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Page 1: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

Chap. 11 Protein Structures

Page 2: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

Amino Acid

• R: large white and gray

• C: black• Nitrogen: blue• Oxygen: red• Hydrogen: white

General structure of amino acids an amino group a carboxyl group α-carbon bonded to a

hydrogen and a side-chain group, R

Side chain R determines the identity of particular amino acid

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Protein Protein: polymer consisting of AA’s linked by peptide

bonds AA in a polymer is called a residue

Folded into 3D structures Structure of protein determines its function

Primary structure: linear arrangement of AA’s AA sequence (primary structure) determines 3D

structure of a protein, which in turn determines its properties

N- and C-terminal Secondary structure: short stretches of AAs Tertiary structure: overall 3D structure

Page 4: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

Protein Structures

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Secondary structure Secondary structures have repetitive interactions

resulting from hydrogen bonding between N-H and carboxyl groups of peptide backbone

Conformations of side chains of AA are not part of the secondary structure

α-helix

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Secondary structure β-pleated sheet

Parallel/antiparallel

3D form of antiparallel

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Secondary structure: domain

(a) α unit(b) α α unit (helix-turn-

helix)(c) meander(d) Greek key

Part of chain folds independently of foldings of other parts

• Such independent folded portion of protein is called domain (super-secondary structure)

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Domain Larger proteins are modular

Their structural units, domains or folds, can be covalently linked to generate multi-domain proteins

Domains are not only structurally, but also functionally, discrete units – domain family members are structurally and functionally conserved and recombined in complex ways during evolution

Domains can be seen as the units of evolution Novelty in protein function often arises as a result of

gain or loss of domains, or by re-shuffling existing domains along sequence

Pairs of protein domains with the same 3D fold, precise function is conserved to ~40% sequence identity (broad functional class is conserved ~20%)

DNA binding domains http://en.wikipedia.org/wiki/DNA-binding_domain

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Motif A short, conserved regions (frequently the most

conserved regions of a domain) Critical for the domain to function Domain vs. Motif

Motif are structural characteristics Domains are functional regions, usually consisting

of a few motifs

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Motif Representation

Motif In multiple alignments

of distinctly related sequences, highly conserved regions are called motifs, features, signatures or blocks

Tends to correspond to core structural and functional elements of the proteins

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Motif

(a) complement control protein module

(b) Immunoglobulin module

(c) Fibronectin type I module

(d) Growth factor module

(e) Kringle module

Greek key motif is often found in –barrel tertiary structure

Page 12: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

(a) Linked series of -meanders

(b) Greek key pattern(c) Alternative α untis(d) Top and side views (α-

helical section is outside)

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Secondary structure: conformation

(a) Schematic diagrams of fibrous and globular proteins

(b) Computer-generated model of globular protein

Two types of Protein Conformations Fibrous Globular –folds back onto itself to create a

spherical shape

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Secondary Structure Prediction Ab initio prediction (from AA sequence)

Still an open problem 1974 Peter Chou and Gerald Fasman

Use known structures to determine which AA contributes to each secondary structure

Propensity values : likelihood that an AA appears in a particular structure P(a), P(b) and P(turn) >1 indicates a greater than average chance (log-

odd ratios) Frequency values: frequency of an AA being found in

a hairpin Four positions in a hairpin beta-turn

Accuracy is around 50-60%, but popular due to its foundation for later prediction programs

