Jürgen Sühnel [email protected] Supplementary Material: 3D Structures of Biological Macromolecules Part 5:

Jürgen SühnelJürgen Sü[email protected]@fli-leibniz.de

Supplementary Material: http://www.fli-leibniz.de/www_bioc/3D/

3D Structures of Biological Macromolecules3D Structures of Biological MacromoleculesPart 5: Protein Structure Prediction Part 5: Protein Structure Prediction

Leibniz Institute for Age Research, Fritz Lipmann Institute,Leibniz Institute for Age Research, Fritz Lipmann Institute,Jena Centre for BioinformaticsJena Centre for Bioinformatics

Jena / GermanyJena / Germany

PDB Content GrowthPDB Content Growth

1993: 698 = 1588 structures (~ 2 structures per day)2003: 4187 = 23674 structures (~ 11 structures per day)2005: 5421 = 34325 structures (~ 15 structures per day)2008: 7069 = 55063 structures (~ 19 structures per day) (experimental structures only)

Sta

rt

May 12, 2009May 12, 2009

PDB Content StatisticsPDB Content Statistics

SwissProt/TrEMBL: Growth Rate SwissProt/TrEMBL: Growth Rate

15-Jan-2008

Swiss-Prot/TrEMBL: Amino Acid Composition Swiss-Prot/TrEMBL: Amino Acid Composition

Swiss-Prot TrEMBL

15-Jan-2008

Structural GenomicsStructural Genomics

Structural genomics consists in the determination of the three dimensional structure of all proteins of a given organism, by experimental methods such as X-ray crystallography, NMR spectroscopy or computational approaches such as homology modelling.

As opposed to traditional structural biology, the determination of a protein structure through a structural genomics effort often (but not always) comes before anything is known regarding the protein function. This raises new challenges in structural bioinformatics, i.e. determining protein function from its 3D structure.

One of the important aspects of structural genomics is the emphasis on high throughput determination of protein structures. This is performed in dedicated centers of structural genomics.

While most structural biologists pursue structures of individual proteins or protein groups, specialists in structural genomics pursue structures of proteins on a genome wide scale. This implies large scale cloning, expression and purification. One main advantage of this approach is economy of scale. On the other hand, the scientific value of some resultant structures is at times questioned.

en.wikipedia.org/wiki/Structural_genomics

http://en.wikipedia.org/wiki/Proteins

http://en.wikipedia.org/wiki/X-ray_crystallography

http://en.wikipedia.org/wiki/Protein_NMR

http://en.wikipedia.org/wiki/Homology_modelling

Structural GenomicsStructural Genomics

Protein Structure Prediction Protein Structure Prediction

http://speedy.embl-heidelberg.de/gtsp/flowchart2.html

clickable map


A Good Protein StructureA Good Protein Structure

• Minimizes disallowed torsion angles

• Maximizes number of hydrogen bonds

• Minimizes interstitial cavities or spaces

• Minimizes number of “bad” contacts

• Minimizes number of buried charges

Protein Structure Prediction – CAFASP Contest Protein Structure Prediction – CAFASP Contest

http://www.cs.bgu.ac.il/~dfischer/CAFASP5/

Protein Structure Prediction – CASP Contest Protein Structure Prediction – CASP Contest

http://predictioncenter.gc.ucdavis.edu/


– Secondary structure

– 3D structure• Modeling by homology (Comparative modeling)• Fold recognition (Threading)• Ab initio prediction

– Rule-based approaches– Lattice models– Simulating the time dependence of folding

• Refinement• Exploring the effect of single amino acid substitutions• Ligand effects on protein structure and dynamics

(induced fit)

Lysozyme Lysozyme

Lysozyme – 5lyz Lysozyme – 5lyz

Lysozyme – 5lyz Lysozyme – 5lyz

Lysozyme – 5lyz: Information from the JenaLib Atlas Page Lysozyme – 5lyz: Information from the JenaLib Atlas Page




Lysozyme – 5lyz: PROSITE Signature Lysozyme – 5lyz: PROSITE Signature

Lysozyme – 5lyz: PROSITE Signature Lysozyme – 5lyz: PROSITE Signature

PROMOTIF Secondary Structure Analysis – 5lyz PROMOTIF Secondary Structure Analysis – 5lyz

.

