Classifying HIV Phenotypes

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Из курса от партнера University of California San Diego

Finding Mutations in DNA and Proteins (Bioinformatics VI)

27 оценки

University of California San Diego

Finding Mutations in DNA and Proteins (Bioinformatics VI)

27 оценки

Курс 6 из 7 — Specialization Bioinformatics

In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.

Из урока

Week 4: Introduction to Hidden Markov Models

<p>Welcome to week 4 of class!</p> <p>This week, we will start examining the case of aligning sequences with many mutations -- such as related genes from different HIV strains -- and see that our problem formulation for sequence alignment is not adequate for highly diverged sequences.</p> <p>To improve our algorithms, we will introduce a machine-learning paradigm called a hidden Markov model and see how dynamic programming helps us answer questions about these models.</p>

Classifying HIV Phenotypes 6:35

Gambling with Yakuza 12:51

From a Crooked Casino to a Hidden Markov Model 9:22

The Decoding Problem 5:38

The Viterbi Algorithm 7:19

Познакомьтесь с преподавателями

Pavel Pevzner
Professor
Department of Computer Science and Engineering
Phillip Compeau
Visiting Researcher
Department of Computer Science & Engineering

[MUSIC]

Hi. Today we will talk about the arms race

between the researchers trying to develop an HIV vaccine, and

the rapidly evolving human immunodeficiency virus.

And this discussion will bring us to the challenge of comparing highly diverse

viruses from different patients, or comparing highly diverse proteins

from species separated by millions of years of evolution.

We will learn that the traditional sickness alignment algorithm

that we studied before often fails to detect

subtle similarity between highly diverged sequences.

To address this limitation, we will develop a new computational framework

called Hidden Markov Models, whose applications in

bioinformatics will extend well beyond sequence comparison.

We will start from classifying HIV phenotypes, and

asking the question of what HIV phenotype Gaëtan Dugas had.

Gaëtan Dugas was a flight attendant who traveled the world and

engaged in sexual liaison with hundreds of men.

In 1984, he has become the most studied HIV patient in the world,

when biologists constructed a network of 40 HIV patients

whom he infected with his HIV virus.

He died in the same year and was named AIDS patient zero,

although, of course, he was not the first HIV patient.

In the same year, the U.S. Health Secretary,

Margaret Heckler, announced that an HIV vaccine is about to be developed.

13 years later, Bill Clinton said: "It's no longer a question of

whether we can develop an AIDS vaccine; it is simply a question of when."

Well, we still don't have an HIV vaccine today,

and to understand why we don't have it,

it's important to figure out how HIV evades the human immune systems.

Vaccines are often made from virus surface proteins to train

the human immune system to recognize vital envelope proteins and destroy them.

However, the strategy didn't work for HIV viruses because HIV evolves so

fast that there is actually a multitude of various HIV subtypes.

This slide shows ten sequences from HIV envelope glycoprotein gp120

that shows a large number of substitutions, insertions, and

deletions within a single patient.

HIV viruses in a single patient evolve with a very

fast rate of 2% per nucleotide per year.

HIV strains in different patients are so

diverged that they require different drug cocktails.

Today we will talk about one specific

HIV phenotype called syncytium-inducing phenotype.

HIV has the ability to trick human cells

to fuse together together to form one giant cell.

Why would the HIV virus do such a strange thing?

Because it's easier to kill all the cells once they are fused in a single cell.

The question we will be interested in today: "Given an HIV sequence,

can we figure out whether it has a syncytium-inducing phenotype or not?"

And in this slide, you see 20 sequences from protein gp120

actually, from a spectacularly conserved region of this protein called the V3 loop.

Despite the fact that it's conserved, the sequences have many mutations

and even have different lengths, so we would definitely need to align them.

We know that six of these sequences have syncytium-inducing phenotype,

but what sequence features

define this syncytium-inducing phenotype?

Biologists noticed that in HIV viruses with the syncytium-inducing phenotype,

the amino acids at positions 11 and 25

of the V3 region are Arginine and Leucine, and it has become

the first computational rule for classifying the syncytium-inducing phenotype.

It later turned out that the classifying rules are much more complex, but

we will not go into the details of the more complex rules.

What we will, however, notice is that when we construct this alignment

and build a profile and sequence logo from this alignment,

we will see the conservation of sequences is extremely

non-uniform across different positions.

For example, positions 11 and 25 are particularly poorly in this case.

And this brings one challenge and

one question: the challenge is to predict an HIV phenotype.

It is very important to align sequences correctly.

In our case, alignment of the V3 region is not difficult.

We can figure out that all V3 regions

belong to the same type of proteins.

However, aligning them correctly so that all amino acids

belonging to columns 11 and 25 appear in the same column may become a challenge.

And it brings the following question:

Was it a good idea to use the same scoring matrix

across different columns of an alignment,

something we have done in our previous studies of multiple alignment?

And a brief look at this sequence logo tells us that we should develop a new

statistically solid problem formulation for alignment

that will use a different scoring approach at different columns.

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