1. Protein Structure and Prediction Michael Strong, Ph.D. Integrated Center for Genes, Environment, and Health National Jewish Health

2. Why do we care about protein structures Structure leads to Function – Enzymes, Structural (Cell Wall), Protein Interactions (host-pathogen), Replication, Transcription, Translation (Binds to receptors on a cell surface, attached carbohydrades help it evade immune system) Shapes the virus, enables budding Protects the RNA viral genome Ebola Virus -RNA genome -Encodes 7 proteins To make new copies of RNA genome

3. Why do we care about protein structures Combining Structure and Genomic Information - Help us understand phylogeny and implications of mutations Science 12 September 2014: Vol. 345 no. 6202 pp. 1369-1372

4. Why do we care about protein structures Combining Structure and Genomic Information - Help us understand phylogeny and implications of mutations Drug Resistance Rifampin Tuberculosis rpoB drug target

5. Why do we care about protein structures Combining Structure and Genomic Information - Help us understand phylogeny and implications of mutations Homology model of CFTR structure, with common mutation F508. PNAS 3256–3261, 105:9

6. Experimental Approach PQITLWKRPLVTIRIGGQL KEALLDTGADDTVLEEM NLPGKWKPKMIGGIGGF IKVRQYDQIPIEICGHKAI GTVLVGPT PVNIIGRNLLTQIGCTLNF From Sequence to Structure HIV Protease With Inhibitor X-ray Crystallography NMR Cryo-Electron Microscopy

7. Computational Approach From Sequence to Structure MNPNQKIITIGSVCMTIGMANLILQIG NIISIWISHSIQLGNQN QIETCNQSVITYENNTWVNQTYVNISN TNFAAGQSVVSVKLAGNSSLCPVSGW AIYSK DNSVRIGSKGDVFVIREPFISCSPLECRT FFLTQGALLNDKHSNGTIKDRSPYRTL MS CPIGEVPSPYNSRFESVAWSASACHDGI NWLTIGISGPDNGAVAVLKYNGIITDTI KS WRNNILRTQESECACVNGSCFTVMTD GPSNGQASYKIFRIEKGKIVKSVEMNAP NYHY EECSCYPDSSEITCVCRDNWHGSNRP WVSFNQNLEYQIGYICSGIFGDNPRPN DKTGS CGPVSSNGANGVKGFSFKYGNGVWIG RTKSISSRNGFEMIWDPNGWTGTDN NFSIKQD IVGINEWSGYSGSFVQHPELTGLDCIRP CFWVELIRGRPKENTIWTSGSSISFCGV NS DTVGWSWPDGAELPFTIDK" H1N1 NA Homology Modeling Protein Threading Ab initio

8. Protein Building Blocks Typical Protein Sequence MNPNQKIITIGSVCMTIGMANLILQIGNIISIWISHSIQLGNQN

9. Protein Building Blocks

10. Amino Acid Side Chain (R groups)

11. Disulfide Bonds

12. Amino Acid Side Chain (R groups) - acidic + basic

13. DNA Folded Protein Most Proteins Spontaneously Fold RNA Some proteins need chaperones for correct folding Transcribed by RNA polymerase Translated by Ribosome

14. native state, Folded protein spontaneous self-organisation (~1 second) Most Proteins Spontaneously Fold Folded protein Unfolded protein Denaturing conditions Native conditions Christian Anfinsen’s Experiment 1950s

15. Most Proteins Spontaneously Fold Important to Computational Biologists, because this suggests that all information relating to the correct folding of a protein is contained in it’s primary amino acid sequence, but …

16. Most Proteins Spontaneously Fold But Proteins lack easy rules for folding as compared to DNA Protein DNA

17. Many Factors Influence Protein Folding Protein Proteins Assume the Lowest Energy Structure Factors that influence folding include: 1.Hydrophobic Interactions / collapse (particularly within the core) 2.Hydrogen bonds – lead to secondary structures 3.Disulfide Bonds (Cysteine residues) 4.Salt Bridges / Ionic Interactions (among charged residues) 5.Multimeric interactions with same type or other proteins

