Structural Biology: What does 3D tell us? Stephen J Everse University of Vermont

Outline Determining a 3D structure Structural elements X-ray crystallography Structural elements Modeling a 3D structure

Protein Structures Primary Secondary Tertiary Quaternary Amino acid sequence. Alpha helices & Beta sheets, Loops. Arrangement of secondary elements in 3D space. Packing of several polypeptide chains. Given an amino acid sequence, we are interested in its secondary structures, and how they are arranged in higher structures.

Secondary Structure a Helix First predicted by Linus Pauling. Modeled on basis x-ray data which provided accurate geometries, bond lengths, and angles. Modeled before Kendrew’s structure; 3.6 residues/ turn, 5.4Å/ turn; The main chain forms a central cylinder with R-groups projecting out; Variable lengths: from 4 to 40+ residues with the average helix length is 10 residues (3 turns).

Secondary Structure The b Sheet Unlike a helix, b sheet composed of secondary structure elements distant in structure; The b strands are located next to each other Hydrogen bonds can form between C=O groups of one strand and NH groups of an adjacent strand. Two different orientations all strands run same direction: “parallel” strands in alternating orientation: the “antiparallel”.

b-Turns Type I: Also referred to as a b turn: H-bond between Acyl O of AA1 and NH of AA4; Type II, glycine must occupy the AA3 position due to steric effects; Type III is equivalent to 310 helix; Types I & III constitute some 70% of all b turns; Proline is typically found in the second position, and most b turns have Asp, Asn, or Gly at the third position.

Other Secondary Structural Elements Random coil Loop -turn defined for 3 residues i, i+1, i+2 if a hydrogen bond exists between residues i and i+2 and the phi and psi angles of residue i+1 fall within 40 degrees of one of the following 2 classes turn type phi(i+1) psi(i+1) classic 75.0 -64.0 inverse -79.0 69.0 Disordered structure

Viewing Structures Ball-and-stick CPK Ca or CA It’s often as important to decide what to omit as it is to decide what to include What you omit depends on what you want to emphasize

Ribbon and Topology Diagrams Representations of Secondary Structures a-helix b-strand N

Tools for Viewing Structures Jmol http://jmol.sourceforge.net PyMOL http://pymol.sourceforge.net Swiss PDB viewer http://www.expasy.ch/spdbv Mage/KiNG http://kinemage.biochem.duke.edu/software/mage.php http://kinemage.biochem.duke.edu/software/king.php Rasmol http://www.umass.edu/microbio/rasmol/

RCSB http://www.rcsb.org/

GRASP Graphical Representation and Analysis of Structural Properties Red = negative surface charge Blue = positive surface charge

Consurf The ConSurf server enables the identification of functionally important regions on the surface of a protein or domain, of known three-dimensional (3D) structure, based on the phylogenetic relations between its close sequence homologues; A multiple sequence alignment (MSA) is used to build a phylogenetic tree consistent with the MSA and calculates conservation scores with either an empirical Bayesian or the Maximum Likelihood method. http://consurf.tau.ac.il/

How do we show 3-D? Stereo pairs Dynamics: rotation of flat image Rely on the way the brain processes left- and right-eye images If we allow our eyes to go slightly wall-eyed or crossed, the image appears three-dimensional Dynamics: rotation of flat image Perspective

Stereo pair: Release factor 2/3 Klaholz et al, Nature (2004) 427:862

Movies http://pymol.org

Proteopedia

Protein structures in the PDB The last 15 years have witnessed an explosion in the number of known protein structures. How do we make sense of all this information? blue bars: yearly total red bars: cumulative total

Classification of Protein Structures The explosion of protein structures has led to the development of hierarchical systems for comparing and classifying them. Effective protein classification systems allow us to address several fundamental and important questions: If two proteins have similar structures, are they related by common ancestry, or did they converge on a common theme from two different starting points? How likely is that two proteins with similar structures have the same function? Put another way, if I have experimental knowledge of, or can somehow predict, a protein’s structure, I can fit into known classification systems. How much do I then know about that protein? Do I know what other proteins it is homologous to? Do I know what its function is?

Definition of Domain “A polypeptide or part of a polypeptide chain that can independently fold into a stable tertiary structure...” from Introduction to Protein Structure, by Branden & Tooze “Compact units within the folding pattern of a single chain that look as if they should have independent stability.” from Introduction to Protein Architecture, by Lesk Thus, domains: can be built from structural motifs; independently folding elements; functional units; separable by proteases. note that Lesk’s definition is more careful... sometimes domains within multidomain proteins can evolve to have dependent stabilities, even when the same types of units occur independently in other proteins. Two domains of a bifunctional enzyme

Proteins Can Be Made From One or More Domains Proteins often have a modular organization Single polypeptide chain may be divisible into smaller independent units of tertiary structure called domains Domains are the fundamental units of structure classification Different domains in a protein are also often associated with different functions carried out by the protein, though some functions occur at the interface between domains domain organization of P53 tumor suppressor 1 60 100 300 324 355 363 393 activation domain sequence-specific DNA binding domain non-specific DNA-binding domain tetramer- ization domain

