CZ5225 Methods in Computational Biology Lecture 8: Protein Structure Prediction Methods . Chen Yu Zong Tel: [phone] Email: [e-mail] http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, NUS August 2004

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CZ5225 Methods in Computational Biology
Lecture 8 Protein Structure Prediction
Methods. Chen Yu ZongTel 6874-6877Email
csccyz_at_nus.edu.sghttp//xin.cz3.nus.edu.sgRoom
07-24, level 7, SOC1, NUSAugust 2004
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Protein Structural Organization

• Proteins are made from just 20 kinds of amino
  acids

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Protein Structural Organization

• Protein has four
• levels of structural
• organization

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Protein Folding Sequence-Structure-Function
Relationship
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Protein Folding Sequence-Structure-Function
Relationship
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Measuring Structural SimilarityThe use of RMSD
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Measuring Structural Similarity
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Measuring Structural Similarity
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Measuring Structural Similarity
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Protein Structure Prediction
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Protein Structure Prediction
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Protein Secondary Structure Prediction

• Secondary structure forms early in protein
  folding process.
• Identification of secondary structural elements
  makes the topology of protein structure more
  obviousso that similar ones can be identified in
  a topology database such as TOPS.
• Prediction of the positions and lengths of
  secondary structure elements can be used as a
  prelude to "docking" these secondary structural
  elements against each other
• Useful guide in the construction or refinement of
  primary structure alignments, and to the correct
  correspondence between parts of two proteins'
  respective tertiary structures.
• Useful for making some kind of intelligent guess
  about the higher order structure of your protein

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Protein Secondary Structure Prediction

• Traditional methods CF, GOR Accuracy 60
• Recent improvements Neural network, homologous
  sequences Accuracy gt 70
• References
• "Prediction of the secondary structure of
  proteins from their amino acid sequence", P. Y.
  Chou, G. D. Fasman, 1978, Adv. Enzymolog. Relat.
  Areas Mol. Biol., 47, 45-147.
• "GOR method for predicting secondary structure
  from amino acid sequence", J. Garnier, J.-F.
  Gibrat, B. Robson, 1996, Methods Enzymol., 266,
  540-553.
• "Analysis of the accuracy and implications simple
  methods for predicting the secondary structure of
  globular proteins", J. Garnier, D. J. Osguthorpe,
  B. Robson, 1978, J. Mol. Biol., 120, 45-147.
• "Improvements in protein secondary structure
  prediction by an enhanced neural network",
  Kneller, 1990, J. Mol. Biol., 214, 171-182

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Protein Secondary Structure Prediction

• Software
• Zvelebil, M.J.J.M., Barton, G.J., Taylor, W.R.
  Sternberg, M.J.E. (1987). Prediction of Protein
  Secondary Structure and Active Sites Using the
  Alignment of Homologous Sequences Journal of
  Molecular Biology, 195, 957-961. (ZPRED)
• Rost, B. Sander, C. (1993), Prediction of
  protein secondary structure at better than 70
  Accuracy, Journal of Molecular Biology, 232,
  584-599. PHD)
• Salamov A.A. Solovyev V.V. (1995), Prediction
  of protein secondary strurcture by combining
  nearest-neighbor algorithms and multiply sequence
  alignments. Journal of Molecular Biology, 247,1
  (NNSSP)
• Geourjon, C. Deleage, G. (1994), SOPM a self
  optimized prediction method for protein secondary
  structure prediction. Protein Engineering, 7,
  157-16. (SOPMA)
• Solovyev V.V. Salamov A.A. (1994) Predicting
  alpha-helix and beta-strand segments of globular
  proteins. (1994) Computer Applications in the
  Biosciences,10,661-669. (SSP)
• Wako, H. Blundell, T. L. (1994), Use of
  amino-acid environment-dependent substitution
  tables and conformational propensities in
  structure prediction from aligned sequences of
  homologous proteins. 2. Secondary Structures,
  Journal of Molecular Biology, 238, 693-708.
• Mehta, P., Heringa, J. Argos, P. (1995), A
  simple and fast approach to prediction of protein
  secondary structure from multiple aligned
  sequences with accuracy above 70 . Protein
  Science, 4, 2517-2525. (SSPRED)
• King, R.D. Sternberg, M.J.E. (1996)
  Identification and application of the concepts
  important for accurate and reliable protein
  secondary structure prediction. Protein Sci,5,
  2298-2310. (DSC).

