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Structure Prediction and Modeling of Biological Macromolecules Jürgen Sühnel [email protected] Leibniz Institute for Age Research, Fritz Lipmann Institute (FLI) Jena Centre for Bioinformatics (JCB) Jena Centre for Systems Biology of Ageing (JenAge) Jena / Germany http://www.fli-leibniz.de/groups/suehnel_3D_en.php Outline Proteins – 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 (Molecular Dynamics) • Refinement • Exploring the effect of single amino acid substitutions • Ligand effects on protein structure and dynamics (induced fit) Nucleic Acids – Secondary structure – 3D structure PDB Content Growth Year 1980 1993 2003 2009 2010 2011 Yearly 16 695 4167 7396 7923 8123 Total 70 1582 23597 62191 70114 78237 structures structures (~ 2 new structures per day) structures (~ 11 new structures per day) structures (~ 20 new structures per day) structures (~ 22 new structures per day) structures (~ 22 new structures per day) (nur experimentelle Strukturen) PDB Content Growth May 29, 2012 UniProt/SwissProt: Growth Rate 29.05.2012 Release 2012_05 of 16-May-12 of UniProtKB/Swiss-Prot contains 536029 sequence entries, comprising 190235160 amino acids abstracted from 209686 references. UniProt/TrEMBL: Growth Rate 29.05.2012 Release 2012_05 of 16-May-2012 of UniProtKB/TrEMBL contains 22128511 sequence entries, comprising 7226807757 amino acids . Swiss-Prot/TrEMBL: Amino Acid Composition Swiss-Prot TrEMBL 15-Jan-2008 Protein Structure Prediction Structural 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. Protein Structure Prediction A 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 http://www.cs.bgu.ac.il/~dfischer/CAFASP5/ Protein Structure Prediction – CASP Contest http://predictioncenter.gc.ucdavis.edu/ Protein Structure Prediction – CASP Contest http://predictioncenter.gc.ucdavis.edu/ Lysozyme Lysozyme – 5lyz Lysozyme – 5lyz: Information from the JenaLib Atlas Page Lysozyme – 5lyz: Information from the JenaLib Atlas Page Lysozyme – 5lyz: Information from the JenaLib Atlas Page Lysozyme – 5lyz: Information from the JenaLib Atlas Page - ProSite Lysozyme – 5lyz: PROSITE Signature PROMOTIF Secondary Structure Analysis – 5lyz . . Protein Backbone Torsion Angles D. W. Mount: Bioinformatics, Cold Spring Harbor Laboratory Press, 2001. Sidechain Torsion/Dihedral Angles PROMOTIF Secondary Structure Analysis – 5lyz Chou-Fasman Secondary Structure Prediction 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 l 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 • 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 – 5lyz: Chou-Fasman Secondary Structure Prediction http://fasta.bioch.virginia.edu/fasta_www/chofas.htm Lysozyme – 5lyz: Chou-Fasman Secondary Structure Prediction GRCE RCEL CELA ELAA (0.57|0.98|0.70|1.39) 0.91 (0.98|0.70|1.39|1.41) 1.12 (0.70|1.39|1.41|1.42) (1.39|1.41|1.42|1.42) 1.23 1.41 http://fasta.bioch.virginia.edu/fasta_www/chofas.htm Lysozyme – 5lyz: PhD/PROF Structure Prediction PROF_sec: Rel_sec SUB_sec O3_acc P3_acc Rel_acc SUB_acc PROF predicted secondary structure: H=helix, E=extended (sheet), blank=other (loop) PROF = PROF: Profile network prediction Heidelberg reliability index for PROF_sec prediction (0=low to 9=high) 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 observed relative solvent accessibility (acc) in 3 states: b = 0-9%, i = 9-36%, e = 36-100%. PROF predicted relative solvent accessibility (acc) in 3 states: b = 0-9%, i = 9-36%, e = 36-100%. reliability index for PROFacc prediction (0=low to 9=high) 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 http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top 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. http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top PSSM A PSSM, or Position-Specific Scoring Matrix, is a type of scoring matrix used in protein BLAST searches in which amino acid substitution scores are given separately for each position in a protein multiple sequence alignment. Thus, a Tyr-Trp substitution at position A of an alignment may receive a very different score than the same substitution at position B. This is in contrast to position-independent matrices such as the PAM and BLOSUM matrices, in which the Tyr-Trp substitution receives the same score no matter at what position it occurs. 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. Conserved Domain Database http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml PsiPred 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). http://bioinf.cs.ucl.ac.uk/psipred/psiform.html Comparing Secondary Structure Prediction Results PsiPred Chou-Fasman Phd/PROF Comparing Secondary Structure Prediction Results 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 Protein Structure Prediction http://speedy.embl-heidelberg.de/gtsp/flowchart2.html Modeling by Homology (Comparative Modeling) http://salilab.org/modeller/ Modeling by Homology (Comparative Modeling) http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi Modeling by Homology (Comparative Modeling) http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi Modeling by Homology (Comparative Modeling) http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi Modeling by Homology (Comparative Modeling) http://swissmodel.expasy.org/ Modeling by Homology (Comparative Modeling) 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. http://salilab.org/modeller/ Threading – Sequence Structure Alignment Methods of protein fold recognition or threading or sequence-structure alignment 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. Fold Recognition (Threading) – Why ? • 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 Threading • Use protein sequence alignment (modeling by homology) • Use 3D profiles • How buried, partly buried, exposed are amino acids? • How is the fraction of surrounding environment that is polar or apolar? • Use contact potentials Fold 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 Recognition http://www.sbg.bio.ic.ac.uk/~3dpssm/index2.html 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 