1. Genomics and Personalized Care in Health Systems Lecture 9 RNA and Protein Structure Leming Zhou, PhD School of Health and Rehabilitation Sciences Department of Health Information Management

2. Outline RNA structure Protein structure Pharmacogenomics

3. Department of Health Information Management Two Types of Genes Protein coding genes –Common patterns: promoter region, start codon, codons, stop codon –Translated to protein sequence RNA genes –No consistent patterns common to all RNA genes –Not translated to proteins –Functional as RNA molecules

4. Department of Health Information Management Types of RNA mRNA: messager RNA tRNA: transfer RNA for providing codons and amino acids rRNA: ribosomal RNA for protein translation miRNA: MicroRNAs are small (22 nucleotides) non- coding RNA gene products that seem to regulate translation snRNAs: small nuclear RNAs –Spliceosomal RNAs found in spliceosome which is involved in splicing –Small nucleolar RNA located in the nucleolus

5. Department of Health Information Management RNA Genes RNA has various functions There are software developed to search for RNA genes in the genome. –tRNAscan searched for tRNA

6. Department of Health Information Management RNA Databases Ribosomal RNA database –Ribosomal Database Project: http://rdp.cme.msu.edu/http://rdp.cme.msu.edu/ tRNA Databases –Genomic tRNA Database: http://gtrnadb.ucsc.edu/http://gtrnadb.ucsc.edu/ snoRNA Databases –Yeast snoRNA Database: http://people.biochem.umass.edu/fournierlab/snornadb/main.php

7. Department of Health Information Management Secondary and Tertiary Structure RNA sequence  RNA structure –folding and pairing of bases within the sequence Canonical pairing: G-C and A-U –G-C pairing give more energetic stability (3 bonds) Non-canonical pairing: G-U (very common), A-C, A-G, etc. Double stranded regions and loop regions are the secondary structure elements Tertiary structure is the interaction between secondary structure elements

8. Department of Health Information Management RNA Secondary Structure For RNAs, secondary structures are conserved, but primary sequences are not necessarily conserved http://rnajournal.cshlp.org/content/10/10/1541/F1.expansion

9. Department of Health Information Management RNA Structure Prediction Methods Sequence and base pairing patterns Energy minimization –Find the energetically most stable structure –Energy calculations based on base pairings –All possible structures are sampled using the Monte Carlo method –Zuker and Stiegler (1981) used dynamic programming and energy rules to get the energetically most favorable structure. –Mfold is software developed by Zuker and co-workers. It is very computationally expensive and can be used on a maximum of about 1000 nucleotides.

10. Department of Health Information Management Exercises Use mfold to predict the secondary structure of a RNA sequence GTTTCCGTAGTGTAGTGGTTATCACGTTCGCCTCACACGCGAAAGG TCCCCGGTTCGAAACCGGGCGGAAACA http://mfold.rna.albany.edu/?q=mfold

14. Protein Structure

15. Department of Health Information Management Four Levels of Protein Structure Primary Structure – Sequence of amino acids Secondary Structure – Local Structure such as alpha-helices and beta-sheets Tertiary Structure – Arrangement of the secondary structural elements to give 3D structure of a protein Quaternary Structure – Arrangement of the subunits to give a protein complex its 3D structure

16. Department of Health Information Management Protein Basic Structure A protein is made of a chain of amino acids A amino acid sequence is generally reported from the N- terminal end to the C-terminal end J. Biol. Chem. 1973, 248, p. 7670

18. Department of Health Information Management Secondary Structure (Helices)

19. Department of Health Information Management Helix Examples

20. Department of Health Information Management Secondary Structure (Beta-sheets)

21. Department of Health Information Management Beta Sheet Examples Parallel beta sheet Anti-parallel beta sheet

22. Department of Health Information Management Beta Sheet Examples (Cont’d)

23. Department of Health Information Management Protein Structure Example Beta Sheet HelixLoop ID: 12as 2 chains

24. Protein Classification

25. Department of Health Information Management Domain and Motif Domain: a discrete portion of a protein assumed to fold independently of the rest of the protein and possessing its own function. –Most proteins have multiple domains Motif: –Frequently occurring structure patterns among multiple proteins

26. Department of Health Information Management Protein Classification Family: the proteins in the same family are homologous, evolved from the same ancestor. Usually, the identity of two sequences are very high. Super Family: distant homologous sequences, evolved from the same ancestor. Sequence identity is around 25%- 30%. Fold: only shapes are similar, no homologous relationship. Usually, sequence identity is very low. Protein classification databases: SCOP, CATH