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Protein Secondary Structure Prediction

• Types of amino acids
• Hydrophobic
• Hydrophilic, Neutral
• Hydrophilic, Acidic
• Hydrophilic, Basic

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Protein Secondary Structure Prediction

• Types of Secondary Structures
• Alpha helix and Beta- sheet

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Protein Secondary Structure Prediction

• Secondary Structures Favored Peptide Conformation

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Protein Secondary Structure Prediction

• Secondary Structures
• Computation of structural propensity of a residue

• Data derived from proteins of known structure is
  used to calculate 'propensities' for each amino
  acid type for adopting helix, sheet or turn

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Protein Secondary Structure Prediction

• Secondary Structures
• Computation of structural propensity of a residue

• Three states alpha helix, beta sheet, turn

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Protein Secondary Structure Prediction

• Structural propensity of
• amino acids
• Each residue is assigned to
• one of the three classes
• Forming residues favor a structure
• Indifferent residues
• Breaking residues stop the extension of a
  structure

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Protein Secondary Structure Prediction

• Position specific turn parameters

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Protein Secondary Structure Prediction

• Chou and Fasman procedure
• Find helical initiation regions
• Extend helices until they reach tetrapeptide
  breakers
• Find beta initiation regions
• Extend until they reach tetrapeptide breakers
• Find turns
• Resolve conflicts between alpha and beta
• Somewhat subjective often have overlaps. Chou
  and Fasman suggest using additional information
• alpha-beta pattern, i.e. does this look like an
  b-a-b structure ???
• end probabilities Chou and Fasman in later
  papers also tabulated the preferences for the
  residues to occur at the amino and carboxyl
  terminal ends of a and b structures.
• These can be used to resolve overlaps
• Chou and Fasman did not provide an explicit
  algorithm for this conflict resolution, relying
  on their expert
• judgment. This meant that each persons
  prediction could be different. Most people are
  not experts.
• "Prediction of the secondary structure of
  proteins from their amino acid sequence",
• P. Y. Chou, G. D. Fasman, 1978, Adv. Enzymolog.
  Relat. Areas Mol. Biol., 47, 45-147.

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Protein Secondary Structure Prediction
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Homology Modeling
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Homology Modeling

• Reference
• Sanchez R, Sali A. Advances in comparative
  protein-structure modelling. Curr Opin Struct
  Biol. 1997 Apr7(2)206-14.
• Krieger E, Nabuurs SB, Vriend G. Homology
  modeling. Methods Biochem Anal. 200344509-23
• Rodriguez R, Chinea G, Lopez N, Pons T, Vriend G.
  Homology modeling, model and software evaluation
  three related resources. Bioinformatics.
  199814(6)523-8
• Alexandrov NN, Luethy R. Alignment algorithm for
  homology modeling and threading. Protein Sci.
  1998 Feb7(2)254-8

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Homology Modeling

• Basic Idea
• Similar sequencegt Similar structure
• Structure is conserved more than sequence
• Structure of new protein derived using existing
  protein structures as templates.
• Changes are compensated for locally.

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Homology Modeling
Twilight Zone below 25 sequence homology
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Homology Modeling

• Similar sequencegt Similar structure

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Homology Modeling

• Step One
• Align sequence of your protein (unknown) with
  that of candidate template proteins (known)

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Homology Modeling

• Step Two
• Select template proteins based on sequence
  similarity and minimize their X-ray structures
• The whole sequence can be matched by one or more
  templates

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Homology Modeling

• Step Three
• Combine the main chain of the template proteins
  and fill-in gap sections to generate a complete
  main chain model of your protein
• Gaps are filled-in by using short sequences from
  a sequence linker library, the selected short

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Homology Modeling

• Step Three
• Combine the main chain of the template proteins
  and fill-in gap sections to generate a complete
  main chain model of your protein
• Gaps are filled-in by using short sequences from
  a sequence linker library, the selected short
  sequences need to be exchangeable to the section
  of your original protein.

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Homology Modeling

• Step Four Adding side chains to the main-chain
  model based on the sequence of your protein
• Mutate and add

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Homology Modeling

• Step Five
• Minimization and MD of the homology model of your
  protein

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Homology Modeling

• Swiss-Model - an automated homology modeling
  server developed at Glaxo Welcome Experimental
  Research in Geneva.  http//www.expasy.ch/swissmod
  / 
• Closely linked to Swiss-PdbViewer, a tool for
  viewing and manipulating protein structures and
  models. 
• Likely take 24 hours to get results returned!  

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Homology Modeling

• How Swiss-model works?
• 1)  Search for suitable templates
• 2)  Check sequence identity with target
• 3)  Create ProModII jobs
• 4)  Generate models with ProModII
• 5)  Energy minimization with Gromos96 
• First approach mode (regular)
• First approach mode (with user-defined template)
• Optimize mode  

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Homology Modeling

• How Swiss-model works?
• Program  Database  Action
• BLASTP2   ExNRL-3D   Find homologous
  sequences
• of
   proteins with known structure.  
• SIM    --  Select
  all templates with sequence
• 
  identities above 25. 
• -- --
  Generate ProModII input files
• ProModII   ExPDB   Generate all
  models  
• Gromos96  --    Energy
  minimization of all models  

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Threading Methods

• Similar proteins at the sequence level may have
  very different secondary structures. On the other
  hand, proteins very different at the sequence
  level may have similar structures. Why? Because
  the protein function is determined by its
  functional sites, which reside in the cores not
  the loops.
• Therefore, researchers propose the inverse
  protein folding problem, namely, fitting a known
  structure to a sequence.
• The problem of aligning a protein sequence to a
  given structural model is known as protein
  threading.
• Given a protein whose structure is known, we
  derive a structural model by replacing amino
  acids by place-holders, each is associated with
  some basic properties such as an alpha-helix or
  beta-strand or loop of the original amino acids.