.

Protein Backbone Torsion AnglesProtein Backbone Torsion Angles

D. W. Mount: Bioinformatics, Cold Spring Harbor Laboratory Press, 2001.

Protein Backbone Torsion AnglesProtein Backbone Torsion Angles




Chou-Fasman Secondary Structure Prediction Chou-Fasman Secondary Structure Prediction

Amino Acid Propensities Amino Acid Propensities

From a database of experimental 3D structures, calculate the propensity for a given amino acid to adopt a certain type of secondary structure

Example:

N(Ala)=2.000; N(tot)=20.000; N(Ala, helix)=568; N(helix)=4,000.

P(Ala,helix) = [N(Ala,helix)/N(helix)] / [N(Ala)/N(tot)]

P(Ala,helix) = [568/4.000]/[2.000/20.000] = 1.42

Used in Chou-Fasman algorithm

Chou-Fasman Secondary Structure Prediction Chou-Fasman Secondary Structure Prediction

• Assign all of the residues in the peptide the appropriate set of parameters. • Scan through the peptide and identify regions where 4 out of 6 contiguous residues have P(a-helix) > 100. • That region is declared an alpha-helix. Extend the helix in both directions until a set of four contiguous

residues that have an average P(a-helix) < 100 is reached. That is declared the end of the helix. If the segment defined by this procedure is longer than 5 residues and the average P(a-helix) > P(b-sheet) for that segment, the segment can be assigned as a helix.

• Repeat this procedure to locate all of the helical regions in the sequence. • Scan through the peptide and identify a region where 3 out of 5 of the residues have a value of

P(b-sheet) > 100. That region is declared as a beta-sheet. Extend the sheet in both directions until a set of four contiguous residues that have an average P(b-sheet) < 100 is reached. That is declared the end of the beta-sheet. Any segment of the region located by this procedure is assigned as a beta-sheet if the average P(b-sheet) > 105 and the average P(b-sheet) > P(a-helix) for that region.

• Any region containing overlapping alpha-helical and beta-sheet assignments are taken to be helical if the average P(a-helix) > P(b-sheet) for that region. It is a beta sheet if the average P(b-sheet) > P(a-helix) for that region.

•To identify a bend at residue number j, calculate the following value p(t) = f(j)f(j+1)f(j+2)f(j+3) where the f(j+1) value for the j+1 residue is used, the f(j+2) value for the j+2 residue is used and the f(j+3) value for the j+3 residue is used. If: (1) p(t) > 0.000075; (2) the average value for P(turn) > 1.00 in the tetrapeptide; and (3) the averages for the tetrapeptide obey the inequality P(a-helix) < P(turn) > P(b-sheet), then a beta-turn is predicted at that location.

Lysozyme –Lysozyme – 5lyz: Chou-Fasman Secondary Structure Prediction 5lyz: Chou-Fasman Secondary Structure Prediction

http://fasta.bioch.virginia.edu/fasta_www/chofas.htm

Lysozyme – 5lyz: Chou-Fasman Secondary Structure Prediction Lysozyme – 5lyz: Chou-Fasman Secondary Structure Prediction

http://fasta.bioch.virginia.edu/fasta_www/chofas.htm

GRCE (0.57|0.98|0.70|1.39) 0.91

RCEL (0.98|0.70|1.39|1.41) 1.12

CELA (0.70|1.39|1.41|1.42) 1.23 ELAA (1.39|1.41|1.42|1.42) 1.41

Lysozyme – 5lyz: PhD/PROF Structure Prediction Lysozyme – 5lyz: PhD/PROF Structure Prediction

http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top

PROF_sec: PROF predicted secondary structure: H=helix, E=extended (sheet), blank=other (loop)PROF = PROF: Profile network prediction Heidelberg

Rel_sec reliability index for PROF_sec prediction (0=low to 9=high) SUB_sec subset of the PROFsec prediction, for all residues with an expected average accuracy > 82% (tables in header)

NOTE: for this subset the following symbols are used:L: is loop (for which above ' ' is used).: means that no prediction is made for this residue, as the reliability is: Rel < 5

