Structural%20Bioinformatics%20Seminar

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Structural Bioinformatics Seminar

• Dina Schneidman
• Email duhovka_at_post.tau.ac.il

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Outline

• Seminar requirements
• Biological Introduction
• How to prepare seminar lecture?

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Seminar Requirements

• No prior knowledge in Biology is assumed or
  required!
• Attend ALL lectures
• Prepare one of the lectures

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Seminar Goals

• Learn how to study new subject from articles
• Learn how to present work in Computer Science

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Biological Introduction
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Schedule

• Introduction to molecular structure.
• Introduction to pattern matching.
• Introduction to protein structure alignment
  (comparison).
• Protein docking.

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Small Ligands

• Small organic molecules, composed of tens of
  atoms.
• Highly flexible can have many torsional degrees
  of freedom.

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DNA The code of life

• DNA is a polymer.
• The monomer units of DNA are nucleotides A, T,
  C, G.
• DNA is a normally double stranded macromolecule.

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RNA

• RNA is a polymer too.
• The monomer units of RNA are nucleotides A, U
  (instead of T), C, G.
• DNA serves as the template for the synthesis of
  RNA.

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Protein

• Protein is a polymer too.
• The monomer units of Protein are 20 amino acids.
  
• Each amino acid is encoded by 3 RNA nucleotides.

Hemoglobin sequence VHLTPEEKSAVTALWGKVNVDEVGGEALG
RLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGA
FSDGLAHLDNLKGTFATLSELHXDKLHVDPENFRLLGNVLVCVLAHHFGK
EFTPPVQAAYQKVVAGVANA LAHKYH
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The Central Dogma
DNA RNA
Protein
Symptomes
(Phenotype)
Cells express different subset of the genes in
different tissues and under different conditions.
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The central dogma

• DNA ---gt mRNA ---gt Protein
• A,C,G,T A,C,G,U A,D,..Y
• Guanine-Cytosine T-gtU
• Thymine-Adenine
  
• 4 letter alphabets 20 letter
  alphabet
• Sequence of nucleic acids Sequence of amino acids

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Bioinformatics - Computational Genomics

• DNA mapping.
• Protein or DNA sequence comparisons.
• Exploration of huge textual databases.
• In essence one- dimensional methods and
  intuition.

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Structural Bioinformatics - Structural Genomics

• Elucidation of the 3D structures of biomolecules.
• Analysis and comparison of biomolecular
  structures.
• Prediction of biomolecular recognition.
• Handles three-dimensional (3-D) structures.
• Geometric Computing. (a methodology shared by
  Computational Geometry, Computer Vision, Computer
  Graphics, Pattern Recognition etc.)

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Protein Structural Comparison
Pseudoazurin - 1pmy
ApoAmicyanin - 1aaj
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Algorithmic Solution
About 1 sec. Fischer, Nussinov, Wolfson 1990.
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Introduction to Protein Structure
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Amino acids and the peptide bond
Cb first side chain carbon (except for glycine).
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Backbone or Secondary structure display
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Wire-frame or ribbons display
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Spacefill model
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Geometric Representation
3-D Curve vi, i1n
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Secondary structure
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? strands and sheets

• Hydrogen bonds.

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The Holy Grail - Protein Folding

• From Sequence to Structure.
• Relatively primitive computational folding models
  have proved to be NP hard even in the 2-D case.

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Determination of protein structures

• X-ray Crystallography
• NMR (Nuclear Magnetic Resonance)
• EM (Electron microscopy)

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An NMR result is an ensemble of models

• Cystatin (1a67)

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The Protein Data Bank (PDB)

• International repository of 3D molecular data.
• Contains x-y-z coordinates of all atoms of the
  molecule and additional data.
• http//pdb.tau.ac.il
• http//www.rcsb.org/pdb/

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Why bother with structureswhen we have sequences
?

• In evolutionary related proteins structure is
  much better preserved than sequence.
• Structural motifs may predict similar
  biological function
• Getting insight into protein folding.
  Recovering the limited (?) number of protein
  folds.

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Applications

• Classification of protein databases by structure.
• Search of partial and disconnected structural
  patterns in large databases.
• Extracting Structure information is difficult, we
  want to extract new folds.