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Threading Methods

• References and software
• Lemer C., Rooman, M. J. Wodak, S. J. (1996),
  Protein Structure Prediction By Threading
  Methods Evaluation Of Current Techniques,
  PROTEINS Structure, Function and Genetics, 23,
  337-355.
• Bryant, S. H. Lawrence, C. E. (1993), An
  empirical energy function for threading a protein
  sequence through the folding motif, PROTEINS
  Structure, Function and Genetics, 16, 92-112.
• Alexandrov NN, Luethy R. Alignment algorithm for
  homology modeling and threading. Protein Sci.
  1998 Feb7(2)254-8
• Jones, D.T., Taylor, W.R Thornton, J.M (1992),
  A new approach to protein fold recognition,
  Nature,358, 86-89. (THREADER).

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Threading Methods

• Threading methods take the amino acid sequence of
  an uncharacterized protein structure, rapidly
  compute models based on a large set of existing
  3D structures. 
• The algorithm then evaluates these models to
  determine how well the unknown amino acid fits
  each template structure. 
• All the threading models in the second to most
  recent CASP competition produced accurate models
  in less than half of the cases. 
• However, threading is more successful than
  homology modeling when attempting to detect
  remote homologies that cant be detected by
  standard sequence alignment. 

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Threading Methods

• Protein Threading Model
• Input
• A protein sequence A with n amino acids
• A structural model with m core segments Ci
• (1) Each core segment Ci has length ci.
• (2) Core segments Ci and Cj are connected by loop
  Li, which has length between li-min and li-max.
• (3) The local structural environment for each
  amino acid position, such as chemical properties
  and spatial constraints.
• A score function to evaluate a given threading.
• Output
• T t1, t2, ..., tm of integers, where ti is
  the amino acid position in A that occupies the
  first position in core segment Ci.

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Threading Methods

• Protein Threading Model
• An algorithm Branch and bound
• Spatial constraints
• 1 SUM (cj lj-min) lt ti lt n 1 - SUM
  (cj lj-min)
• j lt i
  j gt i
• ti ci li-min lt ti1 lt ti ci li-max
• A score function (second order, considering
  pairwise interaction)
• f(T) SUM g1(i,ti) SUM g2(i,j,ti,tj)
• i j gt i
• Algorithm testing self-threading and using
  structural analogs.

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Ab initio Methods

• ab initio means from the beginning. 
• Ab-initio algorithms attempt to predict structure
  based on sequence information alone (i.e., no
  emperical structural info is considered). 
• Although many researchers are working in this
  vein, it is a science in progress sometimes
  marginally successful, but very unreliable. 
• Methods MD and Simplified models

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Ab initio Methods

• References
• Hardin C, Pogorelov TV, Luthey-Schulten Z. Ab
  initio protein structure prediction. Curr Opin
  Struct Biol. 2002 Apr12(2)176-81. Review.
• Srinivasan R, Rose GD. Ab initio prediction of
  protein structure using LINUS. Proteins. 2002 Jun
  147(4)489-95.
• Bonneau R, Strauss CE, Rohl CA, Chivian D,
  Bradley P, Malmstrom L, Robertson T, Baker D. De
  novo prediction of three-dimensional structures
  for major protein families.
• J Mol Biol. 2002 Sep 6322(1)65-78.
• Bystroff C, Shao Y. Fully automated ab initio
  protein structure prediction using I-SITES,
  HMMSTR and ROSETTA. Bioinformatics. 2002 Jul18
  Suppl 1S54-61

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Ab initio Methods

• LINUS as an example Local Independently
  Nucleated Units of Structure
• 50 amino acids are folded at a time, in an
  overlapping fashion 1-50, 26-75, ...
• Based on the idea that actual proteins fold by
  forming local secondary structure first.
• Side chains are simplified. Only 3 interactions
  are used
• 1 repulsive steric
• 2 attractive H-bonds and hydrophobic
• Then the calculation of all possibilities for the
  search of the lowest free energy

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CZ5225 Methods in Computational Biology
Assignment 2

• Option 1
• Write a code for protein secondary structure
  prediction.
• Test your code on several selected proteins and
  compare your prediction results with those from
  the PHD software at http//npsa-pbil.ibcp.fr
• Option 2
• Write a code for protein homology modeling
• Test your code on several selected proteins,
  compute the rmsd of each of your predicted
  structures against an x-ray structure of that
  protein.
• Option 3
• Write a code for structural comparison of two
  structures of unequal number of atoms. Test your
  code on several pairs of molecules/proteins and
  compute the rmsd between each pairs
• Requirement Write a report about the theory,
  algorithm, testing results, and suggested
• Improvement/future work and submit together with
  a soft copy of your code.