Biology subjects

Mondragon, E.

Publications and source records attributed to Mondragon, E..

4 recordsLinked to original sources

bioRxiv2018

Structure-activity relationship of flavin analogs that target the FMN riboswitch

The flavin mononucleotide (FMN) riboswitch is an emerging target for the development of novel RNA-targeting antibiotics. We previously discovered an FMN derivative --5FDQD-- that protects mice against diarrhea-causing Clostridium difficile bacteria. Here, we present the structure-based drug design strategy that led to the discovery of this fluoro-phenyl derivative with antibacterial properties. This approach involved the following stages: (1) structural analysis of all available free and bound FMN riboswitch structures; (2) design, synthesis and purification of derivatives; (3) in vitro testing for productive binding using two chemical probing methods; (4) in vitro transcription termination assays; (5) resolution of the crystal structures of the FMN riboswitch in complex with the most mature candidates. In the process, we delineated principles for productive binding to this riboswitch, thereby demonstrating the effectiveness of a coordinated structure-guided approach to designing drugs against RNA.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC=\"FIGDIR/small/389148_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (41K):\norg.highwire.dtl.DTLVardef@f59798org.highwire.dtl.DTLVardef@1b38b02org.highwire.dtl.DTLVardef@6b50faorg.highwire.dtl.DTLVardef@1915fc4_HPS_FORMAT_FIGEXP M_FIG Exploring the chemical structure landscape of FMN riboswitch binders.\n\nC_FIG

biochemistry↗

bioRxiv2018

Binding specificities of human RNA binding proteins towards structured and linear RNA sequences

Sequence specific RNA-binding proteins (RBPs) control many important processes affecting gene expression. They regulate RNA metabolism at multiple levels, by affecting splicing of nascent transcripts, RNA folding, base modification, transport, localization, translation and stability. Despite their central role in most aspects of RNA metabolism and function, most RBP binding specificities remain unknown or incompletely defined. To address this, we have assembled a genome-scale collection of RBPs and their RNA binding domains (RBDs), and assessed their specificities using high throughput RNA-SELEX (HTR-SELEX). Approximately 70% of RBPs for which we obtained a motif bound to short linear sequences, whereas ~30% preferred structured motifs folding into stem-loops. We also found that many RBPs can bind to multiple distinctly different motifs. Analysis of the matches of the motifs in human genomic sequences suggested novel roles for many RBPs. We found that three cytoplasmic proteins, ZC3H12A, ZC3H12B and ZC3H12C bound to motifs resembling the splice donor sequence, suggesting that these proteins are involved in degradation of cytoplasmic viral and/or unspliced transcripts. Surprisingly, structural analysis revealed that the RNA motif was not bound by the conventional C3H1 RNA-binding domain of ZC3H12B. Instead, the RNA motif was bound by the ZC3H12Bs PilT N-terminus (PIN) RNase domain, revealing a potential mechanism by which unconventional RNA binding domains containing active sites or molecule-binding pockets could interact with short, structured RNA molecules. Our collection containing 145 high resolution binding specificity models for 86 RBPs is the largest systematic resource for the analysis of human RBPs, and will greatly facilitate future analysis of the various biological roles of this important class of proteins.

biochemistry↗

bioRxiv2017

A Real-Time Double Error Correction Model of Associative Learning

In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed.

animal behavior and cognition↗

bioRxiv2017

A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used.\n\nAuthor SummaryHow does the time of events affect the way we learn about associations between these events? Computational models have made great contributions to our understanding of associative learning, but they usually do not perform very well when time is taken into account. Models of timing have reached high levels of accuracy in describing timed behaviour, but they usually do not have much to say about associations. A unified approach would involve combining associative learning and timing models into a single framework. This article takes just this approach. It combines the influential Rescorla-Wagner associative model with a timing model based on the Drift-Diffusion process, and shows how the resultant model can account for a number of learning and timing phenomena. The article also compares the new model to others that are similar in scope.

animal behavior and cognition↗