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Characterization of transient intermediate or transition states is crucial for the description of biomolecular folding pathways, which is, however, difficult in both experiments and computer simulations. Such transient states are typically of low population in simulation samples. Even for simple systems such as RNA hairpins, recently there are mounting debates over the existence of multiple intermediate states. In this paper, we develop a computational approach to explore the relatively low populated transition or intermediate states in biomolecular folding pathways, based on a topological data analysis tool, MAPPER, with simulation data from large-scale distributed computing. The method is inspired by the classical Morse theory in mathematics which characterizes the topology of high-dimensional shapes via some functional level sets. In this paper we exploit a conditional density filter which enables us to focus on the structures on pathways, followed by clustering analysis on its level sets, which helps separate low populated intermediates from high populated folded/unfolded structures. A successful application of this method is given on a motivating example, a RNA hairpin with GCAA tetraloop, where we are able to provide structural evidence from computer simulations on the multiple intermediate states and exhibit different pictures about unfolding and refolding pathways. The method is effective in dealing with high degree of heterogeneity in distribution, capturing structural features in multiple pathways, and being less sensitive to the distance metric than nonlinear dimensionality reduction or geometric embedding methods. The methodology described in this paper admits various implementations or extensions to incorporate more information and adapt to different settings, which thus provides a systematic tool to explore the low-density intermediate states in complex biomolecular folding systems. [ABSTRACT FROM AUTHOR]

Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems. Lizhe Zhu et al. use a quasi Markov State Model built from extensive molecular dynamics simulations to elucidate the dynamics of RISC-mRNA interactions. Their results provide further insight on how a target nucleotide is positioned before recognition by Argonaute. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one common malignancy. The authors previously demonstrated that miR-570 regulates the development of HCC. This study detected the effect of miR-570 on cell apoptosis, angiogenesis, T cell activation, and proliferation in a tumorigenicity assay in nude mice. miR-570 mimics and negative control (NC) were transfected into SMMC7721 cells, and then, the cells were subcutaneously injected in the right flank in nude mice. Six weeks later, the dissected tumors and peripheral blood were collected. Tumor weight and volume were measured, and expression of miR-570 and apoptosis-related gene Bax/Bcl-2 was detected by quantitative real-time polymerase chain reaction. Hematoxylin and eosin, immunohistochemistry of CD31 and vascular endothelial growth factor (VEGF), TUNEL assay, and flow cytometry detection of CD4 and CD8 in peripheral blood were performed. miR-570 mimics suppressed tumor growth compared with the NC, with decreases in tumor weight and tumor volume. Very few CD31 and VEGF were found in tumor sections in miR-570 mimics group. Bax level was significantly increased, while Bcl-2 level was significantly downregulated. Significant lower ratio of CD3+CD4+ T cells and higher ratio of CD8+IFN-γ+ T cells were found in peripheral blood and tumor tissues in miR-570 mimics than NC. Collectively, miR-570 plays an important role in the proliferation, angiogenesis, and immune escape of HCC, which might be potential diagnostic and predictive biomarkers. [ABSTRACT FROM AUTHOR]

In this article, we review the recent progress in multiresolution modeling of structure and dynamics of protein, RNA and their complexes. Many approaches using both physics-based and knowledge-based potentials have been developed at multiple granularities to model both protein and RNA. Coarse graining can be achieved not only in the length, but also in the time domain using discrete time and discrete state kinetic network models. Models with different resolutions can be combined either in a sequential or parallel fashion. Similarly, the modeling of assemblies is also often achieved using multiple granularities. The progress shows that a multiresolution approach has considerable potential to continue extending the length and time scales of macromolecular modeling. [ABSTRACT FROM PUBLISHER]