18. Common Secondary Structures Alpha helix

19. Common Secondary Structures Beta Sheet

20. Common Secondary Structures Loop Regions Loop Strong M et al, Proc National Academy of Sciences vol. 103 no. 21, 8060–8065, 2006

21. Example - Hemoglobin

22. Fluoroquinolone Target gyrA Crystal Structure Rifampin target rpoB Homology Model Diversity of Protein Structures Streptomycin resistance gidB Homology model Isoniazid Target inhA Crystal Structure Ethionamide Target, inhA Crystal Structure Isoniazid Activating Enzyme, KatG Crystal Structure Streptomycin Resistance rpsL Homology model Pyrazinamide Activating enzyme pncA Crystal Structure ABCD E FGH

23. Experimental Methods of Structure Determination X-ray crystallography High resolution structure determination Grow a protein Crystal

24. Experimental Methods of Structure Determination X-ray crystallography High resolution structure determination

25. Experimental Methods of Structure Determination X-ray crystallography High resolution structure determination Intensities and phases of all reflections are combined in a Fourier transform to provide maps of electron density Phases determined by using heavy metals or selenomethionine (MAD)

26. Smaller Proteins than X-ray Distances between pairs of hydrogen atoms Lots of information about dynamics Requires soluble, non-aggregating material Assignment sometimes difficult Experimental Methods of Structure Determination NMR – Nuclear Magnetic Resonance High resolution structure determination NOE cross-peak if they are within 5.0 Å

27. Low to medium resolution ~10-15Å Limited information about dynamics Can be used for very large molecules and complexes Experimental Methods of Structure Determination Cryo Electron Microscopy Low to medium resolution structure determination

28. Database of Protein Structures PDB – Protein Data Bank

29. Database of Protein Structures PDB – Protein Data Bank 104,125 structures as of 10/20/2014

30. Database of Protein Structures PDB – Protein Data Bank Even so, the number of solved structures greatly lags behind the rate of new genes being sequenced … Solution: Computational Structural Methods

31. GenBank Sequences

32. Atoms in pdb files are defined by their Cartesian coordinates: Database of Protein Structures PDB – Protein Data Bank Files

33. Visualization of PDB files Pymol, Jmol, Chimera, etc

34. Visualization of PDB files Pymol, Jmol, Chimera, etc

35. DALI Structural Alignments Align Protein Structures, Structure Superposition Generates a comparison matrix (transform protein into a 2D array of distances between C-alpha atoms. Z score reflects reliability, lowest RMSD identified

36. Computational Approach From Sequence to Structure MNPNQKIITIGSVCMTIGMANLILQIG NIISIWISHSIQLGNQN QIETCNQSVITYENNTWVNQTYVNISN TNFAAGQSVVSVKLAGNSSLCPVSGW AIYSK DNSVRIGSKGDVFVIREPFISCSPLECRT FFLTQGALLNDKHSNGTIKDRSPYRTL MS CPIGEVPSPYNSRFESVAWSASACHDGI NWLTIGISGPDNGAVAVLKYNGIITDTI KS WRNNILRTQESECACVNGSCFTVMTD GPSNGQASYKIFRIEKGKIVKSVEMNAP NYHY EECSCYPDSSEITCVCRDNWHGSNRP WVSFNQNLEYQIGYICSGIFGDNPRPN DKTGS CGPVSSNGANGVKGFSFKYGNGVWIG RTKSISSRNGFEMIWDPNGWTGTDN NFSIKQD IVGINEWSGYSGSFVQHPELTGLDCIRP CFWVELIRGRPKENTIWTSGSSISFCGV NS DTVGWSWPDGAELPFTIDK" H1N1 NA Secondary Structure Prediction Alpha Helix, Beta Strand, or Other Tertiary Predictions: 1.Homology Modeling 2.Fold Recognition 3.De Novo Protein Structure Prediction

37. Secondary Structure Prediction -probability of amino acid to be in a alpha helix, Beta strand, or other (coil/loop) based on known structures. -Chou-Fasman (short runs of amino acids), GOR (Bayesian, takes neighbors into account) - helices – no prolines, periodicity 3.6 residues/turn - strands – alternating hydropathy, or ends hydrophillic and center hydrophobic -other – small, polar, flexible residues, and prolines But, stalled at 55- 60% accuracy Also used position specific profiles based on multiple sequence alignments (evolutionary information) (ie insertion/deletion more likely to be in coil/turn), PSI BLAST and HMM, NN and SVM (improved to about 75-80%)

