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) 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

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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 N-terminal 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: 12as2 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]

MPSESSYKVHRPAKSGGSRRDSSPDSIIFTPESNLSLFSSASVSVDRCSSTSDAHDRDDSLISAWKEEFEVKKDDESQNLDSARSSFSVALRECQERRSRSEALAKKLDYQRTVSLDLSNVTSTSPRVVNVKRASVSTNKSSVFPSPGTPTYLHSMQKGWSSERVPLRSNGGRSPPNAGFLPLYSGRTVPSKWEDAERWIVSPLAKEGAARTSFGASHERRPKAKSGPLGPPGFAYYSLYSPAVPMVHGGNMGGLTASSPFSAGVLPETVSSRGSTTAAFPQRIDPSMARSVSIHGCSETLASSSQDDIHESMKDAATDAQAVSRRDMATQMSPEGSIRFSPERQCSFSPSSPSPLPISELLNAHSNRAEVKDLQVDEKVTVTRWSKKHRGLYHGNGSKM

• 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.