The Structure Lectures

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1 The Structure Lectures Boris Steipe Departments of Biochemistry and Molecular and Medical Genetics Program in Proteomics and Bioinformatics University of Toronto 8.0 1

2 The Structure lectures 8.0 Lecture: Use of protein structures 8.1 Lab: Visualization of structure 9.2 Lecture: Homology and structural similarity 9.3 Lab: Homology modeling 8.0 2

4 Lecture 8.0: Use of Protein Structure Boris Steipe Departments of Biochemistry and Molecular and Medical Genetics Program in Proteomics and Bioinformatics University of Toronto ( Some slides have been taken from a lecture held by Chris Hogue, Toronto, for CBW in 2002) 8.0 4

5 Concepts 1. "Sequence" and "structure" are abstractions of biopolymers. 2. Structure can be determined experimentally. 3. Structure abstractions can be stored, retrieved and visualized. 4. Knowledge of structure allows mechanistic explanations. 5. Structure is not arbitrary, but comes in units - motifs, helices, strands, domains and complexes. 6. Domains are folding units, functional units and units of inheritance

6 Concept 1: "Sequence" and "structure" are abstractions of biopolymers

Physical Amino Acids and Amino Acid Abstractions Formula: C 9 H 9 NO 2 Smiles String : [CH]([NH][R])([C](=[O])[R]) [CH2]- [c]1([ch][ch][c]([ch][ch]1)[ OH]) Name: Tyrosine 3-Letter: Tyr 1-Letter: Y N

7 Physical Amino Acids and Amino Acid Abstractions Formula: C 9 H 9 NO 2 Smiles String : [CH]([NH][R])([C](=[O])[R]) [CH2]- [c]1([ch][ch][c]([ch][ch]1)[ OH]) Name: Tyrosine 3-Letter: Tyr 1-Letter: Y N O OH ATOM 1091 N TYR ATOM 1092 CA TYR ATOM 1093 C TYR ATOM 1094 O TYR ATOM 1095 CB TYR ATOM 1096 CG TYR ATOM 1097 CD1 TYR ATOM 1098 CD2 TYR ATOM 1099 CE1 TYR ATOM 1100 CE2 TYR ATOM 1101 CZ TYR ATOM 1102 OH TYR

8 The Concept of Abstract Amino Acids Allows Highly Compressed Information Bulky H-bond Donor Nucleophile Phospho-Acceptor Hydrophobic H-Bond Acceptor Y 2 side chain rotational freedom Aromatic 8.0 8

2D-map of amino acid similarity A C D E F G H I K L M N P Q R S T V W Ala Alanine Cys Cysteine Asp Aspartic acid Glu Glutamic acid Phe Phenyalanine Gly Glycine His Histidine Ile Isoleucine Lys Lysine

9 2D-map of amino acid similarity A C D E F G H I K L M N P Q R S T V W Ala Alanine Cys Cysteine Asp Aspartic acid Glu Glutamic acid Phe Phenyalanine Gly Glycine His Histidine Ile Isoleucine Lys Lysine Leu Leucine Met Methionine Asn Asparagine Pro Proline Gln Glutamine Arg Arginine Ser Serine Thr Threonine Val Valine Trp Tryptophan Y Tyr Tyrosine C S-S is cysteine in a disulfide bond, C SH indicates the free thiol

10 The Concept of Abstract Amino Acid Similarity is Lossy Bulky (FILQRYW) Hydrophobic (FAMILYVW) H-bond Donor (CHKNQRSTWY) Nucleophile (CDESTY) Phospho-Acceptor (STY) H-Bond Acceptor (DEHNQSTY) Y 2 side chain rotational freedom (CDFHSW) Aromatic (FWH)

11 Structure Contextualizes Sequence V V I Y T T G (Tyr262 in 1ERQ.pdb)

12 Structural Abstraction To store structures we need: - coordinate - topology, and z e y g d x b Sulphur Carbon Oxygen Nitrogen - chemical type a information. Met

13 Concept 2: Structure can be determined experimentally

14 Experimental sources of structure X-ray NMR Crystallization required Diffraction Æ data collection The phase problem: MAD, heavy metal isomorphic derivatives or "Molecular replacement" give phase approximations Model building in electron density maps Refinement

$Experimental sources of structure X-ray Crystallization is limiting. Diffraction is not imaging!$

15 Experimental sources of structure X-ray Crystallization is limiting. Diffraction is not imaging! Refinement is required. NMR Data Model

16 Experimental sources of structure X-ray NMR High concentration required ( ~ 1mM) Assignment of peaks determination of crosspeaks Æ distance constraints Calculation of models from distance constraints Refinement

distance constraints Consensus model 1DRO.

