Lecture 11
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Update Warning! This page has not been revised yet for the 2007 Fall term. Some of the slides may be reused, but please consider the page as a whole out of date as long as this warning appears here.
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Protein Structure Prediction
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Lecture Slides
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Lecture 11, Slide 001
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Lecture 11, Slide 002
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Lecture 11, Slide 003
The observation became famously known as "Levinthal's Paradox", that neither random search nor the postulation of folding pathways can explain how a discrete structure can self-organize, given in a combinatorially large search space. But real proteins do just fine, thank you.
The observation became famously known as "Levinthal's Paradox", that neither random search nor the postulation of folding pathways can explain how a discrete structure can self-organize, given in a combinatorially large search space. But real proteins do just fine, thank you.
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Lecture 11, Slide 004
A toy observation on an optimization problem of similar magnitude as the random folding of a protein...
A toy observation on an optimization problem of similar magnitude as the random folding of a protein...
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Lecture 11, Slide 005
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Lecture 11, Slide 006
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Lecture 11, Slide 007
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Lecture 11, Slide 008
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Lecture 11, Slide 009
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Lecture 11, Slide 010
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Lecture 11, Slide 011
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Lecture 11, Slide 018
Non-polynomial time-complexity problems are considered intractable, since even as the problem size 'n' grows only modestly, the time requirements grow beyond all bounds and reasonable resources. A 1,000 element problem of O(2n) complexity takes the age of the universe to compute.
Non-polynomial time-complexity problems are considered intractable, since even as the problem size 'n' grows only modestly, the time requirements grow beyond all bounds and reasonable resources. A 1,000 element problem of O(2n) complexity takes the age of the universe to compute.
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Lecture 11, Slide 019
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Lecture 11, Slide 020
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Lecture 11, Slide 021
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Lecture 11, Slide 022
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Lecture 11, Slide 023
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Lecture 11, Slide 024
Simulated annealing allows a system to be computationally moved out of situations where it is trapped in local minima, and to proceed towards a global minimum on a rough search landscape.
Simulated annealing allows a system to be computationally moved out of situations where it is trapped in local minima, and to proceed towards a global minimum on a rough search landscape.
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Lecture 11, Slide 025
A simulated annealing strategy (in pseudocode).
A simulated annealing strategy (in pseudocode).
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Lecture 11, Slide 026
Many believe that genetic algorithms - albeit interesting - rarely outperform simulated annealing.
Many believe that genetic algorithms - albeit interesting - rarely outperform simulated annealing.
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Lecture 11, Slide 027
Natural proteins of course have evolved under the constraint of foldability. They may have avoided mutations that would expose them to the requirements of full, combinatorial optimization of their 3-D structure.
Natural proteins of course have evolved under the constraint of foldability. They may have avoided mutations that would expose them to the requirements of full, combinatorial optimization of their 3-D structure.
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Lecture 11, Slide 038
