September 22, 2005
Protein Construction May Be Governed by Simple Rules
Howard Hughes Medical Institute researchers have discovered that all
the necessary information to sculpt a protein into its proper shape and
function is contained in a relatively simple set of instructions
encoded in its amino acid sequence.
The proof comes in a pair of papers published in the September 22,
2005, issue of the journal Nature from researchers in the
laboratory of Rama Ranganathan, a Howard Hughes Medical Institute
investigator at University of Texas Southwestern Medical Center.
Ranganathan and his colleagues first used an algorithm to identify the
key pattern of amino acid interactions in a particular protein family.
Then, they used this pattern to build a dozen synthetic proteins from
scratch that reproduced the form and function of their natural
counterparts, at least in test tubes.
“The main point of these papers is that a rather simple set of sequence rules suffices to specify a protein family.”
Rama Ranganathan
Scientists have known that the sequence of amino acids in a protein
tells it how to fold and what to do. But the new findings may help
explain where the necessary information is in the sequence and may help
explain how a protein can simultaneously tolerate many mutations
without apparent harm, but be crippled by a single mutation at certain
sites. And the method suggests that, if biologists follow the same few
simple rules as evolution, creating complex proteins may not be quite
as complicated as it might seem.
"The studies indicate that the number of crucial interactions in a
protein may be smaller than previously thought—a boon for those
who want to design novel proteins from scratch to fulfill a specific
function," writes Jeffery Kelley of the Scripps Research Institute in
an accompanying commentary in Nature.
"The design of artificial sequences having the capacity to fold into
stable proteins with desired functions has been the holy grail of
protein engineering for many years," write Robert Smock and Lila
Gierasch of the University of Massachusetts, Amherst, in a perspective
in the September 23, 2005, issue of the journal Cell. "From a
protein engineering standpoint, the approach has great promise."
Ranganathan did not set out to build artificial proteins. He is
interested in learning how nature designs proteins naturally through
the evolutionary process of random variation and selection. But
rebuilding a protein was the best way to assess a remarkably simple
evolutionary hypothesis he discovered in genome databases.
"It will be really important to test whether these artificial
proteins can work in living cells, embedded within the signaling
networks that they were selected for through evolution," Ranganathan
said. "But the main point of these papers is that a rather simple set
of sequence rules suffices to specify a protein family."
Six years ago, Ranganathan and his colleagues reported a way to
extract the key amino acids by looking at the record of successful
products of evolution. Modern proteins are patchworks of modules,
swapped among proteins over time, that do similar jobs of binding
specific targets, catalyzing certain reactions, and sending
signals.
Ranganathan reasoned he could compare a family of modules from
several species to find which combinations of amino acids were
evolutionarily selected over many millennia after they branched on the
tree of life. To do so, he wrote a computer program that computes
correlations between amino acids among the similar modules that have
stood the test of time.
Surprisingly, only a few amino acids seemed to be tightly
constrained and linked over evolutionary time. The highly constrained
positions were biologically important; the protein was significantly
affected when those few amino acids were mutated. Other parts of the
protein were different in that they seemed to have evolved
independently from each other, and were relatively unaffected by
experimental mutations, he said.
Ranganathan and his colleagues found that sometimes, functionally
important parts of a folded protein that are far away from the apparent
center of action are detected by the evolution-based algorithm.
"A large body of work shows that proteins have a dense and local
pattern of structural interactions between amino acids," Ranganathan
said. "But in the evolutionary data, we see a sparse but distributed
architecture of amino acids." This new way of looking at the problem
could contribute to protein design efforts by providing the natural
architecture of amino acid interactions. Currently, most protein design
algorithms attempt to optimize all the atomic relationships in a
crystal structure of a protein, he said.
To test the global implications - that not all amino acids in a
protein have equal significance for its structure and function - the
Ranganathan group found the critical amino acid networks in a family of
proteins and used them to build working synthetic proteins from an
otherwise random sequence of amino acids. It promised to be a
labor-intensive, time-consuming project. At first, no one in his lab
wanted to tackle it.
To start, Ranganathan selected the WW domain family, a small module
used by proteins from many diverse species to bind to target sites on
other proteins and mediate protein-protein interactions. The WW domains
all fold into a simple shape that resembles a hand cupped to hold
water. In people, a mutation in the WW domain hinders the ability of a
protein called nedd4 to bind to an ion channel and put it where it is
needed in a kidney cell, which can lead to a serious disease called
Liddle's syndrome, he said.
In experiments described in the first article in Nature,
Michael Socolich, a senior technician, and Steve Lockless, a graduate
student, directed the analysis of 120 WW domains in organisms ranging
from yeast to humans, using all the sequences available in the genome
databases at the time. From this evolutionary analysis, they found the
global pattern of conserved and tightly coupled amino acids.
From there, they created four libraries containing the blueprints
for the WW domain, synthesized all the proteins in the libraries, and
tested which ones folded correctly.
The first library contained a random assortment of natural WW
domains to test for the innate folding rate of their experimental
method. About two-thirds of the proteins folded properly. The second
library contained totally random sequences from the WW alignments. None
of the resulting proteins folded, as expected.
In a third library, they instructed the computer to preserve all the
amino acids that had been conserved over time, but withheld the
information about which amino acids needed to be coupled. None of these
proteins folded properly. But in the fourth library, containing
computer-generated sequences that preserved the few key longstanding
amino acid relationships, about one third of the proteins folded.
The first paper proved that a few key amino acids can cooperate to
control the shape of a protein. "It begins to explain how a sequence
can diverge and retain the essence of a structure," Ranganathan said.
"But nature builds function, not structure."
In the second Nature paper, postdoctoral fellow William Russ
and colleagues expanded their analysis to 292 WW domains now in the
growing genome databases and tested the ability of the folded
artificial WW domains to bind to the same spectrum of peptides as
naturally occurring WW sequences.
When the team examined the co-evolving amino acid network in the WW
domain, it contained residues on the backside of a molecule linked to
the active binding site. Distant as it is from the active binding site,
the residue appears to participate in the peptide binding, mutation
experiments showed.
Together, the papers show that conservation plus coupling
information is necessary and sufficient to recreate members of a
specific protein family. At this stage, the researchers can not
distinguish the rules for structure from the ones for function, or if
they differ. Nor do they know the physical mechanisms underlying the
statistical sequence rules.
So what about all the amino acids excluded from the co-evolving
network? "I see them as the engine for forward evolution," he said.
"The independence of local interactions gives the protein its capacity
for novel variation. The protein conserves a small core network of key
interactions which by themselves guarantee its function. That liberates
the other positions to vary independently. In this way, proteins
created by random mutation become robust to random perturbation. It's
good to have tolerance for the process that created you."
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Jennifer Michalowski
