#computational methods

[asteria-ofthestars]

the only thing keeping me from regularly crashing out in this computational methods and software class is the fact that my professor actually puts things in terms that i'm able to understand. i can't stand calculus usually, but this prof makes it a bit more enjoyable, somehow

[bookpiofficial]

The Future of Prodrugs Design | Chapter 03 | Modern Advances in Pharmaceutical Research Vol. 2

When knowledge fails to provide answers to important questions such as how to improve the bioavailability of vital medications, “Imagination is more important than knowledge,” as Albert Einstein once said. Ingenuity in the design of effective prodrugs has been lacking in quantity and quality. The reasons behind the low quality of ingenuity could be related to the fact that medicinal chemists have expertise in organic and organometalic chemistry not in biochemistry and biology. On the other hand, pharmaceutical chemists, biologists and biochemists do not have the expertise to make sophisticated chemical devices. Therefore, in order for a prodrug strategy to work, a team consisting of all this expertise is necessary.

Author(s) Details

Professor Rafik Karaman Department of Bioorganic Chemistry, Faculty of Pharmacy, Al-Quds University, P.O. Box 20002, Jerusalem, Palestine and Department of Sciences, University of Basilicata, Viadell’Ateneo Lucano 10, 85100, Potenza, Italy.

Read full article: http://bp.bookpi.org/index.php/bpi/catalog/view/85/1196/839-1

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

[plinythesassier]

For my numerical methods final project we are plotting boobs tehe

[tebtebbert]

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.

[tebtebbert]

It's been a while. I haven't been writing like I am supposed to. So I figure I might write something of substance today.

Molecular dynamics is a finicky business. The problem of MD is, at its core, a many body problem. You sum up pairwise electrostatic interactions, toss it into Newton's equations, then watch a bunch of pixels lurch forward in time. This process is repeated ad nauseum.

Pairwise sums of anything scale as O(n^2). Ain't nobody got time for that. So some very smart people get together and figure out all sorts of approximations and tricks to reduce that 2 into something much more manageable. The particular approximation I use is called the General AMBER Force Field (GAFF).

The use of any approximation introduces error. In this case, we can get around them by modifying the two constants associated with the Lennard-Jones potential for each particular atom type. One simply plays around with them, and checks to see if density and some other measurements of your substance match with accepted values.

Now there are no hard and fast rules for doing this sort of thing. It's possible that more than one combination of constants will lead to the correct density. Also, there's often no good indication of how to even go about adjusting these parameters in the first place.

I hate work. My motto is "Automate what can be automated. Make automatable what cannot." So I wasn't about to go and try to figure this out on my own. Instead, I programmed a minimizer to do that biz for me.

I popped open "Numerical Recipes" and implemented the Nelder-Mead simplex algorithm. A simplex is a general name for an n-dimensional polyhedron. The vertices of this shape corresponds to a particular set of parameters. The simplex then navigates through the solution space much the same way an amoeba might crawl through some primordial goop. Eventually, it will happen upon a minimum and the corners of the simplex will cage around the solution.

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.

[sulantha]

New Post has been published on Bioinformatics Research Feeds

New Post has been published on http://bioinformatics.researchfeeds.com/clarna-a-classifier-of-contacts-in-rna-3d-structures-based-on-a-comparative-analysis-of-various-classification-schemes/

ClaRNA: a classifier of contacts in RNA 3D structures based on a comparative analysis of various classification schemes

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.

[sulantha]

New Post has been published on Bioinformatics Research Feeds

New Post has been published on http://bioinformatics.researchfeeds.com/foam-functional-ontology-assignments-for-metagenomes-a-hidden-markov-model-hmm-database-with-environmental-focus/

FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus

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/.

[sulantha]

New Post has been published on Bioinformatics Research Feeds

New Post has been published on http://bioinformatics.researchfeeds.com/quantitative-effect-of-target-translation-on-small-rna-efficacy-reveals-a-novel-mode-of-interaction/

Quantitative effect of target translation on small RNA efficacy reveals a novel mode of interaction

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.