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Ärende: Directed evolution of pro
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October 6, 2003
Volume 81, Number 40
CENEAR 81 40 pp. 35-36, 38-40
ISSN 0009-2347
FROM THE ACS MEETING
BUILDING PROTEINS COMPUTATIONALLY

Computation fuels protein design, as theory and experiment combine to
usher in new era of combinatorial methods and directed evolution
ELIZABETH WILSON, C&EN WEST COAST NEWS BUREAU

POSSIBILITIES Xencor's PDA technology screens myriad protein sequences
for their potential to do a new job.

XENCORSince the dawn of biotechnology, scientists have sought to harness
the awesomely specialized catalytic power of proteins for their own
uses. Despite their numerous successes in engineering proteins for
applications in medicine and industry, researchers are aware of many
additional opportunities for harnessing catalytic proteins. 

Designing proteins, though, remains a daunting task, given that we still
have much to understand about how these molecules work their magic.
Lying just out of reach are a wealth of new drugs and catalysts--and
insight into nature itself.

For years, scientists have painstakingly strung together combinations of
amino acids in the hope that the resulting molecule would fold correctly
and perform its intended task. They also have employed the strategy of
mutating existing proteins by changing only a few specific amino acids
in the active site.

In recent years, directed evolution has emerged as an important approach
to protein design. A field rooted in high-throughput technology,
directed evolution involves randomly mutating proteins to create
enormous arrays of different structures, then screening and selecting
the mutants that best perform a desired "unnatural" task.

BUT TO SEARCH all possible combinations of 20 amino acids in a typical
350-amino-acid protein results in libraries that contain more proteins
than there are atoms in the universe--and most of them won't work. If
scientists could know ahead of time which amino acids in what positions
might be likely to create a successful protein, they'd have a huge
advantage heading into the lab.

Computers, then, are an obvious partner in this endeavor. With
increasingly powerful modeling techniques, faster speeds, and greater
disk space, computation has become an essential element in the search
for new proteins. Previously unmanageable libraries can be winnowed down
to a few promising possibilities. And predictions of the most promising
sites in proteins to alter are allowing scientists to explore new
territory in the protein landscape.

This budding and essential relationship between computation and
experiment was a focus of a symposium, attended by theorists and
experimentalists alike, at the American Chemical Society national
meeting in New York City last month.

SavenBoderPHOTOS BY ELIZABETH WILSON

Computational methods are going to be integral "particularly in the
design of sequences and in more combinatorial-type experiments," said
University of Pennsylvania assistant chemistry professor Jeffery G.
Saven. "There's lots of nice dovetailing between theory and experiment."
Sponsored by the Division of Physical Chemistry, the symposium was
organized by Saven and assistant professor of chemical and biomedical
engineering Eric T. Boder at the University of Pennsylvania.

A number of talks at the symposium focused on computational strategies
for getting the most bang for the buck from protein libraries. For
example, a popular technique for generating protein mutant libraries,
known as DNA shuffling, involves chopping up DNA sequences and
reassembling them randomly. Graduate student Narendra Maheshri described
his work with University of California, Berkeley, assistant chemical
engineering professor David V. Shaffer on a computational model of DNA
shuffling. Dubbed SHUFFIT, the model is intended to optimize shuffling
reactions and minimize the formation of "junk" DNA sequences.

Costas D. Maranas, associate chemical engineering professor at
Pennsylvania State University, uses various computational methods,
including mean-field theory calculations, to identify "clashes" between
protein fragments--unfavorable structures that can be easily eliminated
from a protein library.
THEORY CAN ALSO be used to zoom in on the crux of a protein's behavior
with unprecedented precision. Virginia W. Cornish, assistant chemistry
professor at Columbia University, and her colleagues are applying this
strategy to help understand the evolution of the bacterial enzyme
responsible for penicillin resistance.

The Achilles' heel of penicillin-sensitive bacteria is the penicillin
binding protein (PBP), an enzyme that's essential for building cell
walls but which is inactivated when it encounters the antibiotic.

Penicillin-resistant bacteria, however, carry an additional enzyme,
-lactamase, which instead hydrolyzes the antibiotic, rendering it
powerless. -Lactamase likely evolved from an ancient PBP, but what
exactly happened to the enzyme has remained a mystery. The proteins are
remarkably alike. They have similar three-dimensional structures and
conserved active-site residues. Yet their penicillin-hydrolyzing rate
constants differ by about six orders of magnitude.

"Our hope is to begin to understand what's responsible for the
difference in chemical reactivity," Cornish said.

EVOLUTION A penicillin-binding protein, showing residues where mutations
occurred (pink).
Courtesy Of Shalom Goldberg 

TO THAT END, Cornish and graduate student Shalom Goldberg are trying to
"evolve" a PBP into a -lactamase. They've collaborated with Columbia
chemistry professor Richard A. Friesner and his graduate student
Benjamin F. Gherman in a computational study of the proteins. They
combined quantum mechanics and classical molecular mechanics, treating
the bulk of the protein as a classical blob of noncovalent interactions,
while saving the more detailed and intensive quantum mechanical
calculations for the few amino acids in the protein's active site.

The researchers modeled both the ground and transition state of the
hydrolysis reaction for both PBP and the -lactamase. Their calculations
pointed to a single tyrosine residue, which is stabilized by a
hydrogen-bonding network in the b-lactamase, allowing it to act as a
general base catalyst. The residue isn't stabilized in PBP, however.
PBP's active site looks like that of the -lactamase, "just slightly more
crowded," Cornish said.

