Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Predicting the evolution of genetic mutations

14.04.2020

Quantitative biologists David McCandlish and Juannan Zhou at Cold Spring Harbor Laboratory have developed an algorithm with predictive power, giving scientists the ability to see how specific genetic mutations can combine to make critical proteins change over the course of a species's evolution.

Described in Nature Communications, the algorithm called "minimum epistasis interpolation" results in a visualization of how a protein could evolve to either become highly effective or not effective at all.


The algorithm called "minimum epistasis interpolation" results in a visualization of how a protein could evolve to either become highly effective or not effective at all. They compared the functionality of thousands of versions of the protein, finding patterns in how mutations cause the protein to evolve from one functional form to another.

Credit: McCandlish lab/CSHL, 2020

They compared the functionality of thousands of versions of the protein, finding patterns in how mutations cause the protein to evolve from one functional form to another.

"Epistasis" describes any interaction between genetic mutations in which the effect of one gene is dependent upon the presence of another. In many cases, scientists assume that when reality does not align with their predictive models, these interactions between genes are at play.

With this in mind, McCandlish created this new algorithm with the assumption that every mutation matters. The term "Interpolation" describes the act of predicting the evolutionary path of mutations a species might undergo to achieve optimal protein function.

The researchers created the algorithm by testing the effects of specific mutations occurring in the genes that make streptococcal GB1 protein. They chose the GB1 protein because of its complex structure, which would generate enormous numbers of possible mutations that could be combined in an enormous number of possible ways.

"Because of this complexity, visualization of this data set became so important," says McCandlish. "We wanted to turn the numbers into a picture so that we can understand better what [the data] is telling us."

[Video - Visualizing the evolution of a protein: https://www.youtube.com/watch?v=0miHVrncrhY]

The visualization is like a topological map. Height and color correlate with the level of protein activity and distance between points on the map represents how long it takes for the mutations to evolve to that level of activity.

The GB1 protein begins in nature with a modest level of protein activity, but may evolve to a level of higher protein activity through a series of mutations that occur in several different places.

McCandlish likens the evolutionary path of the protein to hiking, where the protein is a hiker trying to get to the highest or best mountain peaks most efficiently. Genes evolve in the same manner: with a mutation seeking the path of least resistance and increased efficiency.

To get to the next best high peak in the mountain range, the hiker is more likely to travel along the ridgeline than hike all the way back down to the valley. Going along the ridgeline efficiently avoids another potentially tough ascent. In the visualization, the valley is the blue area, where combinations of mutations result in the lowest levels of protein activity.

The algorithm shows how optimal each possible mutant sequence is and how long it will take for one genetic sequence to mutate into any of many other possible sequences. The predictive power of the tool could prove particularly valuable in situations like the COVID-19 pandemic. Researchers need to know how a virus is evolving in order to know where and when to intercept it before it reaches its most dangerous form.

McCandlish explains that the algorithm can also help "understand the genetic routes that a virus might take as it evolves to evade the immune system or gain drug resistance. If we can understand the likely routes, then maybe we can design therapies that can prevent the evolution of resistance or immune evasion."

There are additional potential applications for such a predictive genetic algorithm, including drug development and agriculture.

"You know, at the very beginning of genetics... there was all this interesting speculation as to what these genetic spaces would look like if you could actually look at them," McCandlish added. "Now we're really doing it! That's really cool."

Media Contact

Sara Roncero-Menendez
roncero@cshl.edu
516-367-6866

 @CSHL

http://www.cshl.edu 

Sara Roncero-Menendez | EurekAlert!
Further information:
http://dx.doi.org/10.1038/s41467-020-15512-5

More articles from Life Sciences:

nachricht X-ray scattering shines light on protein folding
10.07.2020 | The Korea Advanced Institute of Science and Technology (KAIST)

nachricht Surprisingly many peculiar long introns found in brain genes
10.07.2020 | Moscow Institute of Physics and Technology

All articles from Life Sciences >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: The spin state story: Observation of the quantum spin liquid state in novel material

New insight into the spin behavior in an exotic state of matter puts us closer to next-generation spintronic devices

Aside from the deep understanding of the natural world that quantum physics theory offers, scientists worldwide are working tirelessly to bring forth a...

Im Focus: Excitation of robust materials

Kiel physics team observed extremely fast electronic changes in real time in a special material class

In physics, they are currently the subject of intensive research; in electronics, they could enable completely new functions. So-called topological materials...

Im Focus: Electrons in the fast lane

Solar cells based on perovskite compounds could soon make electricity generation from sunlight even more efficient and cheaper. The laboratory efficiency of these perovskite solar cells already exceeds that of the well-known silicon solar cells. An international team led by Stefan Weber from the Max Planck Institute for Polymer Research (MPI-P) in Mainz has found microscopic structures in perovskite crystals that can guide the charge transport in the solar cell. Clever alignment of these "electron highways" could make perovskite solar cells even more powerful.

Solar cells convert sunlight into electricity. During this process, the electrons of the material inside the cell absorb the energy of the light....

Im Focus: The lightest electromagnetic shielding material in the world

Empa researchers have succeeded in applying aerogels to microelectronics: Aerogels based on cellulose nanofibers can effectively shield electromagnetic radiation over a wide frequency range – and they are unrivalled in terms of weight.

Electric motors and electronic devices generate electromagnetic fields that sometimes have to be shielded in order not to affect neighboring electronic...

Im Focus: Gentle wall contact – the right scenario for a fusion power plant

Quasi-continuous power exhaust developed as a wall-friendly method on ASDEX Upgrade

A promising operating mode for the plasma of a future power plant has been developed at the ASDEX Upgrade fusion device at Max Planck Institute for Plasma...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Contact Tracing Apps against COVID-19: German National Academy Leopoldina hosts international virtual panel discussion

07.07.2020 | Event News

International conference QuApps shows status quo of quantum technology

02.07.2020 | Event News

Dresden Nexus Conference 2020: Same Time, Virtual Format, Registration Opened

19.05.2020 | Event News

 
Latest News

X-ray scattering shines light on protein folding

10.07.2020 | Life Sciences

Looking at linkers helps to join the dots

10.07.2020 | Materials Sciences

Surprisingly many peculiar long introns found in brain genes

10.07.2020 | Life Sciences

VideoLinks
Science & Research
Overview of more VideoLinks >>>