View Volume: https://doi.org/10.9734/bpi/mapr/v2

For my numerical methods final project we are plotting boobs tehe

Two posts ago talked about minimizing the force field parameters. Turns out, it's been done and used. It's neat that I was able to come up with this solution entirely on my own. It's like doing a derivation from scratch and then finding out that it was correct. On the other hand, if only I was 16 years earlier to this party. I could have had 125+ citations to my name. I would have been the nerdiest 11 year old ever.

The gif shown here is the algorithm at work. The contour plot is of Rosenbrock's Banana. With the coefficients that I used, the global minimum exists at (1,1). The red triangle is the simplex reaching and feeling out for the minimum of this function.

The understanding of folding and function of RNA molecules depends on the identification and classification of interactions between ribonucleotide residues. We developed a new method named ClaRNA for computational classification of contacts in RNA 3D structures. Unique features of the program are the ability to identify imperfect contacts and to process coarse-grained models. Each doublet of spatially close ribonucleotide residues in a query structure is compared to clusters of reference doublets obtained by analysis of a large number of experimentally determined RNA structures, and assigned a score that describes its similarity to one or more known types of contacts, including pairing, stacking, base–phosphate and base–ribose interactions. The accuracy of ClaRNA is 0.997 for canonical base pairs, 0.983 for non-canonical pairs and 0.961 for stacking interactions. The generalized squared correlation coefficient (GC2) for ClaRNA is 0.969 for canonical base pairs, 0.638 for non-canonical pairs and 0.824 for stacking interactions. The classifier can be easily extended to include new types of spatial relationships between pairs or larger assemblies of nucleotide residues. ClaRNA is freely available via a web server that includes an extensive set of tools for processing and visualizing structural information about RNA molecules.

A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.

Small regulatory RNAs (sRNAs) in bacteria regulate many important cellular activities under normal conditions and in response to stress. Many sRNAs bind to the mRNA targets at or near the 5′ untranslated region (UTR) resulting in translation inhibition and accelerated degradation. Often the sRNA-binding site is adjacent to or overlapping with the ribosomal binding site (RBS), suggesting a possible interplay between sRNA and ribosome binding. Here we combine quantitative experiments with mathematical modeling to reveal novel features of the interaction between small RNAs and the translation machinery at the 5’UTR of a target mRNA. By measuring the response of a library of reporter targets with varied RBSs, we find that increasing translation rate can lead to increased repression. Quantitative analysis of these data suggests a recruitment model, where bound ribosomes facilitate binding of the sRNA. We experimentally verified predictions of this model for the cell-to-cell variability of target expression. Our findings offer a framework for understanding sRNA silencing in the context of bacterial physiology.