Now the team is beginning to use this information to guide mutagenesis
experiments. In directed evolution experiments, they've been able to
increase the activity of PBP by an order of magnitude.

"I think this is a trend in the field--to marry strengths in computation
with the strengths of directed evolution to solve problems we haven't
been able to solve yet," Cornish said.
Many computational methods exist for optimizing protein structures,
Saven noted at the meeting. However, many of these strategies involve
huge numbers of degrees of freedom and are therefore computationally
intensive. 

Rather than perform calculations that solve for specific amino acids,
his group has developed a less unwieldy method to obtain the
probabilities of amino acids, which they call a "statistical
computationally assisted design strategy" (scads). This algorithm
calculates the likelihood that certain amino acids will behave well at
different positions in a protein based on their interactions with the
protein's backbone, other side chains, and the environment.

They've used this method to help design a 114-residue, monomeric,
helical di-iron protein. The four-helix bundle with two iron or
manganese atoms in the center is a common structural motif in many
proteins and has important biological functions, such as oxygen binding
and transport.

REDESIGNED A water-soluble version of the potassium-channel protein KcsA
contains 29 computationally designed exterior amino acids for each
subunit.
COURTESY OF AVRAM SLOVICRECENTLY, 

University of Pennsylvania biophysics professor William F. DeGrado
designed simple oligomeric versions of these bundles. Saven, DeGrado,
and their colleagues, including graduate student Jennifer Calhoun, then
took that motif even further, using computational methods to design a
more "natural" protein facsimile. They first designed a sequence
backbone and added a couple of dozen residues important for function and
metal binding. They then used scads to solve for the remaining 88 amino
acids.

"The idea was to develop an analog that was more akin to what we see in
nature--and to make something more soluble and easily expressed," Saven
said. "Now we have something robust that should tolerate lots of
mutations."

The University of Pennsylvania groups and their colleagues, including
Hidetoshi Kono at the Japan Atomic Energy Research Institute, Kyoto,
also used scads to transform KcsA, a membrane-bound bacterial
potassium-channel protein, into one that's water soluble. Their strategy
was to make the protein's lipid-contacting side chains more polar while
maintaining its structure and function. Though membrane proteins
comprise a large fraction of drug targets, they are notoriously
difficult to study experimentally. The group's computational methods may
therefore make it possible to study these proteins' structure and
biophysical properties in unprecedented detail.
Computational direction is a foundation of protein engineering methods
developed by Monrovia, Calif.-based Xencor. John R. Desjarlais, Xencor's
director of computational biology, explained at the meeting that their
Protein Design Automation (PDA) technology couples computer-aided design
with experimental high-throughput methods. With a specific job in mind
for an engineered protein, they identify sites in a natural protein
likely to be involved in the action they're seeking. They
computationally scan different combinations of amino acids at those
positions and select those sequences predicted to produce proteins with
the structure, stability, and function they want. They then create these
proteins in the lab using combinatorial mutagenesis methods.

In one example that Desjarlais presented at the meeting, Xencor created
a variant of thioredoxin reductase, an enzyme important in cellular
metabolism. It requires the biological cofactor NADPH to perform. A
similar cofactor, NADH, is less expensive and more stable, so the group
altered the enzyme to use NADH in order to make a food-processing system
more cost efficient, Desjarlais said. "It worked very well," he said.
"We've been able to discover numerous novel protein sequences with a
diverse range of cofactor specificities."

INTERACTION Dark blue squares show strong interactions between bZIP
proteins in this 2-D array.
COURTESY OF AMY KEATING

APPROACHING THE PROBLEM from the other direction, some researchers are
using high-throughput assays to help direct computational studies. Amy
E. Keating, assistant chemistry professor at Massachusetts Institute of
Technology, studies the interactions of common proteins used in DNA
transcription, known as bZIP transcription factors. These proteins have
characteristic regions of coiled coils, which bind together. Scientists
want to understand how the amino acid sequences in these coiled coil
regions determine what combinations of bZIP proteins will dimerize.
Keating's group created a two-dimensional array of all possible
combinations of about 50 coiled coil regions from different bZIP
proteins. They used fluorescent markers to determine how strongly
different combinations dimerized. Strikingly, only a few combinations
bound well [Science, 300, 2097 (2003)].

With this information, Keating and Princeton University assistant
computer science professor Mona Singh are now testing a machine-learning
method for predicting interactions for these proteins. Keating said
they've used the experimental bZIP data to train the prediction method,
improving its abilities considerably. 

They also plan to use the experimental data to improve atomic-level
models for coiled coil interactions. "These models will, in turn,
improve our ability to do prediction and will also be useful for protein
design calculations," Keating said.

It's not yet entirely clear what characteristics make for good binding,
she noted. Factors such as electrostatic charge complementarity, pairing
of buried asparagine residues, and good hydrophobic packing at the
helix-helix interface are known to be important. But Keating and Singh's
computations suggest they're not the whole story. Rather, when the team
considered interfacial residue-residue interactions (which have not
traditionally been considered important), they were found to improve the
performance of machine-learning algorithms.

This new close mingling of experiments and calculations, Keating added,
is a promising approach for making progress in understanding--and
ultimately predicting and rationally modifying--the factors that
determine the specificity of protein interactions. 
INCOMPLETE Interactions such as core packing(left), charge
complementarity at repeating residue positions 'e' and 'g' (center) and
core polar residues are important in dimerization, but other factors may
be involved.
COURTESY OF AMY KEATING 

Chemical & Engineering News
Copyright © 2003 American Chemical Society
 

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