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Genomics and Personalized
Care in Health Systems
Lecture 9 RNA and Protein Structure
Leming Zhou, PhD
School of Health and Rehabilitation Sciences
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Outline
• RNA structure
• Protein structure
• Pharmacogenomics
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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
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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) noncoding 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
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RNA Genes
• RNA has various functions
• There are software developed to search for RNA genes in
the genome.
– tRNAscan searched for tRNA
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RNA Databases
• Ribosomal RNA database
– Ribosomal Database Project: http://rdp.cme.msu.edu/
• tRNA Databases
– Genomic tRNA Database: http://gtrnadb.ucsc.edu/
• snoRNA Databases
– Yeast snoRNA Database:
http://people.biochem.umass.edu/fournierlab/snornadb/main.php
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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
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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
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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.
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Exercises
Use mfold to predict the secondary structure of a
RNA sequence
GTTTCCGTAGTGTAGTGGTTATCACGTTCGCCTCACACGCGAAAGG
TCCCCGGTTCGAAACCGGGCGGAAACA
http://mfold.rna.albany.edu/?q=mfold
Protein Structure
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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
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Protein Basic Structure
• A protein is made of a chain of amino acids
• A amino acid sequence is generally reported from the Nterminal end to the C-terminal end
J. Biol. Chem. 1973, 248, p. 7670
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Secondary Structure (Helices)
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Helix Examples
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Secondary Structure (Beta-sheets)
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Beta Sheet Examples
Parallel beta sheet
Anti-parallel beta sheet
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Beta Sheet Examples (Cont’d)
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Protein Structure Example
Beta Sheet
Helix
Loop
ID: 12as
2 chains
Protein Classification
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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
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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
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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
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CATH
• CATH is novel hierarchical classification of protein
domain structures, which clusters proteins at four major
levels:
– Class
– Architecture
– Topology
– Homologous super family
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CATH-Protein Structure Classification
Class
Architecture
Topology
Protein Structure Determination
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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
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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
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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.
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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.
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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
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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
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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%
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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
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Read the Results
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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
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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.