachment of the thiol to the polymethine bridge disrupts the original conjugated π-electron system of the dye.

Interaction-dependent PCR (IDPCR) is a solution-phase method to identify binding partners from combined libraries of small-molecule ligands and targets in a single experiment. Binding between DNA-linked targets and DNA-linked ligands induces formation of an extendable duplex. Extension links codes that identify the ligand and target into one selectively amplifiable DNA molecule. In a model selection, IDPCR resulted in the enrichment of DNA encoding all five known protein−ligand pairs out of 67 599 possible sequences.

We developed a sequential strand-displacement strategy for multistep DNA-templated synthesis (DTS) and used it to mediate an efficient six-step DTS that proceeded in 35% overall yield (83% average yield per step). The efficiency of this approach and the fact that the final product remains linked to a DNA sequence that fully encodes its reaction history suggests its utility for the translation of DNA sequences into high-complexity synthetic libraries suitable for in vitro selection.

HNO can interact with numerous heme proteins, but atomic level structures are largely unknown. In this work, various structural models for the first stable HNO heme protein complex, MbHNO (Mb, myoglobin), were examined by quantum chemical calculations. This investigation led to the discovery of two novel structural models that can excellently reproduce numerous experimental spectroscopic properties. They are also the first atomic level structures that can account for the experimentally observed high stabilities. These two models involve two distal His conformations as reported previously for MbCNR and MbNO. However, a unique dual hydrogen bonding feature of the HNO binding was not reported before in heme protein complexes with other small molecules such as CO, NO, and O2. These results shall facilitate investigations of HNO bindings in other heme proteins.

Apratoxins are cytotoxic marine natural products that prevent cotranslational translocation early in the secretory pathway. We showed that apratoxins downregulate receptors and growth factor ligands, giving a one–two punch to cancer cells, particularly those that rely on autocrine loops. Through total synthesis, we tested the effects of amino acid substitutions, including alanine scanning, on the downregulation of receptor tyrosine kinases and vascular endothelial growth factor A (VEGF-A) and probed the stereospecificity of target engagement by epimerization of selected chiral centers. Differential effects on two types of secretory molecules suggest that the apratoxins' substrate selectivity with respect to inhibition of secretion may be tuned through structural modifications to provide tailored therapy. Our structure–activity relationship studies and medicinal chemistry efforts led to a potent inhibitor with in vivo efficacy in a colorectal tumor xenograft model without irreversible toxicity exerted by apratoxin A, demonstrating that this novel mechanism of action has therapeutic potential.

Protein dynamics on the micro- to millisecond time scale is increasingly found to be critical for biological function, as demonstrated by numerous NMR relaxation dispersion studies. Methyl groups are excellent probes of protein interactions and dynamics because of their favorable NMR relaxation properties, which lead to sharp signals in the 1H and 13C NMR spectra. Out of the six different methyl-bearing amino acid residue types in proteins, methionine plays a special role because of its extensive side-chain flexibility and the high polarizability of the sulfur atom. Methionine is over-represented in many protein–protein recognition sites, making the methyl group of this residue type an important probe of the relationships among dynamics, interactions, and biological function. Here we present a straightforward method to label methionine residues with specific 13CHD2 methyl isotopomers against a deuterated background. The resulting protein samples yield NMR spectra with improved sensitivity due to the essentially 100% population of the desired 13CHD2 methyl isotopomer, which is ideal for 1H and 13C spin relaxation experiments to investigate protein dynamics in general and conformational exchange in particular. We demonstrate the approach by measuring 1H and 13C CPMG relaxation dispersion for the nine methionines in calcium-free calmodulin (apo-CaM). The results show that the C-terminal domain, but not the N-terminal domain, of apo-CaM undergoes fast exchange between the ground state and a high-energy state. Since target proteins are known to bind specifically to the C-terminal domain of apo-CaM, we speculate that the high-energy state might be involved in target binding through conformational selection.

We developed a method to translate DNA sequences into densely functionalized nucleic acids by using T4 DNA ligase to mediate the DNA-templated polymerization of 5′-phosphorylated trinucleotides containing a wide variety of appended functional groups. This polymerization proceeds sequence specifically along a DNA template and can generate polymers of at least 50 building blocks (150 nucleotides) in length with remarkable efficiency. The resulting single-stranded highly modified nucleic acid is a suitable template for primer extension using deep vent (exo-) DNA polymerase, thereby enabling the regeneration of template DNA. We integrated these capabilities to perform iterated cycles of in vitro translation, selection, and template regeneration on libraries of modified nucleic acid polymers.