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Protein Structure Prediction. Mason Bially. Types of Structure. Primary Structure The linear amino acid sequence. Secondary Structure The local three-dimensional structure. Defined by hydrogen bonding patterns. Tertiary Structure The global three-dimensional structure.

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types of structure
Types of Structure
  • Primary Structure
    • The linear amino acid sequence.
  • Secondary Structure
    • The local three-dimensional structure.
    • Defined by hydrogen bonding patterns.
  • Tertiary Structure
    • The global three-dimensional structure.
    • Defined in atomic coordinates.
    • The actual function.
  • Quaternary Structure
    • The arrangement of multiple proteins.
how do we find secondary structure
How do we find Secondary Structure?
  • Couple Algorithms:
    • DSSP (Original, Slight Errors)
    • STRIDE (Newer, Sliding Window)
  • Requires the primary and tertiary structure.
    • Because of this they are exact, not guesswork.
  • Finds hydrogen bonds.
    • Uses potential energy functions.
      • Based on amino acid locations and orientations.
      • STRIDE’s is slightly more accurate
    • Returns one of 8 types of secondary structure for each amino acid.
      • 3 helix types
      • 2 beta-sheet types
      • 2 turn types
      • and ‘other’
x ray crystallography
X-Ray Crystallography
  • Shoot X-rays through a crystal and depending on how the X-rays come back, angle and intensity, the structure can be determined.
  • Some proteins are challenging to crystallize (intrinsic membrane proteins).
  • Can handle arbitrarily large sizes.
nmr protein spectroscopy
NMR Protein Spectroscopy
  • Uses Nuclear Magnetic Resonance a phenomena by which atomic nuclei in a magnetic field respond to electromagnetic radiation by reemitting it.
  • Has difficulty with large proteins.
  • Works on almost anything. (Including proteins with unstable tertiary structure)
why do we need structure prediction
Why do we need Structure Prediction?
  • Experimentally Finding tertiary structure has problems.
    • Slow, difficult, hard.
    • Some proteins can’t be found experimentally.
  • We need to cover more ground, quicker.
    • Drug design.
    • Bioinformatics tool development.
    • More detailed Interactome information.
but isn t it computationally hard
But isn’t it computationally hard?
  • Yes.
  • Secondary structure prediction.
    • Machine learning methods.
  • Tertiary structure prediction.
    • Homology Modeling
    • Fold Recognition (AKA Protein Threading )
    • From scratch (AKA de novo, AKA ab initio)
basis for prediction comparative modeling
Basis for Prediction(Comparative Modeling)
  • Protein structure (Secondary and Tertiary) is evolutionarily more conserved than the DNA or amino acid sequence.
    • Structure is function; changing it would prevent the protein from doing it’s job.
  • Therefore proteins will probably share structure with each other.
secondary structure prediction
Secondary Structure prediction
  • Early attempts. (~60%)
    • Chou-Fasman
      • Uses the probability of a secondary structure containing an amino acid.
    • GOR
      • Bayesian inference applied to the same basic idea.
  • Machine learning methods. (~70%)
    • Neural networks.
    • Support vector machines.
    • Hidden Markov models.
  • Future.
    • Secondary structure is also based on the environment the protein is folded in.
    • Including this metadata to attempt to improve methods.
homology modeling1
Homology Modeling
  • Requires primary structure and a template tertiary structure.
    • Relies on the idea that if one protein has a specific structure, so do other proteins.
  • Only works with relatively similar sequences.
    • Sequence identity above 50% is high quality.
      • Low quality x-ray crystallography.
    • Sequence identity above 30% is medium quality.
      • Anything lower degrades rapidly.
    • Limited by availability of suitable templates.
    • Limited by the ability to accurately align and choose distant templates.
  • Sometimes function/structure will diverge for seemingly similar targets and templates.
    • Happily generates models against incorrect templates.
homology modeling2
Homology Modeling
  • Template selection and Sequence alignment
    • Crucial, but relatively simple if a similar sequence exists (BLAST).
    • For edge cases:
      • PSI-Blast, HMM or profile-profile alignment based.
  • Model Generation
    • Multiple methods.
    • Construct the model by placing the amino acids where the aligned template suggests.
    • Then refine by going back to the chemistry/physics and fixing errors.
  • Model Assessment
    • Make sure the resulting fold is correct.
    • Detects errors in alignments and template selection.
    • Sometimes chooses the best of many potential models.
fold recognition aka protein threading
Fold Recognition(AKA Protein Threading)
  • Requires primary structure and a library of tertiary structures.
    • Relies on the idea that there are (relatively) few folds (tertiary structure) of proteins.
  • Often feeds final structure back to Homology Modeling techniques as template to get final model.
  • Can use a number of different scoring algorithms.
    • Most popular is free energy.
  • Attempts to find which templates in the library minimize the scoring algorithm
    • Threading
    • Dynamic Programming. (Optimization technique)
    • Machine Learning.
  • Often finds a large number of results.
how do we know these models work
How do we know these models work?
  • CASP (Critical Assessment of Techniques for Protein Structure Prediction)
    • Every two years.
    • Tests blind prediction algorithms.
      • In many different categories.
    • Since 1994.
  • Other variations.
future
Future
  • Mix it all together!
  • Including evolutionary information.
    • Improves alignment.
    • Helps find better folds.
  • Structural information.
    • Predicted secondary structure can help.
  • Mixing with ab initio/de novo methods.
questions
Questions?
  • COMPUTATIONAL STRUCTURAL BIOLOGY Methods and Applications
    • By Torsten Schwede and Manuel C Peitsch
  • Images from Wikipedia or sources.
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