17 Experimental sources of structure X-ray Ensemble of structures that are compatible with experimental distance constraints Consensus model 1DRO.PDB NMR Concentration/Solubility Assignment and NOEs Refinement

$Assessing structure quality Metrics: Resolution, R-factor and R-free Bond length and angle deviations Coordinate error can be estimated from diffraction data Programs Whatcheck and Procheck calculate$

18 Assessing structure quality Metrics: Resolution, R-factor and R-free Bond length and angle deviations Coordinate error can be estimated from diffraction data Programs Whatcheck and Procheck calculate quality metrics: (also NMR) Rules of thumb for "good structures": Resolution 2Å, R-factor 20%, mean coordinate error 0.2 Å, RMSD bond-lengts: 0.02Å

19 Concept 3: Structure abstractions can be stored, retrieved and visualized

20 The PDB The PDB is the primary repository of protein structure data

21 What s in a Structure File? Population experiments X-ray, 1 structure NMR - sometimes many structures Incomplete - not all atoms are there Hydrogens, parts of the protein in motion Crystallographic space correct, but not always relevant

22 The PDB format Flat file, column oriented Human readable Human editable Huge legacy problems Flat File: A datafile without indexing structure or hierarchy. In contrast, to relational database, or data grammar

23 Header HEADER IMMUNOGLOBULIN 01-MAR-93 2IMM 2IMM 2 COMPND IMMUNOGLOBULIN VL DOMAIN (VARIABLE DOMAIN OF KAPPA LIGHT 2IMM 3 COMPND 2 CHAIN) OF MCPC603 2IMM 4 SOURCE HUMAN (HOMO $SAPIENS) RECOMBINANT SYNTHETIC M603 GENE 2IMM 5 AUTHOR B.STEIPE,R.HUBER 2IMM 6 REVDAT 1 15-JUL-93 2IMM 0 2IMM 7 REMARK 1 2IMM 8 REMARK 1 REFERENCE 1 2IMM 9 REMARK 1 AUTH B.STEIPE,A.PLUCKTHUN,R.HUBER 2IMM 10 REMARK 1 TITL REFINED CRYSTAL STRUCTURE OF A RECOMBINANT 2IMM 11 REMARK 1 TITL 2 IMMUNOGLOBULIN DOMAIN AND A 2IMM 12 REMARK 1 TITL 3 COMPLEMENTARITY-DETERMINING REGION 1-GRAFTED MUTANT 2IMM 13 REMARK 1 REF J.MOL.BIOL. V IMM 14 REMARK 1 REFN ASTM JMOBAK UK ISSN IMM 15 [...] REMARK 2 2IMM 23 REMARK 2 RESOLUTION ANGSTROMS. 2IMM 24 REMARK 3 2IMM 25 [...]

24 Seqres [...] SEQRES ASP ILE VAL MET THR GLN SER PRO SER SER LEU SER VAL 2IMM 35 SEQRES SER ALA GLY GLU ARG VAL THR MET SER CYS LYS SER SER 2IMM 36 SEQRES GLN SER LEU LEU ASN SER GLY ASN GLN LYS ASN PHE LEU 2IMM 37 SEQRES ALA TRP TYR GLN GLN LYS PRO GLY GLN PRO PRO LYS LEU 2IMM 38 SEQRES LEU ILE TYR GLY ALA SER THR ARG GLU SER GLY VAL PRO 2IMM 39 SEQRES ASP ARG PHE THR GLY SER GLY SER GLY THR ASP PHE THR 2IMM 40 SEQRES LEU THR ILE SER SER VAL GLN ALA GLU ASP LEU ALA VAL 2IMM 41 SEQRES TYR TYR CYS GLN ASN ASP HIS SER TYR PRO LEU THR PHE 2IMM 42 SEQRES GLY ALA GLY THR LYS LEU GLU LEU LYS ARG 2IMM 43 [...] Explicit (above) and implicit sequence may differ!

