1. PREDICTING PROTEIN STRUCTURE AND BEYOND …. P. V. Balaji Biotechnology Center I.I.T., Bombay

2. Organization of the talk 1. Why predict the structure? 2. Methods for structure prediction 3. What next?

3. Genome Size is not Proportional to the Complexity of the Organism Size of the Genome Complexity

4. Molecular Logic of Life is Same Biochemically, all things living – animals, plants, bacteria, viruses, etc. – are remarkably similar English  26-Letter alphabet  Only one grammar  Extremely diverse literature Genome  4-Letter alphabet  Only one grammar  Extremely diverse organisms

5. Genome Sequencing and Analysis: One of the Key Steps in Deciphering the Logic of Life Even minute details have to be analyzed Hang him Hang him, not let him go Hang him not Hang him not, let him go Ac Humans: NeuNAc Gc Chimpanzees: NeuNGc –CH 3 –CH 2 OH

6. Innovations in Technology Have Made Genome Sequencing a Routine Affair Genome sequencing Completed: ~70 organisms In the pipeline: Several more “ … it is unlikely that the base sequence of more than a few percent of such a complex DNA will ever be determined …” C W Schmid & W R Jelinek, Science, June 1982

7. One Aspect of Genome Sequence Analysis is to Assign Functions to Proteins (Reverse Genetics) Proteins are workhorses of the cell Are involved in every aspect of living systems

8. Function of a Protein can be Defined at Different Levels Example: Lysozyme Biochemical level: Hydrolyzes C—O bond Physiological level: Breaks down the cell wall Cellular level: Defense against infection Different Analysis Tools Provide Functions at Different Levels

9. Hallmark of Proteins: Specificity Know exactly which small molecule (ligand) they should bind to or interact with Also know which part of a macromolecule they should bind to

10. Origin of Specificity 1ruv.pdb Function is critically dependent on structure

11. Structure Structure – Key to Dissect Function Interaction Interfaces Crystal Packing Functional Oligomerization Location of Mutants Conserved Residues SNPs Evolutionary Relationships Fold Relative Juxtaposition Catalytic Clusters Motifs Catalytic Mechanism Clefts (active sites) Antigenic Sites, surface patches Surface Shape & Charge Dynamics (breathing)

12. Christian B. Anfinsen: Nobel Prize in Chemistry (1972) 1 KETAAAKFERQHMDSSTSAASSSNYCNQMMKSRNLTKDRCKPVNTFVHES LADVQAVCSQKNVACKNGQTNCYQSYSTMSITDCRETGSSKYPNCAYKTT QANKHIIVACEGNPYVPVHFDASV 124 Sequence Determines Structure 1ruv.pdb

13. Sequence Structure Function How Does Sequence Specify Structure? Structure has to be determined experimentally The Protein Folding Problem (second half of the genetic code) ? Functional Genomics

14. Experimental Methods of Structure Determination Provides a static picture X-ray crystallography Obtaining crystals that diffract Solubilization of the over-expressed protein Nuclear Magnetic Resonance spectroscopy Provides a Dynamic picture Size-limit is a major factor Solubilization of the over-expressed protein

15. Annotated proteins in the databank: ~ 100,000 Limitations of Experimental Methods: Consequences Proteins with known structure: ~5,000 ! Total number including ORFs: ~ 700,000 ORF, or Open Reading Frame, is a region of genome that codes for a protein Have been identified by whole genome sequencing efforts ORFs with no known function are termed orphan Dataset for analysis

16. Structural Biology Consortia: Brute Force Approach Towards Structure Elucidation Employ battalions of Ph.Ds & Post-doctorals Aim to solve about 400 structures a year Large-scale expression & crystallization attempts + – Basic strategies remain the same No (known) new tricks * Enhances the statistical base for inferring sequence – structure relationships “Unrelenting” ones will be ignored

17. ? KQFTKCELSQNLYDIDGYGRIALPELICTMF HTSGYDTQAIVENDESTEYGLFQISNALWCK SSQSPQSRNICDITCDKFLDDDITDDIMCAK KILDIKGIDYWIAHKALCTEKLEQWLCEKE Predicting Protein Structure: 1. Comparative Modeling (formerly, homology modeling) Use as template & model 8lyz 1alc KVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAK FESNFNTQATNRNTDGSTDYGILQINSRWWCND GRTPGSRNLCNIPCSALLSSDITASVNCAKKIV SDGNGMNAWVAWRNRCKGTDVQAWIRGCRL Share Similar Sequence Homologous

