Creating Artificial Proteins

Spy der Mann writes "By examining how proteins have evolved, UT Southwestern Medical Center researchers have been able to design genes to create artificial proteins. The researchers have discovered a set of simple "rules" that nature appears to use to design proteins. By feeding these rules into a computer program, they were able to obtain a sequence of artificial genes. These genes were then inserted into laboratory bacteria, producing the artificial proteins as expected."

Comments

Re:I hate to turn this into a flamewar so soon, bu

[haluness]

And the answer to this could be that a lot of rules have been randomly tried out. It turns out that the rule(s) we are seeing/discovering are the ones that lasted - and if they are simple they are probably efficient in some way.

The creationist/ID policy is to avoid facing unknowns by passing the buck onto a designer. In the current example, just because something appears elegant and simple to some person, it does'nt mean that it could not have naturally occured.

Our jobs, as scientists, or in the more general case, as people with a scientific temperement, is to uncover how or why this simple and elegant thing is the way it is - not to say, 'It's too tough, lets pass the buck onto the designer'!

if anyone is interested in the algorithms used

[afodor]

We published a series of papers evaluating correlated mutation algorithms, including SCA, which is the algorithm used in this pair of Nature papers. I haven't had a chance to look closely at the two new papers, but we found that SCA performed rather poorly when compared to other algorithms that calculate covariance from a multiple sequence alignment. SCA has a troubling tendency to assign high scores to pairs of columns of a multiple sequence alignment that have random sequence in them.

PDFs of our papers, and Java code implementing 4 different correlated mutation algorithms including SCA, are at my web site:

http://www.afodor.net

The references are:

Anthony A. Fodor, Richard W. Aldrich. On Evolutionary Conservation of Thermodynamic Coupling in Proteins. JBC 279(18):19046-19050, 2004

John P. Dekker, Anthony Fodor, Richard Aldrich and Gary Yellen. A pertubation-based method for calculating explicit likelihood of evolutionary co-variance in multiple sequence alignments. Bioinformatics 20:1565-1572, 2004

Anthony A. Fodor and Richard W. Aldrich. Influence of Conservation on Calculations of Amino Acid Covariance in Multiple Sequence Alignments. Proteins 56(2): 211-221, 2004

The last paper contains a comparison between SCA and three other correlated mutation algorithms.

As I said, I haven't had a chance to look carefully or critically at the new papers. (It takes me a LONG time to read a paper critically :-> This Slashdot thread will be likely long archived before I finish thinking about these papers!). But this particular algorithm aside, people who are interested in bioinformatics and contact prediction may find the math behind the correlated mutation algorithms interesting.

Anthony

Email: anthony.fodor(remove this and put in an at symbol)gmail.com
http://www.afodor.net/

Re:I hate to turn this into a flamewar so soon, bu

[shawb]

Why are there symbiant relationships? It allows for division of labor, essentially. The genetic load of one organism after symbiosis does not have to take care of these certain task that the other is taking care of. Most of the cells contained in your body are not actually yours. The majority of cells in the body are bacteria living in your intestine which each produce proteins which help with digestion. If our DNA had to encode for every one of those digestive and metabolic proteins that are actually used in digestion, we would be selected against compared to an organism that could make more efficient use of its DNA.

Diversity also leads to a sort of long term stability. If there are different ways to obtain resources, the ecosystem as a whole can adapt to environmental changes far more gracefully.