25 Atom Pitfalls: Atomname is a mix of Chemical element and bond distance. "CA.." ".CA." Atom number Sequence number is actually a string - Chain and insertion code are required to make it unique (e.g B 123A). Amino acid type X Y Z Occ ATOM 119 CA ARG IMM 179 Atom name Sequence number B (Temperature factors) Record type PDB format is strictly column oriented!

26 Hetero Atoms [...] HETATM 877 O HOH IMM 937 [...]

The crystallographic asymmetric units does not necessarily contain a

pdb Tet-repressor/operator complex The contents of a crystal lattice unit

symmetry operations for the crystallographic space-group.

27 The crystallographic asymmetric units does not necessarily contain a functional molecule 1qpi.pdb Tet-repressor/operator complex The contents of a crystal lattice unit cell can be generated from the asymmetric unit by applying the required symmetry operations for the crystallographic space-group. But neither is this trivial for the non-crystallographer, nor is it obvious which of the symmetry replicates might make physiological contacts

28 ... Biological Unit PQS reasons automatically about how a monomer might be correctly completed to a functional biomolecular complex (and is often correct)

29 NCBI structure group MMDB - very well integrated but somewhat impenetrable

30 NDB urx035.pdb (Hammerhead Ribozyme)

31 PDBsum - and "secondary" structure databases

32 PDBsum - Information

33 Others Macromolecular Structure Database at EBI (Relibase, PQS...) Macromolecular structure related resources at the PDB Structure links at the Southwestern Biotechnology and Informatics Center Molecular Models from Chemistry Molecular Library many, many more

34 Concept 4: Knowledge of structure allows mechanistic explanations

35 Structure as an integrated map - Example questions Which part of my structure appears to be conserved? Are two functionally important residues possibly in contact? Where is Asn220 relative to the active site? May the mutation E123A possibly have something to do with protein stability? Is Leu234 on the surface, or in the core? I want to clone my protein into a yeast two-hybrid system: should I fuse the DNA binding domain to the N- or the C- terminus?

36 Geometric relationships Bonds Angles, plain and dihedral Surfaces Chemical potential, amino acid functions Static and dynamic disorder Structural similarity Electrostatics Conservation patterns (structural and functional) Quarternary structure Posttranslational modification sites Unexpected homology [...]

Distances from coordinates XYZ coordinates are vectors in an orthogonal coordinate system, in Å. All the rules of analytical geometry apply. [...] ATOM 687 OH TYR 86 7.415 62.584 32.900 1.00 3.37 [.

37 Distances from coordinates XYZ coordinates are vectors in an orthogonal coordinate system, in Å. All the rules of analytical geometry apply. [...] ATOM 687 OH TYR [...] ATOM 651 O ASP [...] d = [( ) 2 + ( ) 2 + ( ) 2 ] 0.5 = [(2.581) 2 + (-0.013) 2 + (-0.412) 2 ] 0.5 = [ ] 0.5 = [ ] 0.5 = Å = nm = m

38 Dihedral angles i i+1 i+3 i+2 +f Single bonds: Freely rotable, but constrained by steric overlap. Small energetic barrier, preference for staggered conformations. Double bonds: Constrained to planar geometry. Large energetic barrier to isomerization

y f w Backbone dihedral angles: Ramachandran plots

Due to steric overlap, not all combinations of (f,y)

Allowed and forbidden regions of (f,y) space are

39 y f w Backbone dihedral angles: Ramachandran plots Rotatable bonds in the backbone are named f,y and w. Due to steric overlap, not all combinations of (f,y) are allowed. Allowed and forbidden regions of (f,y) space are shown on the Ramachandran plot. Observed (f,y) values reflect the theoretical boundaries well

