|
(page 2 of 2)
The goal now is to fill in a large matrix of potential drugs
and known and potential HIV protease mutants in order to see
which drugs are the most robust against which mutants.
The first step is to figure out which computations to run.
A vast number of possible permutations of mutant and inhibitor
combinations exist, so it would be impossible to run them
all. Olson and his colleagues focus on mutations to the 10
amino acids in the protease monomer that have direct contact
with the substrate or inhibitor in the binding site.
This still takes quite a bit of computing power.
So Olson and his colleagues try to rationally decide which
computations are most important to run first. These, accordingly,
are put higher up in the queue to be solved by FightAIDS@Home
computers.
Rik Belew, who is a professor in the Department of Cognitive
Science at the University of California, San Diego, and a
member of the research consortium, is an expert in machine
learning. Belew and his graduate student Chris Rosen developed
a computational optimization scheme called co-evolution to
find the best candidate mutant and inhibitor combinations
to study.
The scheme roughly looks for the best inhibitor for the
best mutant by using a simple docking of candidate molecules,
assigning a numeric score for how well the molecules fit together.
From that, ideally, comes information on which are the best
inhibitors.
"His goal is to take a look at the matrix and see which
results will give us the most information about the entire
system," says Olson. "We're not going to be able to do all
possible drugs against all mutants, so we try to use the most
informative mutants to test the [most promising] drugs."
These computations can then guide the experimental biologists
and chemists, who can make the mutant proteins, synthesize
the inhibitors, and test whether they bind as expected.
All this information is then fed to the chemists who can
use it to design inhibitors to test and to the biologists
who can create the mutant proteases and design the experiments
in which to test them. For instance, Elder can take whatever
mutants are deemed to be the most informative and express
them. Then, if what the computational group has predicted
is not true at all or if it is, the team will get his experimental
feedback
The computational group can also interact with Elder and
Torbett, who are co-principal investigators on one of the
four projects on the program project grant, and look at the
viral dynamics that take place in tissue culture. Elder and
Torbett have developed a panel of tissue culture assays with
various mutant forms of HIV—with wild type and drug-resistant
proteases.
"Before we try to predict viral dynamics in a patient, we
may try to predict it in tissue culture," says Olson.
The Path of Most Resistance
Torbett and Elder are interested in the molecular biological
consequences of mutations to the HIV protease as a result
of drug therapy, and their project follows from work that
they previously carried out with TSRI Professor Chi-Huey Wong
who is the Ernest W. Hahn Professor and Chair in Chemistry.
Torbett used the TL3 protease inhibitor that was developed
by Elder and Wong to develop protease-resistant mutants by
treating HIV-infected cells with the drug and forcing mutants
to arise. Torbett's lab isolated the protease genes after
each mutation arose, and developed a chronological library
of mutant forms of the HIV protease.
"We end up getting a mutation path of changes, from a little
resistant to very resistant," says Torbett. In the laboratory,
Torbett has generated a "supermutant" form of protease that
has changed a half dozen animo acids and is resistant to protease
inhibitors.
Along this path of most resistance, they put the various
mutant forms into expression systems and asked how these sequential
changes affect the biochemistry of the protein.
What they have observed is that the initial changes are,
not surprisingly, directed against the particular inhibitor
being used. This initial inhibitor selects for those mutants
that have arisen spontaneously and can resist it.
However, over time, the protease continues to mutate.
One of the important observations that Torbett and other
scientists have made is that the later mutants exhibit broad
resistance against a number of drugs. This robust cross-resistance
gives rise to the dangerous multiple-drug-resistant strains
of HIV that have been emerging since the advent of antiretroviral
therapy in the last decade.
"Why does the protease become cross-resistant when treated
with a single drug?" asks Torbett.
Substrates and Aptamers
Torbett and Elder are also asking how the mutations to the
viral protease affect the binding of the protease to substrate
and how these changes map to structural changes that occur
when HIV protease mutates.
Mutant forms of the protease are all different in terms
of how they bind to substrate, and Torbett and his laboratory
use a technique called phage display to see the range of structures
to which the HIV protease can bind as it accumulates mutations.
Phage display is a method of generating large libraries
of variant forms of a particular protein—in this case,
the HIV protease substrate. In the technique, a protein is
fused to a viral coat protein of the phage—a filamentous
virus that infects bacteria. Then the virus is allowed to
reproduce in culture, where it copiously makes new copies
of itself.
Potentially, more than a billion variants of substrate can
be generated in this way, and the substrate preferences of
HIV protease can be checked against this library.
Torbett also uses RNA aptamers—a type of structural
probe made out of RNA—to probe the outside structure
of the protease to see how it has changed in response to the
mutations. These aptamers are also generated in great numbers
as well.
All of this is aimed at giving the investigators some idea
of possible targets than will not mutate.
"If you can force the virus into a corner where it can replicate
but at low efficiency, it will not be able to grow very well,"
says Torbett. "If the immune system's intact, then it should
be able to control the virus.
Next week: Chemical Approaches
1 | 2 |
|
