New Robotic System to Help Improve Biomolecular Evolution

Researchers at the Massachusetts Institute of Technology have developed a phage- and robotics-assisted near-continuous evolution (PRANCE) platform that adjusts to real-time measurements of biomolecular activity.

New Robotic System to Help Improve Biomolecular Evolution

Image Credit: Burgstedt

Natural selection drives evolution. Researchers have sought to mimic this process to engineer proteins in controlled laboratory conditions. However, they have been limited in their ability to study the effects of population diversity, chance mutations and the environment on biomolecular development.

The MIT team was able to rapidly evolve three distinct types of biomolecules and demonstrate the effects of random and historical patterns on biomolecular development. Their work opens up new opportunities for the development of industrially relevant proteins.

A Brief Introduction to Phage-Assisted Continuous Evolution (PACE)

Proteins are among some of the most versatile macromolecules found in nature. As catalysts in many physiological and natural processes, they have been the subject of intense study.

Using recombinant DNA technology, protein engineering seeks to manipulate the sequence of the amino acids which make up these proteins to recreate molecules displaying diverse properties. Novel applications have been found in industry, medicine and nanotechnology.

Direct evolution is one of three methods used in the design of new protein molecules. It is particularly useful when the three-dimensional structure of the desired molecule is not well understood. In contrast, rational design is useful when this structure is well known.

Bacteriophages (or phages for short) are viruses that infect and replicate within bacteria or single-celled prokaryotic organisms known as archaea. They are composed of proteins that encapsulate a DNA or RNA genome. This genome ranges from a few to hundreds of genes.

Bacteriophages are, in fact, more plentiful than all the other organisms on Earth combined. They are found wherever bacteria are present. Although there are thousands of types of phages, it is important to remember that each may only infect one or a few types of bacteria. T-type phages were the first to be studied. Lambda, Mu and M13 (used by the MIT team), are some of the types used in recombinant DNA studies.

In 1980. American scientist George P. Smith developed phage display technology which fuses engineered DNA fragments into the genome (gene III) of phages. Since gene III is responsible for expressing a protein on the phage’s surface, the protein produced by the fused DNA fragment could also be expressed.

Researchers could then develop antibodies to recognize the foreign protein to identify those phages which had accepted the engineered DNA and thus focus their study.

Direct evolution extends the concept of Darwinian evolution to the laboratory. In continuous evolution, every step of a protein’s evolution cycle (screening, selection, etc.) is performed continuously, eliminating manual intervention. Most studies favor the replicative fitness of host phages under a continuous dilution.

In phage-assisted continuous evolution (PACE), the evolving gene is transferred from one host cell to the next, and phages displaying the engineered protein are identified through phage display technology. PACE technology significantly accelerates the directed evolution of biomolecular compounds.

Phage- and Robotics-Assisted Near-Continuous Evolution (PRANCE)

“Lagoon,” a fixed-volume vessel, constitutes the central component of PACE. The lagoon is populated with M13 phages, which have incorporated the gene of interest. E. coli bacterial cells facilitate the replication of phages.

The lagoon is continuously weakened by the adding and draining of liquid media that contains E. coli cells. The dilution rate is quicker than the E. coli reproduction rate but slower than the phage reproduction rate. Thus, phages are only capable of being retained by means of sufficiently fast replication.

The MIT team used a 96-well plate-based method, where 500-µl cultures of evolving M13 phages were successively diluted with E. coli bacteria two times an hour utilizing an automatic liquid handler.

To estimate continuous flow at the pipette, the team created a robotic interface controlled by a Python algorithm that timed the dispersal of bacteria, the calculation of chemical stimuli, the sampling of populations for real-time monitoring and historical sample preservation.

Cohesive real-time measurement of turbidity, luminescence and fluorescence enabled fitness tracking based on different activities. Therefore, a luminescent or fluorescent reporter could be coupled to the presence of phages or to the direct activity of the evolving biomolecule.

The team dubbed their approach phage- and robotics-assisted near-continuous evolution (PRANCE).

The system performs feedback-controlled evolution. Feedback control is triggered by luminescence, which is extensible to activity-dependent fluorescent reporters such as transcription, quorum sensing, solubility, protease activity and splicing. Measurements such as PCR, binding measurements and orthogonal in vitro assays can be integrated robotically.

PRANCE was able to sample all parameters known to affect evolution outcomes, including initial genotype, environment factors and chance events.

Such high throughput evolution improves researchers’ ability to sample multiple outcomes and may help resolve controversial questions in evolutionary biology, such as the friction between determinism and contingency.

References and Further Reading

DeBenedictis, E.A., et al., (2021) Systematic molecular evolution enables robust biomolecule discovery. Nature, [online]. Available at:

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William Alldred

Written by

William Alldred

William Alldred is a freelance B2B writer with a bachelor’s degree in Physics from Imperial College, London. William is a firm believer in the power of science and technology to transform society. He’s committed to distilling complex ideas into compelling narratives. Williams’s interests include Particle & Quantum Physics, Quantum Computing, Blockchain Computing, Digital Transformation and Fintech.


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