40 Sidechain rotamers c 3 c 2 c 1 Ponder & Richards (1987) J. Mol. Biol. 193, randomly chosen Phe-residues superimposed

41 H-bond patterns Example: TYR - Side Chain Donor OH can donate a single hydrogen (The OH-H bond is 1.00Å long and lies in the plane of CE1, CE2, CZ and OH forming an angle of 110 degrees with the CZ-OH bond.) Distribution of H-bond counts in all and buried residues, D-A distances, H-A distances and D-H-A angles intyr sidechains. Tyr-Thr sidechain H-bond: despite canonical geometry, correct topology may be ambiguous! McDonald & Thornton (1994) J. Mol. Biol. 238,

42 Molecular surface Chain "A" of 1AON.PDB - GroEL/ES complex Surface rendering of GroEL/ES complex (D. Goodsell)

43 Molecular surface Surface provides a visual metaphore, and a useful tool to map properties. But how can a molecular surface be defined? Obviously, the hard-sphere surface is chemically not very relevant. Van der Waals surface

44 Molecular surface r = 1.4Å Probe! Van der Waals surface

45 Molecular surface Contact surface Accessible surface "Accessible" Van der Waals surface "Buried" Reentrant surface

46 Calculating solvent accessible surfaces 1. Draw a sphere around each atom, with a radius of (VdW + solvent probe ). 2. Erase all overlapping sphere surfaces. 3. The remaining area is the accessible surface. r = 1.4Å C: 1.75 Å N: 1.55 O: 1.4Å H: 1.17Å

separation Problem: Efficiency Solution: Place points only once, translate as needed Problem: What is a good value for n?

47 Parameters and assumptions Problem: Analytical solution inefficient. Solution: Numerical solution with probe points Problem: Regular placement of n probe points Solution: Stochastic placement Problem: Stochastic placement quite irregular Solution: Enforce minimum separation Problem: Efficiency Solution: Place points only once, translate as needed Problem: What is a good value for n? Solution: Try different n, evaluate standard deviation Problem: Should n be constant per atom, or per area? Solution: dots/area - need to scale dots with r VdW Problem: Hydrogens - where to get united atom radii? Solution: Literature search. Problem: Reference areas for relative SAA needed Solution: Model explicitely, as tripeptides [...] u,v Œ [0,1] q = 2p u f = cos -1 (2v 1) SpherePointPicking.html Even a straightforward algorithm has it's hidden parameters and assumptions. Results are meaningful only in this context. Any comparison is problematic

Mapping properties on surfaces Properties of atoms (B-factors) Ensemble properties of residues (hydrophobicity, conservation) Geometry (local curvature) Fields

48 Mapping properties on surfaces Properties of atoms (B-factors) Ensemble properties of residues (hydrophobicity, conservation) Geometry (local curvature) Fields and potentials (isosurfaces, binding potential) AChE (1ACL.PDB) color coded by electrostatic potential with GRASP. ( columbia.edu/grasp/)

49 Concept 5: Structure is not arbitrary, but contains recurring units

50 Basic building blocks of structure: Eg. PROMOTIF - as used in PDBSUM But: classical descriptions of structural building blocks are as much based on idealized concepts of geometry as on observations of nature. An unbiased analysis may arrive at significantly different classifications!

Unbiased structure motifs: alignment with added value Motif alignments... Why are particular amino acids conserved? What is essential in a sequence?

51 Unbiased structure motifs: alignment with added value Motif alignments... Why are particular amino acids conserved? What is essential in a sequence? A structure motif consensus sequence, compiled from unrelated segments, averages out features of conservation that are only due to incomplete divergence (homology). A consensus sequence, taken from different structural contexts, averages out features of sequence that are due to specific functional (binding, catalysis) or non-local structural requirements (packing, interaction). What remains is information about sequence propensities of local structural elements

A schematikon motif example: complex loop 3.8 1icf_I_215_0_7 Support = 7 against nrdb distribution 3.6 3.4 3.2 3.