Page 15: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

AA P(a) P(b) P(turn) f(i) f(i+1) f(i+2) f(i+3)Alanine 142 83 66 0.060 0.076 0.035 0.058Arginine 98 93 95 0.070 0.106 0.099 0.085Asparagine 67 89 95 0.161 0.083 0.191 0.091Aspartic acid 101 54 146 0.147 0.110 0.179 0.081Cysteine 70 119 119 0.149 0.050 0.117 0.128Glutamic acid 151 37 74 0.056 0.060 0.077 0.064Glutamine 111 110 98 0.074 0.098 0.037 0.098Glycine 57 75 156 0.102 0.085 0.190 0.152Histidine 100 87 95 0.140 0.047 0.093 0.054Isoleucine 108 160 47 0.043 0.034 0.013 0.054Leucine 121 130 59 0.061 0.025 0.036 0.070Lysine 114 74 101 0.055 0.115 0.072 0.095Methionine 145 105 60 0.068 0.082 0.014 0.055Pheylalanine 113 138 60 0.059 0.041 0.065 0.065Proline 57 55 152 0.102 0.301 0.034 0.068Serine 77 75 143 0.120 0.139 0.125 0.106Threonine 83 119 96 0.086 0.108 0.065 0.079Tryptophan 108 137 96 0.077 0.013 0.064 0.167Tyrosine 69 147 114 0.082 0.065 0.114 0.125Valine 104 170 50 0.062 0.048 0.028 0.053

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Chou-Fasman Algorithm

Step 1: identify alpha-helices Find a region of six contiguous residues where at

least four have P(a)>103 Extend the region until a set of four contiguous

residues with P(a)<100 is found If region’s average P(a)>103, length is >5, and

∑P(a)> ∑P(b), alpha Step 2: beta strands

Find a region of five contiguous residues with at least three with P(b)>105

Extend the region until a set of four contiguous residues with P(b)<100 is found

If region’s average P(b)>105, and ∑P(b)> ∑P(a), beta

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Chou-Fasman Algorithm Step 3: beta turns

For each residue f, determine the turn propensity (P(t)) for j, asP(t) j = f(i) j *f(i+1) j+1 *f(i+2) j+2 *f(i+3) j+3

A turn at postion if P(t) >0.000075, average P(turn) from j to j+3 > 100, and ∑P(a)< ∑P(turn) > ∑P(b)

Step 4: overlaps If alpha region overlaps with beta, the region’s ∑P(a)

and ∑P(b) determine the most likely structure in the overlapped region

If ∑P(a) > ∑P(b) for the overlapping region, alpha If ∑P(a) < ∑P(b) for the overlapping region, beta If ∑P(a) = ∑P(b), no valid call

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Secondary structure prediction

Page 427

Chou and Fasman (1974) based on the frequencies of amino acids found in a helices, b-sheets, and turns.

Proline: occurs at turns, but not in a helices. GOR (Garnier, Osguthorpe, Robson): related algorithm Modern algorithms: use multiple sequence alignments

and achieve higher success rate (about 70-75%)

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Secondary structure prediction

Web servers:

GOR4JpredNNPREDICTPHDPredatorPredictProteinPSIPREDSAM-T99sec

Table 11-3Page 429

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Secondary Structure Prediction by PSIRED

Prediction of regions of the protein that form alpha-helix, beta-sheet, or random coil

http://bioinf.cs.ucl.ac.uk/psipred/ Based on neural networks Uses Chou-Fasman-like algorithm but first does

PSI-BLAST search to get a collection of sequences related to the input (searching for orthologous sequences)

Univ. College London, 1999

Page 21: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

PSI-BLAST is performed in five steps

1. Select a query and search it against a protein database

2. PSI-BLAST constructs a multiple sequence alignment then creates a “profile” or specialized position-specificscoring matrix (PSSM)

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R,I,K C D,E,T K,R,T N,L,Y,G

Inspect the blastp output to identify empirical “rules” regarding amino acids tolerated at each position