18. Structure is much more conserved than sequence during evolution Comparative Modeling Basis* Higher the similarity, higher is the confidence in the modeled structure * Limited applicability A large number of proteins and ORFs have no similarity to proteins with known structure *

19. Predicting Protein Structure: Alternative Methods Threading or Fold Recognition Both these methods depend heavily on the analysis of known protein structures* Ab initio In addition, establishing sequence  structure relationship is also important * Input from people trained in statistics, pattern recognition and related areas of computer science is very critical *

20. Statistical Analysis of Protein Structures: Microenvironment Characterization Atom based properties Residue based properties Chemical group Secondary structure Other properties Type, Hydrophobicity, Charge Type, Hydrophobicity Hydroxyl, Amide, Carbonyl, etc.  -Helix,  -Strand, Turn, Loop VDW volume, B-factor, Mobility, Solvent accessibility Describe structures at multiple levels of detail using a comprehensive set of properties

21. Predicting Protein Structure: 2. Threading or Fold Recognition Basis It is estimated there are only around 1000 to 10 000 stable folds in nature* Irrespective of the amino acid sequence, a protein has to adopt one of these folds* Fold recognition is essentially finding the best fit of a sequence to a set of candidate folds * Select the best sequence-fold alignment using a fitness scoring function* NP-complete problem*

22. Fold of a Protein Refers to the spatial arrangement of its secondary structural elements (  -helices and  -strands) 1l45.pdb4bcl.pdb1mbl.pdb  /  -barrel  -barrel  /  -sandwich

23. Threading: Basic Strategy Sequence Template Spatial Interactions dhgakdflsdfjaslfkjsdlfjsdfjasd Library of folds Query Scoring & selection

24. Predicting Protein Structure: 3. Ab Initio Methods Sequence Secondary structure Prediction Tertiary structure Low energy structures Predicted structure Energy Minimization Validation Mean field potentials

25. Predicting the structure of such proteins is an entirely different challenge 1a6g.pdb Small molecules and/or metal ions are an integral part of certain proteins

26. Proof of the Pudding: CASP Meetings Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction – 4 Predictions; not Post-dictions Easy and medium targets: ~100% success Hard targets: ~50% success Significant increase from CASP3

27. OK, I can predict the structure correctly! is that it? Strict structure – function correlation exists only for a subset of proteins Some folds (ferredoxin, TIM barrel, …) are very popular – several protein families, with diverse functions, adopt these folds Well, no!! Detailed biochemical characterization is required Despite high similarity in sequence and structure, may act on different substrates (hence different functions) – due to subtle changes in active site (  1  3-GalT and  1  3-GlcNAcT)

28. Similar structure, mutually exclusive function: Lysozyme &  -lactalbumin Inferring Function from Structure: Caveats Same function, completely different structures: Carbonic anhydrases from M. thermophila and mouse 8lyz.pdb, 1alc.pdb 1thj.pdb 1dmx.pdb “Moonlighting” proteins – one structure(?), multiple functions Glyceraldehyde 3-phosphate dehydrogenase Glycolysis Binding protein for plasmin, fibronectin and lysozyme Transcriptional control of gene expression, DNA replication and repair Flocculation Gal1p – Kinase as well as regulator of Gal-gene expression Gal3p – 70% similar; does not have kinase activity

29. Same fold, different oligomerization DimerizationTetramerization ConA PNA PNA, GSIV

30. Ligand Induced Conformational Changes are Quite Common Binding of first substrate redefines the active site and creates the binding pocket for the second substrate and the metal ion Flexible loop Before After

31. Take Home Message Predicting Protein Structure is a key component of genome sequence analysis Structure is a very important link in deciphering the function New tools are required? Or larger training dataset is required?

32. Acknowledgement Organizers for giving me this opportunity Sujatha and Jayadeva Bhat for helping me put together this talk http://guitar.rockefeller.edu/modeller/modeller.html Few Useful Links http://www.biochem.ucl.ac.uk/bsm/cath-new/index.html http://predictioncenter.llnl.gov/ http://insulin.brunel.ac.uk