Protein Prediction I

Karteikarten und Zusammenfassungen für Protein Prediction I an der TU München

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Lerne jetzt mit Karteikarten und Zusammenfassungen für den Kurs Protein Prediction I an der TU München.

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

What is the difference between Hashing and Dynamic Programming?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Types of Protein Structure Comparison

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Through what is a secondary structure stabilized?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

What is a dark-proteome?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Statistical significance? What are statistically significant digits?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Define five different criteria to measure the performance of secondary structure prediction

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Give an example for a method that predicts protein disorder through machine learning without using any experimental data about disorder. This method uses no positive (disorder) “like” what it is supposed to predict and uses many negatives (not disorder) that incorrectly labelled. How can it still work?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Applying a method for secondary structure prediction fails for proteins with transmembrane helices (TMH). How could you adapt the known solution to the new problem?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

How can you encode the profile?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Protein adopt unique 3D structure to function. Explain how some proteins often referred to as proteins with disordered regions do not.

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Q5: Why is it impractical to dynamic programming align multiple proteins? What is an alternative?

Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

When to use global and when local alignment?

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Beispielhafte Karteikarten für Protein Prediction I an der TU München auf StudySmarter:

Protein Prediction I

What is the difference between Hashing and Dynamic Programming?
-> database today has 120 million records
-> Hashing doesn’t align entire matches but (randomly picked) stretches or words
(of size 3, but today it is recommended to use 5)
-> Hashing much faster
-> Hashing more feasible, doable
-> Hashing cannot do multiple sequence alignments (like all vs all)

Protein Prediction I

Types of Protein Structure Comparison
All-alpha, All-beta and AlphaBeta

Protein Prediction I

Through what is a secondary structure stabilized?
Hydrogen-bonds

Protein Prediction I

What is a dark-proteome?
No 3D-structure is known. Some part is disordered, transmembrane, but rest is unknown.

Protein Prediction I

Statistical significance? What are statistically significant digits?
statistical significance: Method A = 60%, Method B = 63% => deltaQ3 = 63-60 = 3
stderr = sigma/sqrt(# of proteins)

If the delta between the new method and existing method is larger than the stderr of the new method then it is statistically significant.

Protein Prediction I

Define five different criteria to measure the performance of secondary structure prediction
1. Do they use the same measure? (for example Q3)
2. Do they use the same test data?
3. Is it ensured that there is no overlap between test data and train data?
4. Is DeltaQ3 significant? (stderror: sigma /sqrt(#ofInstances)
5. Cross-training

Protein Prediction I

Give an example for a method that predicts protein disorder through machine learning without using any experimental data about disorder. This method uses no positive (disorder) “like” what it is supposed to predict and uses many negatives (not disorder) that incorrectly labelled. How can it still work?
See absence of order (constant signal?), absence of a clear signal.

Only signal is consistent. In dataset with more than 70 residues, there is something about loopy disorder, which are conservation, solvent accessibility etc.

Protein Prediction I

Applying a method for secondary structure prediction fails for proteins with transmembrane helices (TMH). How could you adapt the known solution to the new problem?
Retrain with new data (membrane proteins)
Problem: 2nd level now makes the helices much too long.
Fix: Just cut them? (More in the lecture…)

Protein Prediction I

How can you encode the profile?
By assigning probabilities to the occurrence of a particular amino acid at each position of a motif
or
Compile the degree given by Blosum62
Simply count how many have aa say ‘L’, then replace with the probability vector

Protein Prediction I

Protein adopt unique 3D structure to function. Explain how some proteins often referred to as proteins with disordered regions do not.
Disordered region:
Protein is not adopting a unique 3D structure
Being disordered could be seen as a feature as it allows more flexibility
Saying: Same sequence adopts same 3D structure, info how it will look is in the sequence
However here the information is disordered
some regions have to bind to partners
disordered region adopts to different regions/partners, dynamic, flexible

Protein Prediction I

Q5: Why is it impractical to dynamic programming align multiple proteins? What is an alternative?
Dynamic programming for multiple alignments is extremely expensive in terms of CPU time and RAM. Alternatives to using dynamic programming is hashing (BLAST and PSI-BLAST), genetic algorithms (T-Coffee) and HMM-s.

– Clustal Omega (ClustalW/ClustalX) does dynamic programming on multiple proteins
– Problem with dyn programming is CPU time
– can’t align >1000 proteins

Hidden Markov models (like HMMER) does alignments faster than Clustal Omega

Protein Prediction I

When to use global and when local alignment?
local always, global never (wrong answer) :

global:
align two proteins from one end to the other, first to last residue
search sequence is a whole protein/
domain = unit that folds on its own, unique protein structure

local:
find a match

o Global: all residues aligned: you are forced to compare two proteins from end to end.
o Local: best matches: you just cut out something that matches locally.

We can’t use global alignment to detect local regions of high similarity
We can’t use global alignment to align a fragment and a sequence

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