The Resistance Part II:
Fighting HIV Resistance At Home and in the Laboratory

By Jason Socrates Bardi

"All must work together or the Body will go to pieces."

—Aesop, The Belly and The Members, circa 600 B.C.

It was early summer, lunchtime at the Scripps Research Institute (TSRI), and the monthly meeting of scientists who are funded by a program project grant from the National Instuitutes of Health called Drug Design Cycle Targeting HIV-Protease Drug Resistance was about to begin.

There were over a dozen people in the room from all parts of the TSRI campus and beyond—organic chemists, molecular biologists, computer scientists, protein chemists, and cell biologists.

TSRI Associate Professor Bruce Torbett, one of the investigators in the room, delighted at being able to have biologists at the same table with modelers, crystallographers, and chemists. "It forces us to think differently," he says.

At 5 minutes after the hour, TSRI Professor John Elder rushed in and grabbed an open seat across from the other biologists. "I think I'll sit on the computational side today," he said.

The meeting lasts over an hour. One person describes the latest "phage display" experiments. Another discusses cloning a mutant only to find it to be a wild type. There were reports on data mining; shuttle and expression vectors; the chemistry and synthesis of small molecule HIV inhibitors; selecting D-amino acid peptides as inhibitors; and an upcoming conference in Washington, D.C.

Also during the meeting, Molecular Biology Professor Arthur Olson discusses the status of the FightAIDS@Home project. FightAIDS@Home is a distributed computer project that is integral to the research consortium that Olson leads.

"Scripps is a highly collaborative place," says Olson. "That's why I've stayed here for 20 years."

Computing for a Cause

Twenty years ago, AIDS was still a relatively unknown disease. TSRI was at that time called the Research Institute of the Scripps Clinic, and there was no Department of Chemistry. The institute had no computational research program at all, Olson adds. There were not even any computers, outside of the few in administrative offices and those that were hooked up to one scientific instrument or another.

Over the years, computers and chemists have both arrived at TSRI, and now Olson directs a program project grant that has brought these two groups together with biologists to take a multidisciplinary approach to addressing the problem of HIV protease drug resistance.

"Nobody knows how to do everything, so you really need these kinds of collaborations," says Olson.

Olson's FightAIDS@Home project is one of a growing number of "distributed computing" projects that seek to make use of the vast untapped computational resource that exists in the form of personal home computers. In fact, Olson's was the first such project involving biomedical research.

"The idea is that with a large enough computing source, you can look at all the mutations that may arise during the evolution of drug resistance," says Olson.

The procedure is simple. Any person with a computer and an internet connection can sign up by logging onto http://fightaidsathome.scripps.edu/ and downloading the program—the "client" in computer software parlance. Once the client is installed on the individual's computer, it is designed to conduct some set of calculations that may be one tiny part of a larger computation.

The computations basically take a known or candidate drug and simulate docking it into the HIV protease enzyme—or one of many mutant forms of this enzyme.

The client, designed by the company Entropia, Inc., runs so that the computations take place without disturbing normal computer use. The program runs when the machine is not in use, and runs until the computation is finished—usually after several dedicated hours of computing time.

The process is further made unobtrusive by what is known as pull scheduling. In this system, when the client finishes with one computation, the program waits until the user connects to the internet. Then the program wakes up, sends the results to the server at TSRI, and requests another job. The server then sends another computation.

"It's as simple as that," says Olson.

AutoDock and the Source Code

FightAIDS@Home was originally managed by Entropia, but it is now a nonprofit venture managed by TSRI. TSRI investigators are now in direct communication with the users.

In May, investigators at TSRI sent out an email to some 30,000 previous FightAIDS@Home users. Each email went to an individual who has expressed an interest in donating some of his/her computer time for the cause, and invited him/her to upload the new client application and continue under TSRI's management.

At a meeting a few days later, a research associate describes how in the first week of the sign-up they had received a flood of emails from countries in Europe, Japan, and even Turkey. In the first 48 hours of TSRI-managed operations, they had received 1,000 emails. And by the end of May, there were about 2,500 people who had uploaded the new client.

"We're thrilled about this," says Olson

The real advantage of local management of the project, says Olson, is that the investigators have access to the source code. This means that Olson and the other investigators can build improvements directly into their docking software.

The docking software they use is called AutoDock, and it was designed by Olson's group in the 1980s. This software basically takes a computer representations of a protein like the HIV protease and assays how well it binds to a computer representation of a flexible molecule like any protease inhibitor.

Originally the TSRI investigators had to supply Entropia with the source code for AutoDock, which locked them into using the one version of AutoDock they had when the project started.

They can now use different variations of the AutoDock code for different computations, and they can also upgrade the FightAIDS@Home client as the AutoDock software improves.

In fact, improvements to the AutoDock software have been made nearly every year since the program first came out in 1989. Version 4 of the software—the next major release—is currently waiting in the wings for beta-testing.

"It has a lot of enhanced functionality," says Olson.

All Mutants Great and Small

Now that the project has moved to TSRI, Olson and his colleagues have done a lot of thinking about how best to schedule and run calculations. This has resulted in redundant jobs to multiple clients and statistical analysis to make sure that the data returned is not corrupted—if someone were to unplug a computer during the middle of a calculation, for instance.

"We don't have to worry about losing a job or two," says Olson.

They have also done a lot of thinking about the sort of jobs they want to send to people who are donating their computer time.

Originally, the questions were rather simple. Investigators took the known structures of the wild type and mutant proteases and docked clinically approved drugs to them to see if they could detect resistance.

"In fact, we could detect resistance," says Olson.

Now they are asking somewhat more complicated questions, such as whether they can predict the resistance in a mutant protease without a known structure. Not all the structures of the mutants that arise clinically in patients who take HIV drugs have been solved.

Investigators can expand the types of calculations that are used, changing the way in which the computer models the proteins and the inhibitors. They can increase the grid size—something akin to resolution—and make more accurate computations. They can make parts of the modeled molecules more or less flexible as they see fit as flexibility directly affects the complexity of a calculation.

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

 

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A number of Scripps Research scientists work together under an NIH program project grant, Drug Design Cycle Targeting HIV-Protease Drug Resistance. Photo by Kevin Fung.