Rates of Change Why? Not all proteins change at the same rate; Functional pressures Surface residues are observed to change most frequently; Interior less frequently;

SequenceStructureFunction Many sequences can give same structure Side chain pattern more important than sequence When homology is high (>50%), likely to have same structure and function (Structural Genomics) Cores conserved Surfaces and loops more variable *3-D shape more conserved than sequence* *There are a limited number of structural frameworks* W. Chazin © 2003

Degree of Evolutionary Conservation Less conserved Information poor More conserved Information rich DNA seq Protein seq Structure Function ACAGTTACAC CGGCTATGTA CTATACTTTG HDSFKLPVMS KFDWEMFKPC GKFLDSGKLG S. Lovell © 2002

How is a 3D structure determined ? 1. Experimental methods (Best approach): X-rays crystallography - stable fold, good quality crystals. NMR - stable fold, not suitable for large molecule. 2. In-silico methods (partial solutions - based on similarity): Sequence or profile alignment - uses similar sequences, limited use of 3D information. Threading - needs 3D structure, combinatorial complexity. Ab-initio structure prediction - not always successful.

Experimental Determination of Atomic Resolution Structures X-ray X-rays Diffraction Pattern Direct detection of atom positions Crystals NMR RF Resonance H0 Indirect detection of H-H distances In solution

• • Resolving Power d Resolving Power: Signal Position Resolving Power: The ability to see two points that are separated by a given distance as distinct Resolution of two points separated by a distance d requires radiation with a wavelength on the order of d or shorter: wavelength Mark Rould © 2007

X-ray Microscopes? nair nair nglass ∆ There are no lenses for xrays. •Lenses require a difference in refractive index between the air and lens material in order to 'bend' and redirect light (or any other form of electromagnetic radiation.) •The refractive index for x-rays is almost exactly 1.00 for all materials. ∆ There are no lenses for xrays. How do lenses work? We'll see how we deal with this minor difficulty in a few slides. There's another problem with trying to obtain atomic resolution images of specimens with x-rays... Mark Rould © 2007

Fourier Transform of specimen Light Scattering and Lenses are Described by Fourier Transforms Scattering = Fourier Transform of specimen Lens applies a second Fourier Transform to the scattered rays to give the image There are no lenses for xrays. The mathematical equivalent of a Lens = Fourier Transform. Scattering = Fourier Transform of specimen Lens applies a second Fourier Transform to the scattered rays to give the image Since X-rays cannot be focused by lenses and refractive index of X-rays in all materials is very close to 1.0 how do we get an atomic image? Mark Rould © 2007

X-ray Diffraction with “The Fourier Duck” The molecule The diffraction pattern Images by Kevin Cowtan http://www.yorvic.york.ac.uk/~cowtan

Animal Magic The CAT (molecule) The diffraction pattern Images by Kevin Cowtan http://www.yorvic.york.ac.uk/~cowtan

Solution: Measure Scattered Rays, Use Fourier Transform to Mimic Lens Transforms X-Ray Detector Computer Instead of using a lens to recombine the scattered ray, we capture the scattered x-rays with an x-ray detector. To get an image of the specimen, just take the inverse Fourier transform of the diffraction data, right? No -- we only have half the data that are needed to mimic a lens! Mark Rould © 2007

A Problem… A single molecule is a very weak scatterer of X-rays. Most of the X-rays will pass through the molecule without being diffracted. Those rays which are diffracted are too weak to be detected. Solution: Analyzing diffraction from crystals instead of single molecules. A crystal is made of a three-dimensional repeat of ordered molecules (1014) whose signals reinforce each other. The resulting diffracted rays are strong enough to be detected. 3D repeating lattice; Unit cell is the smallest unit of the lattice; Come in all shapes and sizes. Crystals come from slowly precipitating the biological molecule out of solution under conditions that will not damage or denature it (sometimes). A Crystal Sylvie Doublié © 2000

Putting it all together: X-ray diffraction Electron density map Rubisco diffraction pattern Crystallographer Detector Computer Scattered rays Object X-rays Diffraction pattern is a collection of diffraction spots (reflections) Model Sylvie Doublié © 2000

What information does structure give you? 3-D view of macromolecules at near atomic resolution. The result of a successful structural project is a “structure” or model of the macromolecule in the crystal. You can assign: - secondary structure elements - position and conformation of side chains - position of ligands, inhibitors, metals etc. A model allows you: - to understand biochemical and genetic data (i.e., structural basis of functional changes in mutant or modified macromolecule). - generate hypotheses regarding the roles of particular residues or domains Sylvie Doublié © 2000

What did I just say????!!! A structure is a “MODEL”!! What does that mean? It is someone’s interpretation of the primary data!!!

So what happens when we can’t get an NMR or X-ray structure?