38. Secondary Structure Prediction

39. But we really want to know how the protein folds in three dimensions

40. CASP - Critical Assessment of Techniques for Protein Structure Prediction Started in 1994, Helped push the field of structure prediction “Contest-like” setup Catagories include: Homology Modeling / Comparative Modeling Fold Recognition / Threading Ab Initio, De novo Partially vs. Automated Methods (now quite similar results) Goal: Predict structures of solved but unpublished/unreleased structures (used to evaluate predictions. Every year, predictions / algorithms get better

41. Comparative Modeling “Homology Modeling” Proteins that have similar sequences (i.e., related by evolution) are likely to have similar three-dimensional structures 1. BLAST sequence of Interest against PDB to identify a template Multiple templates can be used if desired Templates with Ligands bound can be used to identify binding sites and interacting residues in the homology model Sequence identity required depends on protein length. A good rule of thumb is to have at least 40% sequence identity. Higher sequence identity is best. Lower than 25% is not reliable (zone of uncertainty) Above 75% sequence identity, usually quite reliable homology model Accurate sequence alignments very important Programs include Modeller and Swiss Model

42. Comparative Modeling “Homology Modeling” Steps include: 1.Template recognition and initial alignment 2.Alignment Correction (Multiple Sequence Alignment can Help) 3.Backbone Generation (transfer coordinates from template) 4.Loop Modeling (loops hard to predict with insertions) 5.Side Chain Modeling (usually similar tortion angles at high sequenc ID) 6.Model Optimization (minor energy minimization steps or restrain some atom positions) 7.Model Validation (Higher ID more accurate usually, Calculate energy, or normality index (bond length, tortion angles)) 8.Iteration (to refine)

43. Protein Threading § Generalization of homology modeling method Homology Modeling: Align sequence to sequence Threading: Align sequence to structure (templates) For each alignment, the probability that that each amino acid residue would occur in such an environment is calculated based on observed preferences in determined structures. § Rationale: Limited number of basic folds found in nature Amino acid preferences for different structural environments provides sufficient information to choose the best-fitting protein fold (structure) Protein Threading, Fold Recognition Often, seemingly unrelated proteins adopt similar folds. -Divergent evolution, convergent evolution. For sequences with low or no sequence homology

44. Fold recognition The number of possible protein structures/folds is limited (large number of sequences but relatively few folds (some estimate ~1000)) (most apparent when 50% of structures with no seq homology were solved and had folds similar to known structures) 90% of new structures deposited in PDB have similar folds to those already known Proteins that do not have similar sequences sometimes have similar three- dimensional structures (such as B-barrel TIM fold) A sequence whose structure is not known is fitted directly (or “threaded”) onto a known structure and the “goodness of fit” is evaluated using a discriminatory function Need ways to move model closer to the native structure 3.6 Å 5% ID NK-lysin (1nkl) Bacteriocin T102/as48 (1e68)

45. Ab initio prediction of protein structure – concept Difficult because search space is huge. Much larger conformational space Goal: Predict Structure only given its amino acid sequence In theory: Lowest Energy Conformation Go from sequence to structure by sampling the conformational space in a reasonable manner and select a native-like conformation using a good discrimination function Difficult for sequences larger that 150aa Rosetta (David Baker lab) one of best (CASP evaluation)

46. Rosetta structure prediction 2 phases 1.Low-resolution phase – statistical scoring function and fragment assembly A. local structure conformations using info from PDB (3 and 9mer stretches) B. multiple fragment substitution simulated annealing – to find best arrangement of the fragments (Monte Carlo Search) C. low resolution ensemble of decoy conformations 2. Atomic refinement phase using rotamers and small backbone angle moves (in populated regions of Ramachandran plot) A. Refinement B. Then structures clustered based on RMSD C. Center of the Largest Clusters chosen as representative folds (likely to be correct fold)

47. Quality Assessment Ramachandran Plot – Phi Psi angles To identify residues that may be in wrong conformation Procheck, What_check