Searching for Post-Translational Modifications

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From the course by University of California San Diego

Molecular Evolution (Bioinformatics IV)

34 оценки

University of California San Diego

Molecular Evolution (Bioinformatics IV)

34 оценки

Course 4 of 7 in the Specialization Bioinformatics

In the previous course in the Specialization, we learned how to compare genes, proteins, and genomes. One way we can use these methods is in order to construct a "Tree of Life" showing how a large collection of related organisms have evolved over time. In the first half of the course, we will discuss approaches for evolutionary tree construction that have been the subject of some of the most cited scientific papers of all time, and show how they can resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. In the second half of the course, we will shift gears and examine the old claim that birds evolved from dinosaurs. How can we prove this? In particular, we will examine a result that claimed that peptides harvested from a T. rex fossil closely matched peptides found in chickens. In particular, we will use methods from computational proteomics to ask how we could assess whether this result is valid or due to some form of contamination. Finally, you will learn how to apply popular bioinformatics software tools to reconstruct an evolutionary tree of ebolaviruses and identify the source of the recent Ebola epidemic that caused global headlines.

From the lesson

Week 5: Resolving the T. rex Peptides Mystery?

<p>Welcome to week 5 of class!</p> <p>Last week, we asked whether it is possible for dinosaur peptides to survive locked inside of a fossil for 65 million years. This week, we will see what this question has to do with statistics; in the process, we will see how a monkey typing out symbols on a typewriter can be used to address it.</p>

Peptide Identification 4:50

Spectral Dictionaries 6:35

The Ostrich Hemoglobin Riddle 15:20

Searching for Post-Translational Modifications 5:16

Spectral Alignment Algorithm 11:29

Meet the Instructors

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

It turns out that most prolines in the T-Rex peptides identified by Asara

were actually modified version of prolines called hydroxyprolines.

And in this segment, we will talk about searches for

post-translationally modified peptides.

The central dogma doesn't mention it, but

many proteins are subject to a large number of post-translational

modifications that are important for cell-signaling and metabolic regulation.

In fact, most proteins are post-translationally modified and

over 600 types of modifications of amino acids are known today.

Some of them are extremely important, such as phosphorylation, shown on this slide.

How does a modification affect a peptide vector?

If you think about this, modification is simply changing the mass of the amino acid.

If we limit ourselves to computational

analysis of modification and a modification of mass "delta".

applied to an amino acid results in adding data to its mass.

For phosphorylation, for example, delta = 80.

For hydroxyproline in collagens, it is equal to 16, and

for a modification of lysine into allysin, it is equal to -1.

Therefore, modifications essentially increase the number of letters

in the amino acid alphabet and complicate peptide identifications.

In the beginning of this lecture, we looked at the ideal spectrum of this peptide.

Let's now look at how this ideal spectrum changes

if we introduce a modification on this peptide.

Here's this modification on C+16, and this modification affects

two prefix peptides and, in fact, three suffix peptides.

So, the ideal spectrum has changed in this case.

But please note that we don't know in advance what is the mass of modifications,

and we don't know on which amino acid this modification is residing.

Let's also take a look at how a modification affects a peptide vector.

In this particular example, a modification of mass 3 on the i-th

amino acids essentially amounts to inserting a block of 3

consecutive 0s before the i-th occurrence of 1 in the peptide vector.

This is for the modification of positive mass.

For a modification of negative mass, -2 in this case,

the modification corresponds to deleting the block of 2 0s

before the i-th occurrence of 1 in the peptide.

And after we figure out how modifications affect the peptide vector,

we are ready to formulate the spectral alignment problem.

Before we do this, let's define the notion of Variants_k(Peptide)

as the set of all modified variants of Peptide with up to k modifications.

We want to solve the spectral alignment problem, which is: Given a peptide and

a spectral vector, find a modified variant of this peptide

that maximizes the peptide-spectrum score among all

variants of the peptide with up to k modifications.

Of course, there is a very simple solution of this problem: We simply check all

possible peptides in the set Variants_k(Peptide)

and see which of them has the maximum score.

But this will be too slow because our more ambitious goal

is to solve the following modification search problem:

Given a Spectrum and a Proteome,

find a peptide of a maximum score against this Spectrum among

all modified peptides in the Proteome with up to k modifications.

Obviously, to efficiently solve the modification search problem, we need to

come up with a very fast algorithm for solving the spectrum alignment problem.

And, if we figure out how to search for modified peptides,

we will immediately be able to search for mutated peptides as well

because the mutation search problem results from simply substituting

the word modifications for mutations, like shown in this slide.

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