2˚ & 3˚ Structure Prediction 38 38

Secondary (2o) Structure Table 10 -

Secondary Structure Prediction One of the first fields to emerge in bioinformatics (~1967) Grew from a simple observation that certain amino acids or combinations of amino acids seemed to prefer to be in certain secondary structures Subject of hundreds of papers and dozens of books, many methods…

Simplified C-F Algorithm Select a window of 7 residues Calculate average Pa over this window and assign that value to the central residue Repeat the calculation for Pb and Pc Slide the window down one residue and repeat until sequence is complete Analyze resulting “plot” and assign secondary structure (H, B, C) for each residue to highest value

Protein Principles Proteins reflect millions of years of evolution. Most proteins belong to large evolutionary families. 3D structure is better conserved than sequence during evolution. Similarities between sequences or between structures may reveal information about shared biological functions of a protein family.

The PhD Algorithm Search the SWISS-PROT database and select high scoring homologues Create a sequence “profile” from the resulting multiple alignment Include global sequence info in the profile Input the profile into a trained two-layer neural network to predict the structure and to “clean-up” the prediction http://www.predictprotein.org/

Best of the Best PredictProtein-PHD (72%) http://www.predictprotein.org/ Jpred (73-75%) http://www.compbio.dundee.ac.uk/www-jpred/index.html SAM-T08 (75%) http://compbio.soe.ucsc.edu/SAM_T08/T08-query.html PSIpred (77%) http://bioinf.cs.ucl.ac.uk/psipred/psiform.html

Structure Prediction T Threading A protein fold recognition technique that involves incrementally replacing the sequence of a known protein structure with a query sequence of unknown structure. Why threading? Secondary structure is more conserved than primary structure Tertiary structure is more conserved than secondary structure T H R E A D 45

3D Threading Servers Generate 3D models or coordinates of possible models based on input sequence PredictProtein-PHDacc http://www.predictprotein.org PredAcc http://mobyle.rpbs.univ-paris-diderot.fr/cgi- bin/portal.py?form=PredAcc Loopp (version 2) http://cbsuapps.tc.cornell.edu/loopp.aspx Phyre http://www.sbg.bio.ic.ac.uk/~phyre/ SwissModel http://swissmodel.expasy.org/ All require email addresses since the process may take hours to complete

Ab Initio Folding Two Central Problems Sampling conformational space (10100) The energy minimum problem The Sampling Problem (Solutions) Lattice models, off-lattice models, simplified chain methods, parallelism The Energy Problem (Solutions) Threading energies, packing assessment, topology assessment

Lattice Folding

http://predictioncenter.org/ Critical Assessment of protein Structure Prediction (CASP)

http://folding.stanford.edu/

For the gamers out there… http://fold.it/portal/

Print & Online Resources Crystallography Made Crystal Clear, by Gale Rhodes http://www.usm.maine.edu/~rhodes/CMCC/index.html http://ruppweb.dyndns.org/Xray/101index.html Online tutorial with interactive applets and quizzes. http://www.ysbl.york.ac.uk/~cowtan/fourier/fourier.html Nice pictures demonstrating Fourier transforms http://ucxray.berkeley.edu/~jamesh/movies/ Cool movies demonstrating key points about diffraction, resolution, data quality, and refinement. http://www-structmed.cimr.cam.ac.uk/course.html Notes from a macromolecular crystallography course taught in Cambridge

Evolutionarily Conserved Domains Often certain structural themes (domains) repeat themselves, but not always in proteins that have similar biological functions. This phenomenon of repeating structures is consistent with the notion that the proteins are genetically related, and that they arose from one another or from a common ancestor. In looking at the amino acid sequences, sometimes there are obvious homologies, and you could predict that the 3-D structures would be similar. But sometimes virtually identical 3-D structures have no sequence similarities at all!

The Motif There are certain favored arrangements of multiple secondary structure elements that recur again and again in proteins--these are known as motifs or supersecondary structures A motif is usually smaller than a domain but can encompass an entire domain. Sometimes the structures of domains are partly named after motifs that they contain, e.g. “greek key beta barrel” It should be noted that the term motif, when used in conjunction with proteins, sometimes also refers to sequence features with an associated function, e.g. the “copper binding motif” HXXXXH. motif is a fairly broad concept a motif is usually part of a domain and does not fold on its own: for instance, some domains are composed of repeating beta-alpha-beta units. but sometimes a motif is big enough to fold on its own and encompasses a whole domain--a jellyroll barrel might be an example of that. when using the word motif to describe proteins, it sometimes has a functional rather than a structural meaning, like a phosphate-binding motif. here, though, we are speaking in a purely structural sense. “greek key” motif beta-alpha-beta motif

Limitations of Chou-Fasman Does not take into account long range information (>3 residues away) Does not take into account sequence content or probable structure class Assumes simple additive probability (not true in nature) Does not include related sequences or alignments in prediction process Only about 55% accurate (on good days)

Prediction Performance

An Approach SAS Calculations DSSP - Database of Secondary Structures for Proteins http://swift.cmbi.ru.nl/gv/start/index.html VADAR - Volume Area Dihedral Angle Reporter http://redpoll.pharmacy.ualberta.ca/vadar/ GetArea http://curie.utmb.edu/getarea.html Naccess - Atomic Solvent Accessible Area Calculations http://www.bioinf.msnchester.ac.uk/naccess