O3_acc observed relative solvent accessibility (acc) in 3 states: b = 0-9%, i = 9-36%, e = 36-100%. P3_acc PROF predicted relative solvent accessibility (acc) in 3 states: b = 0-9%, i = 9-36%, e = 36-100%.Rel_acc reliability index for PROFacc prediction (0=low to 9=high) SUB_acc subset of the PROFacc prediction, for all residues with an expected average correlation > 0.69 (tables in header)

NOTE: for this subset the following symbols are used:I: is intermediate (for which above ' ' is used).: means that no prediction is made for this residue, as the reliability is: Rel < 4

Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST


Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST


Lysozyme – 5lyz: PhD/PROF Structure Prediction Lysozyme – 5lyz: PhD/PROF Structure Prediction


• Perform BLAST search to find local alignments• Remove alignments that are “too close”• Perform multiple alignments of sequences• Construct a profile (PSSM) of amino-acid frequencies at each residue• Use this profile as input to the neural network• A second network performs “smoothing”• The third level computes jury decision of several different instantiations of

the first two levels.

Lysozyme – 5lyz: PsiPred Structure Prediction Lysozyme – 5lyz: PsiPred Structure Prediction

http://bioinf.cs.ucl.ac.uk/psipred/psiform.html

PsiPredPsiPred

http://bioinf.cs.ucl.ac.uk/psipred/psiform.html

PSIPRED is a simple and reliable secondary structure prediction method, incorporating two feed-forward neural networks which perform an analysis on output obtained from PSI-BLAST (Position Specific Iterated - BLAST).

Version 2.0 of PSIPRED includes a new algorithm which averages the output from up to 4 separate neural networks in the prediction process to further increase prediction accuracy.

Using a very stringent cross validation method to evaluate the method's performance, PSIPRED 2.0 is capable of achieving an average Q3 score of nearly 78%. Predictions produced by PSIPRED were also submitted to the CASP4 server and assessed during the CASP4 meeting, which took place in December 2000 at Asilomar.PSIPRED 2.0 achieved an average Q3 score of 80.6% across all 40 submitted target domains with no obvious sequence similarity to structures present in PDB, which placed PSIPRED in first place out of 20 evaluated methods (an earlier version of PSIPRED was also ranked first in CASP3 held in 1998).

PSI-BLAST PSI-BLAST

Position specific iterative BLAST (PSI-BLAST) refers to a feature of BLAST 2.0 in which a profile (or position specific scoring matrix, PSSM) is constructed (automatically) from a multiple alignment of the highest scoring hits in an initial BLAST search.

The PSSM is generated by calculating position-specific scores for each position in the alignment. Highly conserved positions receive high scores and weakly conserved positions receive scores near zero.

The profile is used to perform a second (etc.) BLAST search and the results of each "iteration" are used to refine the profile.

This iterative searching strategy results in increased sensitivity.

Comparing Secondary Structure Prediction Results Comparing Secondary Structure Prediction Results

PsiPred

Chou-Fasman

Phd/PROF

Comparing Secondary Structure Prediction Results Comparing Secondary Structure Prediction Results

Protein Secondary Structure Prediction - Summary Protein Secondary Structure Prediction - Summary

1st Generation - 1970s• Chou & Fasman, Q3 = 50-55%

2nd Generation -1980s• Qian & Sejnowski, Q3 = 60-65%

3rd Generation - 1990s• PHD, PSI-PRED, Q3 = 70-80%

Features of the new methods:• Taking into account evolutionary information• Neural networks

Failures:• Nonlocal sequence interactions• Wrong prediction at the ends of H/E

Q3 – Percentage of correctly assigned amino acids in a test set


http://speedy.embl-heidelberg.de/gtsp/flowchart2.html

Modeling by Homology (Comparative Modeling) Modeling by Homology (Comparative Modeling)

http://salilab.org/modeller/


http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi






http://swissmodel.expasy.org/


http://salilab.org/modeller/

Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates).

The prediction process consists of

• fold assignment, • target template alignment, • model building, and • model evaluation and refinement.

The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software.