27. Department of Health Information Management SCOP The SCOP database aims to provide a detailed and comprehensive description of the structural and evolutionary relationships between all proteins whose structure is known. Proteins are classified to reflect both structural and evolutionary relatedness. –Many levels exist in the hierarchy –The principal levels are family, super family and fold

28. Department of Health Information Management CATH CATH is novel hierarchical classification of protein domain structures, which clusters proteins at four major levels: –Class –Architecture –Topology –Homologous super family

29. Department of Health Information Management CATH-Protein Structure Classification Class Architecture Topology

30. Protein Structure Determination

31. Department of Health Information Management Experimental Methods for Protein Structure Determination X-ray crystallography –Crystallize proteins –Measure X-ray diffraction pattern NMR spectroscopy –NMR – Use nuclear magnetic resonance to predict distances between different Functional groups in a protein in solution. –Calculate possible structure using these distances. Neutron diffraction Electron microscopy Atomic force microscopy

32. Department of Health Information Management Limitations of Experimental Methods X-ray Diffraction –Only a small number of proteins can be made to form crystals –A crystal is not the protein’s native environment –Very time consuming NMR Distance Measurement –Not all proteins are found in solution –This method generally looks at isolated proteins rather than protein complexes –Very time consuming

33. Department of Health Information Management Computational Structure Prediction The functions of a protein is determined by its structure. Experimental methods to determine protein structure are time-consuming and expensive. Big gap between the available protein sequences and structures.

34. Department of Health Information Management Observations Sequences determine structures Proteins fold into minimum energy state. Structures are more conserved than sequences. If two protein sequences share 30% identical residues, then they have a very good chance to have the same fold.

35. Department of Health Information Management Prediction Methods Ab initio folding: build a structure without referring to an existing structure Homology Modeling: sequence-based method Protein Threading: sequence-structure alignment Consensus Method: vote a prediction from some candidates generated by several prediction programs

36. Department of Health Information Management Ab Initio Folding Based on the “first-principle” Build structures purely from protein sequences, no templates used Unaffordable computing demands Paradigm is changing, knowledge-based methods are proposed

37. Department of Health Information Management Secondary Structure Prediction Three-state model: helix (H), strand (E), coil (L) Given a protein sequence: –NWVLSTAADMQGVVTDGMASGLDKD… Predict are secondary structure sequence: –LLEEEELLLLHHHHHHHHHHLHHHL… –Accuracy: 50-85%

38. Department of Health Information Management Predict Protein Secondary Structure Using PredictProtein Protein Sequence >gi|22330039|ref|NP_683383.1| unknown protein; protein id: At1g45196.1 [Arabidopsis thaliana] MPSESSYKVHRPAKSGGSRRDSSPDSIIFTPESNLSLFSSASVSVDRCSSTSDAHDRDD SLISAWKEEFEVKKDDESQNLDSARSSFSVALRECQERRSRSEALAKKLDYQRTVSLDL SNVTSTSPRVVNVKRASVSTNKSSVFPSPGTPTYLHSMQKGWSSERVPLRSNGGRSPPN AGFLPLYSGRTVPSKWEDAERWIVSPLAKEGAARTSFGASHERRPKAKSGPLGPPGFAY YSLYSPAVPMVHGGNMGGLTASSPFSAGVLPETVSSRGSTTAAFPQRIDPSMARSVSIH GCSETLASSSQDDIHESMKDAATDAQAVSRRDMATQMSPEGSIRFSPERQCSFSPSSPS PLPISELLNAHSNRAEVKDLQVDEKVTVTRWSKKHRGLYHGNGSKM PredictProtein web server: –http://www.predictprotein.org

39. Department of Health Information Management Read the Results

41. Department of Health Information Management Evolutionary Methods Taking into account related sequences helps in identification of “structurally important”residues. Algorithm: –Find similar sequences –Construct multiple alignment –Use alignment profile for secondary structure prediction Additional information used for prediction –Mutation statistics –Residue position in sequence –Sequence length

42. Department of Health Information Management Sequence Similarity Methods for Structure Prediction These methods can be very accurate if there is >50% sequence similarity They are rarely accurate if the sequence similarity <30% They use similar methods as used for sequence alignment such as the dynamic programming algorithm, hidden markov models, and clustering algorithms.