Ab Initio Prediction http://robetta.bakerlab.org Ab Initio Prediction Ab Initio Prediction Simons, Strauss, Baker. J. Mol. Biol. 2001, 306, 1191-1199. Ab Initio Prediction – Lysozyme (5lyz) http://rosettadesign.med.unc.edu/ Protein Model Portal http://www.proteinmodelportal.org/ Simulation of Protein Folding Simulation of Protein Folding Thousand trillon FLOPs IBM Blue Gene Project | System-on-a-Chip Approach ~ 65.000 processors teraflop – a trillion floating point operations per second Quantum Chemistry Quantum Chemistry Quantum-chemical Calculations: Telomeric DNA Quantum-chemical Calculations: Telomeric DNA Molecular Dynamics Simulation of Protein Folding – Molecular Dynamics AMBER GROMOS CHARMM TINKER Molecular Mechanics (Force Field) http://cmm.info.nih.gov/modeling/guide_documents/molecular_mechanics_document.html How Do We Get the Parameters ? Experimental Data (Examples: Geometrical Parameters) Quantum-chemical Calculations (Examples: Charges) Geometry Optimization Molecular Dynamics Simulation Protein Capsid Of Filamentous Bacteriophage Ph75 From Thermus Thermophilus 1HGV, extended structure 1HGV, actual structure 1HGV, 61% helix, 1.928 ns 1HGV, 75% helix, 3.428 ns Images created using VMD (Visual Molecular Dynamics) (HUMPHREY, W., DALKE, A. and SCHULTEN, K., 1996.VMD - Visual Molecular Dynamics. Journal Molecular Graphics,14, pp33-38). Optimization Methods – Newton-Raphson Methods g -. gradient h - Hessian Optimization Methods – Steepest Descent Steepest descent Optimization Methods – Conjugate Gradients Method Molecular Dynamics Simulation amber.scripps.edu Molecular Dynamics Simulation Molecular Dynamics Simulation – GROMOS Package www.gromos.net Molecular Dynamics Packages www.charmm.org Molecular Dynamics Packages dasher.wustl.edu/ffe/ Visualizing and Analyzing Molecular Dynamics Simulations www.ks.uiuc.edu/Research/vmd/ Folding Surface for Lysozyme Dobson, Sali, Karplus, Angew. Chem. Int. Ed. 1998, 37, 868. Protein Folding States Dobson, Sali, Karplus, Angew. Chem. Int. Ed. 1998, 37, 868. Monitoring Protein Folding by Experimental Methods Dobson, Sali, Karplus, Angew. Chem. Int. Ed. 1998, 37, 868. Monitoring Protein Folding by Experimental Methods Paxco, Dobson, Curr. Opin. Struct. Biol. 1996, 6, 630. Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Villin headpiece domain (PDB code: 1vii) Actin binding site highlighted 36 amino acids Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Radius of Gyration In a globular protein the radius of gyration Rg can be predicted with reasonable accuracy from the relationship Rg(pred) = 2.2 N 0.588 where N is the number of amino acids. Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Statistical Potentials A statistical potential or knowledge-based potential is an energy function derived from an analysis of known protein structures. They are mostly applied to pairwise amino acid interactions. The statistical potential assigns to each possible pair of amino acids a weight or score or energy. Statistical potentials are applied to protein structure prediction and to protein folding. Their physical interpretation is highly disputed. Nevertheless, they have been applied with great success, and do have a rigorous probabilistic justification. Thomas, Dill, J. Mol. Biol. 1996, 257, 457-469 Statistical Potentials Boltzmann distribution: The Boltzmann distribution applied to a specific pair of amino acids, is given by: where r is the distance, k is the Boltzmann constant, T is the temperature and Z is the partition function, with The quantity F(r) is the free energy assigned to the pairwise system. Simple rearrangement results in the inverse Boltzmann formula, which expresses the free energy F(r) as a function of P(r): To construct a so-called Potentail of Mean Force (PMF) , one then introduces a so-called reference state with a corresponding distribution QR and partition function ZR, and calculates the following free energy difference: The reference state typically results from a hypothetical system in which the specific interactions between the amino acids are absent. The second term involving Z and ZR can be ignored, as it is a constant. Statistical Potentials In practice, P(r) is estimated from the database of known protein structures, while QR(r) typically results from calculations or simulations. For example, P(r) could be the conditional probability of finding the Cβ atoms of a valine and a serine at a given distance r from each other, giving rise to the free energy difference ΔF. The total free energy difference of a protein, ΔFT, is then claimed to be the sum of all the pairwise free energies: where the sum runs over all amino acid pairs ai,aj (with i < j) and rij is their corresponding distance. It should be noted that in many studies QR does not depend on the amino acid sequence Intuitively, it is clear that a low free energy difference indicates that the set of distances in a structure is more likely in proteins than in the reference state. However, the physical meaning of these PMFs have been widely disputed since their introduction. The main issues are the interpretation of this "potential" as a true, physically valid potential of mean force, the nature of the reference state and its optimal formulation, and the validity of generalizations beyond pairwise distances. Statistical Potentials wij(r) ij(r) * – - interaction free energy pair density reference pair density at infinite separation Statistical potentials can be determined by simply counting interactions of a specific type in a dataset of experimental structures. The distance dependence may or may not be taken into account. If not, the interaction free energy is usually called a contact potential. It represents an average over distances shorter than some cutoff distance rc. Thomas, Dill, J. Mol. Biol. 1996, 257, 457-469 Lattice Folding Lattice Algorithm • • • • • • Red = hydrophobic, Blue = hydrophilic If Red is near empty space E = E+1 If Blue is near empty space E = E-1 If Red is near another Red E = E-1 If Blue is near another Blue E = E+0 If Blue is near Red E = E+0