Further advances are necessary in recognizing weak sequence structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences.

Fold Recognition (Threading) Fold Recognition (Threading)

Methods of protein fold recognition attempt to detect similarities between protein 3D structure that are not accompanied by any significant sequence similarity.

The unifying theme of these appraoches is to try and find folds that are compatible with a particular sequence. Unlike sequence-only comparison, these methods take advantage of the extra information made available by 3D structure information.

Rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence.

• Secondary structure is more conserved than primary structure

• Tertiary structure is more conserved than secondary structure

• Therefore very remote relationships can be better detected through 2o or 3o structural homology instead of sequence homology

Fold Recognition (Threading) – Why ? Fold Recognition (Threading) – Why ?

Fold Recognition (Threading) Fold Recognition (Threading)

Fold Recognition (Threading) – 2 Kinds Fold Recognition (Threading) – 2 Kinds

• 2D Threading or Prediction Based Methods (PBM)– Predict secondary structure (SS) or ASA of query– Evaluate on basis of SS and/or ASA matches

• 3D Threading or Distance Based Methods (DBM)– Create a 3D model of the structure– Evaluate using a distance-based “hydrophobicity” or

pseudo-thermodynamic potential

Fold RecognitionFold Recognition

• Database of 3D structures and sequences– Protein Data Bank (or non-redundant subset)

• Query sequence– Sequence < 25% identity to known structures

• Alignment protocol– Dynamic programming

• Evaluation protocol– Distance-based potential or secondary structure

• Ranking protocol

Fold RecognitionFold Recognition

http://www.sbg.bio.ic.ac.uk/~3dpssm/index2.html

Ab Initio Prediction Ab Initio Prediction

• Predicting the 3D structure without any “prior knowledge”

• Used when homology modelling or threading have failed (no homologues are evident)

• Equivalent to solving the “Protein Folding Problem”

• Still a research problem


http://rosettadesign.med.unc.edu/


Simons, Strauss, Baker. J. Mol. Biol. 2001, 306, 1191-1199.

Ab Initio Prediction – Lysozyme (5lyz) Ab Initio Prediction – Lysozyme (5lyz)

http://rosettadesign.med.unc.edu/

Combining Prediction Procedures Combining Prediction Procedures

http://robetta.bakerlab.org/

Molecular Mechanics (Force Field)Molecular Mechanics (Force Field)

http://cmm.info.nih.gov/modeling/guide_documents/molecular_mechanics_document.html

How Do We Get the Parameters ?How Do We Get the Parameters ?

Experimental Data(Examples: Geometrical Parameters)

Quantum-chemical Calculations(Examples: Charges)

Geometry OptimizationGeometry Optimization

Optimization Methods – Steepest DescentOptimization Methods – Steepest Descent

Steepest descent

Optimization Methods – Conjugate Gradients MethodOptimization Methods – Conjugate Gradients Method

Optimization Methods – Newton-Raphson MethodsOptimization Methods – Newton-Raphson Methods

g -. gradienth - Hessian

FLI Computing Facilities

IBM Linux Cluster SGI Altix

Clusters are made up of dedicated components and all components in a cluster are exclusively owned and managed as part of the cluster. All resources are known, fixed and usually uniform in configuration. It is a static environment.

Grids differ from clusters because grids share resources from and among independent system owners. Grids are configured from computer systems that are individually managed and used both as independent systems and as part of the grid. Thus, individual components are not 'fixed' in the grid and the overall configuration of the grid changes over time. This results in a dynamic system that continually assesses and optimises its utilisation of resources.

Cluster vs. Grid Computing Cluster vs. Grid Computing

EUROGRID - BioGRID EUROGRID - BioGRID

www.eurogrid.org/wp1.html

Simulation of Protein FoldingSimulation of Protein Folding

Simulation of Protein FoldingSimulation of Protein Folding

Thousand trillon FLOPs

~ 65.000 processors

teraflop – a trillion floating point operations per second

IBM Blue Gene Project | System-on-a-Chip ApproachIBM Blue Gene Project | System-on-a-Chip Approach

Jürgen Sühnel [email protected] Supplementary Material: 3D Structures of Biological Macromolecules Part 5:

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