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Applications (continued)

• Speed up of drug discovery.
• Detection of structural pharmacophores in an
  ensemble of drugs (similar substructures in
  drugs acting on a given receptor
  pharmacophore).
• Comparison and detection of drug receptor active
  sites (structurally similar receptor cavities
  could bind similar drugs).

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Object Recognition
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Model Database
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Scene
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Recognition
Lamdan, Schwartz, Wolfson, Geometric
Hashing,1988.
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Protein Alignment Geometric Pattern
Discovery
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Protein Alignment

• The superimposition pattern is not known
  a-priori pattern discovery .
• The matching recovered can be inexact.
• We are looking not necessarily for the
• largest superimposition, since other
• matchings may have biological meaning.

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Geometric Task
Given two configurations of points in the
three dimensional space,
find those rotations and translations of one
of the point sets which produce large
superimpositions of corresponding 3-D
points.
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Geometric Task (continued)

• Aspects
• Object representation (points, vectors, segments)

• Object resemblance (distance function)

• Transformation (translations, rotations, scaling)

-gt Optimization technique
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Transformations

• Translation
• Translation and Rotation
• Rigid Motion (Euclidian Trans.)
• Translation, Rotation Scaling

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Inexact Alignment. Simple case two closely
related proteins with the same number of amino
acids.
Question how to measure alignment error?
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Superposition - best least squares(RMSD Root
Mean Square Deviation)
Given two sets of 3-D points Ppi, Qqi ,
i1,,n rmsd(P,Q) v S ipi - qi 2 /n Find a
3-D rigid transformation T such that rmsd(
T(P), Q ) minT v S iTpi - qi 2 /n
A closed form solution exists for this task. It
can be computed in O(n) time.
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Problem statement with RMSD metric.
Given two configurations of points in the
three dimensional space, and e threshold
find the largest alignment, a set of matched
elements and transformation, with RMSD less than
e. (belong to NP,)
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Distance Functions

• Two point sets Aai i1n
• Bbj j1m
• Pairwise Correspondence
• (ak1,bt1) (ak2,bt2) (akN,btN)

(1) Exact Matching aki bti0
(2) RMSD (Root Mean Square Distance)
Sqrt( Saki bti2/N) lt e (3) Bottleneck
max aki bti

• Hausdorff distance h(A,B)maxa?A minb?B a
  b
• H(A,B)max(
  h(A,B), h(B,A))

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Docking Problem

• Given two molecules find their correct
  association

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Docking Problem

?
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Docking Problem

?
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How to present a paper in Computer Science
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Lecture Preparation

• The lecture should cover a given slot of time
  (90 minutes).
• Use PowerPoint slides for presentation.
• Each slide usually spans 1-2 minutes.
• The slides should not be overloaded.
• Use mouse or pointer.
• Use colors, pictures, tables and animation, but
  dont exaggerate.

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What to say and how

• Communicate the key ideas during your lecture.
• Dont get lost in technical details.
• Structure your talk.
• Use a top-down approach.

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Lecture Structure

• Introduction general description of the paper.
• Body - abstract of the current method.
• Technical details.
• Conclusions and discussion.

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Introduction

• Most important part of your talk!
• Title short explanation about the presented
  topic.
• Lecture outline.
• Problem definition, input and output. Dont
  forget to define the problem!
• Problem motivation.
• Introduce terminology of the field.
• Short review of existing approaches (dont
  forget to add references!).

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Body

• Abstract of the major results presented in the
  paper.
• Significance of the results.
• Sketch of the method.

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Technicalities

• Extended presentation of the method.
• Present key algorithmic ideas clearly and
  carefully.
• Complexity of the method.
• Experimental results.

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Conclusions and Discussion

• Summarize major contributions of the work.
• You can highlight points based on technical
  details you couldnt discuss in introduction.
• Present related open problems.
• Dont forget to thank the audience !!!
• Questions.

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Getting to the Audience

• Use repetitions
• Tell them what you're going to tell them.
• Tell them.
• Then tell them what you told them".
• Remind, dont assume
• Maintain eye contact
• Control your voice and motion

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Thanks!!!and Good Luck in your lectures!