52 A schematikon motif example: complex loop 3.8 1icf_I_215_0_7 Support = 7 against nrdb distribution Motif: 1icf 215 Length: 7 Support: 7 Unique: 7 Rank: Position

A schematikon motif example: strand N-cap 3.8 1whi_0_35_0_4 Support = 23 against nrdb distribution 3.6 3.4 3.2 3.

53 A schematikon motif example: strand N-cap 3.8 1whi_0_35_0_4 Support = 23 against nrdb distribution Motif: 1whi 35 Length: 4 Support: 7 Unique: 7 Rank: Position

54 Concept 6: Domains are folding units, functional units, and units of inheritance

55 Domains are ubiquitous in proteins Large proteins are composed of compact, semi-independent units - domains. Reason: Modularity Folding efficiency 2MCP.PDB

56 Domains in proteins: Number of domains in 787 representative proteins used as the basis for the CATH database Jones S et al. (1998) Protein Science 7:

57 Domains in proteins: Non-random relationship between domain number and chain length in the 787 representative proteins used as the basis for the CATH database Jones S et al. (1998) Protein Science 7:

58 Domains in proteins: Domain size in the 787 representative proteins used as the basis for the CATH database Jones S et al. (1998) Protein Science 7:

59 There is no universal definition of "domains" Possible definitions are based on independently inherited (sub)sequences (sequence domain), modular protein functions (functional domain), folding unit or atomic contacts (structural domain). Domain: A part of structure that can fold irrespective of the presence of other parts of structure But: what is measured is commonly sequence, function, or structure - NOT FOLDING!

60 Further complications: Analogous structure, Domain insertions, Circular permutations, Domain swapping. Domain insertion 1A2J.PDB Protein disulfide isomerase 2TRX.PDB Thioredoxin

61 Further complications: Analogous structure, Domain insertions, Circular permutations, Domain swapping ERQ.PDB beta lactamase Circular permutation 1ALQ.PDB beta lactamase

62 Further complications: Analogous structure, Domain insertions, Circular permutations, Domain swapping. Domain swapping 11BG.PDB Bull seminal ribonuclease

63 Domains can be elusive: The separation of a structure into domains requires the arbitrary definition of thresholds in a continuum of possibilities

64 Why care? Function: evolution works on sequence, but selects function. Definition of domains in structure can uncover functional units that may evolve independently. Sequence searches, alignments etc. with domains are much more specific. Once structural domains have been defined, sequence profiles, HMMs or other computational procedures can be used to pick out more members of the domain family from the database. Domains can be defined from sequence patterns, or from the analyis of structure

65 Automated (objective) domain definition: - Sequence (CDD) CDD from Smart and Pfam CDART from CDD and Genbank

66 SemiAutomated consensus domain definition: - Structure (CATH) Dehydrolipoamide dehydrogenase 1LPFA: Jones S et al. (1998) Domain assignment for protein structures using a consensus approach: Chracterization and analysis. Protein Science 7:

67 SCOP & CATH: structural classification The eight most frequent SCOP Superfolds

68 CATH - Class Class1: Mainly Alpha Class 2: Mainly Beta Class 3: Mixed Alpha/Beta Class4: Few Secondary Structures

69 CATH - Architecture Roll Super Roll Barrel 2-Layer Sandwich

70 CATH - Topology L-fucose Isomerase Serine Protease Aconitase, domain 4 TIM Barrel

71 CATH - Homology Alanine racemase Dihydropteroate (DHP) synthetase FMN dependent fluorescent proteins 7-stranded glycosidases

72 CATH - Entry (Example)

73 IV: Open Issues I: Integration into processes, scriptable APIs II: Sequence based identification of domains III: Analysing domains in context IV: Defining modular domain functions

74 Bioinformaticians apparently do not like structure! Sequence: Discrete alphabet Easy to manipulate Well developed datastructures Well developed libraries Structure: Continuous space Linear algebra, complicated energy functions Databases and datastructures are difficult Paucity of libraries Meet the challenge!

75 Questions? Feedback?

Structural bioinformatics

Structural bioinformatics Why structures? The representation of the molecules in 3D is more informative New properties of the molecules are revealed, which can not be detected by sequences Eran Eyal Plant