Designing and using RNA scaffolds to assemble proteins in vivo

RNA scaffolds are synthetic noncoding RNA molecules with engineered 3D folding harnessed to spatially organize proteins in vivo. Here we provide a protocol to design, express and characterize RNA scaffolds and their cognate proteins within 1 month. The RNA scaffold designs described here are based on either monomeric or multimeric units harboring RNA aptamers as protein docking sites. The scaffolds and proteins are cloned into inducible plasmids and expressed to form functional assemblies. RNA scaffolds find applications in many fields in which in vivo organization of biomolecules is of interest. RNA scaffolds provide extended flexibility compared with DNA or protein scaffolding strategies through programmed modulation of multiple protein stoichiometry and numbers, as well as the proteins’ relative distances and spatial orientations. For synthetic biology, RNA scaffolds provide a new platform that can be used to increase yields of sequential metabolic pathways.


Tool Developer Website Summary
mfold University of Albany RNA folding software; folding temperature and ionic conditions are fixed
NUPACK California Institute of Technology RNA software suite for design and folding analysis with the option of designing RNA reaction pathways
RNA Designer University of British Columbia RNA design tool using the dot-bracket format; temperature and GC content are adjustable
RBS Calculator Penn State University Predicts translation initiation rate in bacteria; takes into account RNA secondary structures for predictions
Nucleotide BLAST National Center for Biotechnology Information BLAST compares nucleotide sequences to sequence database and calculates the statistical significance of any match
Primer-BLAST National Center for Biotechnology Information Uses the popular primer3 engine to design primers; results are submitted to BLAST to check for unwanted endogenous match
BioNumbers Harvard Medical School Registry of useful biological numbers, including genomic GC contents
genormPLUS Biogazelle Algorithm to determine the most stable reference genes from a set of tested candidate reference genes in a given qPCR sample panel

JBEI-ICE, part registry platform and tools

The Joint BioEnergy Institute Inventory of Composable Elements (JBEI-ICEs) is an open source registry platform for managing information about biological parts. It is capable of recording information about ‘legacy’ parts, such as plasmids, microbial host strains and Arabidopsisseeds, as well as DNA parts in various assembly standards. ICE is built on the idea of a web of registries and thus provides strong support for distributed interconnected use. The information deposited in an ICE installation instance is accessible both via a web browser and through the web application programming interfaces, which allows automated access to parts via third-party programs. JBEI-ICE includes several useful web browser-based graphical applications for sequence annotation, manipulation and analysis that are also open source. As with open source software, users are encouraged to install, use and customize JBEI-ICE and its components for their particular purposes. As a web application programming interface, ICE provides well-developed parts storage functionality for other synthetic biology software projects. A public instance is available at, where users can try out features, upload parts or simply use it for their projects. The ICE software suite is available via Google Code ( , a hosting site for community-driven open source projects.


Maximum expected accuracy structural neighbors of an RNA secondary structure

Since RNA molecules regulate genes and control alternative splicing by allostery, it is important to develop algorithms to predict RNA conformational switches. Some tools, such as paRNAss, RNAshapes and RNAbor, can be used to predict potential conformational switches; nevertheless, no existent tool can detect general (i.e., not family specific) entire riboswitches (both aptamer and expression platform) with accuracy. Thus, the development of additional algorithms to detect conformational switches seems important, especially since the difference in free energy between the two metastable secondary structures may be as large as 15-20 kcal/mol. It has recently emerged that RNA secondary structure can be more accurately predicted by computing the maximum expected accuracy (MEA) structure, rather than the minimum free energy (MFE) structure.


Source code for RNAborMEA can be downloaded from or

Apps Collections for Synthetic Biology


Synthetic biology which is inspired by “plug-and-play” concept is supported by standardized biobricks and also computational tools for analysis and optimization [1]. Here are bio-apps collections for you. Personally Tinkercell and UNAFold are mostly used.

Circuit design and simulation
Circuit optimization
DNA and RNA design
 Gene Designer
 Vienna RNA package∼ivo/RNA/
 Vienna RNA web servers
 Zinc Finger Tools
Protein design
 Autodock 4.2
 HEX 5.1∼ritchied/hex/
Integrated workflows


Suggested pages listing useful bio-apps maybe you will like in synthetic biology research:

  1. WikiGenes Toolbox ( As a free on-line tutorial edited by people all around the world, WikiGenes has spirits of Linux: open-source, sharing, interest, so is WikiGenes-Synthetic-Biology page. It enables you fast-learn what is synthetic life.
  2. OpenWetWare (
  3. JCVI ( This page is from J. Craig Venter Institute.


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[1] Marchisio, M. A., & Stelling, J. (2009). Computational design tools for synthetic biology. Current Opinion in Biotechnology, 20(4), 479-485. Retrieved from

DNA Nanotechnology Kills Cancer

Here I recommend you a wonderful bio-app, named: caDNAno. The open-source software, caDNAno,  will enables you  design  three-dimensional
DNA origami nanostructures. You can fold your DNA in any shapes. One of the features is no programming required, thus even our kids can give it a try, and perhaps can get a Science paper published [1].

What will DNA origami do?

Drug loaded and kill cancer cells. DNA is known to carry the genetic information, but here DNA changes its faces. No more genetic codes. Since the four basic bricks of DNA, A, T, G and C, will bind in pairs (A-T, G-C) according to Watson-Crick base paring principles, scientists could use the principle and engineer the DNA self-assembly to get designed 3-D structures, for example, a medicine capsule.

How does DNA robot work?

First we need drug loaded. When DNA is designed to a box with the help of caDNAno for example, it can be loaded with drugs, which is easy.

Second part, killing target without fault, is tough and tricky, just as quoted from the author.

Whether or not these structures will work in a living organism remains to be seen. For one thing, they are designed to communicate with molecules on a cell’s surface. “If your therapeutic target is inside the cell, it’s going to be tricky,” says Bachelet, a postdoctoral fellow at Harvard Medical School in Boston, Massachusetts, and one of the authors of the study [1].

One method to achieve target-killing, in my mind, is still to use the base paring principles. DNA nano-box per se is made up of four bases, thus a sequence can be designed as a switch to open or keep locking the nano-box.

Is it safe? 

This is question is inevitable though I don’t want to expand. DNA nano-robot, is intrinsic DNA sequence, thus it is sensitive to DNase, enzyme to degrade DNA. Once DNA nano-robots are destroyed, which is quite easy, the payload will be leaked out to be harmful to nearby cells and tissues.

What can DNA nanotechnology do? 

DNA nanotechnology can be applied to bio-computing and shows great promise. Qian, researches at California Institute of Technology, built up a bio-computing network based on DNA chain displacement. It is straightforward to watch the introduction video provided in the original paper [2].



DNA nanotechnology has a long way to go, nevertheless it shows promise in bio-computing and future therapeutics.

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[1] Douglas, S. M., Bachelet, I., & Church, G. M. (2012). A Logic-Gated Nanorobot for Targeted Transport of Molecular Payloads. Science, 335(6070), 831-834. doi:10.1126/science.1214081

[2] Qian, L., & Winfree, E. (2011). Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades. Science, 332(6034), 1196-1201. doi:10.1126/science.1200520