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A R N D C Q E G H I L K M F P S T W Y V 1 M -1 -2 -2 -3 -2 -1 -2 -3 -2 1 2 -2 6 0 -3 -2 -1 -2 -1 1 2 K -1 1 0 1 -4 2 4 -2 0 -3 -3 3 -2 -4 -1 0 -1 -3 -2 -3 3 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 4 V 0 -3 -3 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -1 4 5 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 6 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 7 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 8 L -1 -3 -3 -4 -1 -3 -3 -4 -3 2 2 -3 1 3 -3 -2 -1 -2 0 3 9 L -1 -3 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 2 10 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 11 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 12 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 13 W -2 -3 -4 -4 -2 -2 -3 -4 -3 1 4 -3 2 1 -3 -3 -2 7 0 0 14 A 3 -2 -1 -2 -1 -1 -2 4 -2 -2 -2 -1 -2 -3 -1 1 -1 -3 -3 -1 15 A 2 -1 0 -1 -2 2 0 2 -1 -3 -3 0 -2 -3 -1 3 0 -3 -2 -2 16 A 4 -2 -1 -2 -1 -1 -1 3 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 -1 ... 37 S 2 -1 0 -1 -1 0 0 0 -1 -2 -3 0 -2 -3 -1 4 1 -3 -2 -2 38 G 0 -3 -1 -2 -3 -2 -2 6 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4 39 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -3 -2 0 40 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 41 Y -2 -2 -2 -3 -3 -2 -2 -3 2 -2 -1 -2 -1 3 -3 -2 -2 2 7 -1 42 A 4 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0

20 amino acids

all the amino acids from position 1 to the end of your PSI-BLAST query protein

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A R N D C Q E G H I L K M F P S T W Y V 1 M -1 -2 -2 -3 -2 -1 -2 -3 -2 1 2 -2 6 0 -3 -2 -1 -2 -1 1 2 K -1 1 0 1 -4 2 4 -2 0 -3 -3 3 -2 -4 -1 0 -1 -3 -2 -3 3 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 4 V 0 -3 -3 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -1 4 5 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 6 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 7 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 8 L -1 -3 -3 -4 -1 -3 -3 -4 -3 2 2 -3 1 3 -3 -2 -1 -2 0 3 9 L -1 -3 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 2 10 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 11 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 12 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 13 W -2 -3 -4 -4 -2 -2 -3 -4 -3 1 4 -3 2 1 -3 -3 -2 7 0 0 14 A 3 -2 -1 -2 -1 -1 -2 4 -2 -2 -2 -1 -2 -3 -1 1 -1 -3 -3 -1 15 A 2 -1 0 -1 -2 2 0 2 -1 -3 -3 0 -2 -3 -1 3 0 -3 -2 -2 16 A 4 -2 -1 -2 -1 -1 -1 3 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 -1 ... 37 S 2 -1 0 -1 -1 0 0 0 -1 -2 -3 0 -2 -3 -1 4 1 -3 -2 -2 38 G 0 -3 -1 -2 -3 -2 -2 6 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4 39 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -3 -2 0 40 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 41 Y -2 -2 -2 -3 -3 -2 -2 -3 2 -2 -1 -2 -1 3 -3 -2 -2 2 7 -1 42 A 4 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0

note that a given amino acid (such as alanine) in your query protein can receive different scores for matching alanine—depending on the position in the protein

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A R N D C Q E G H I L K M F P S T W Y V 1 M -1 -2 -2 -3 -2 -1 -2 -3 -2 1 2 -2 6 0 -3 -2 -1 -2 -1 1 2 K -1 1 0 1 -4 2 4 -2 0 -3 -3 3 -2 -4 -1 0 -1 -3 -2 -3 3 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 4 V 0 -3 -3 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -1 4 5 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 6 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 7 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 8 L -1 -3 -3 -4 -1 -3 -3 -4 -3 2 2 -3 1 3 -3 -2 -1 -2 0 3 9 L -1 -3 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 2 10 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 11 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 12 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 13 W -2 -3 -4 -4 -2 -2 -3 -4 -3 1 4 -3 2 1 -3 -3 -2 7 0 0 14 A 3 -2 -1 -2 -1 -1 -2 4 -2 -2 -2 -1 -2 -3 -1 1 -1 -3 -3 -1 15 A 2 -1 0 -1 -2 2 0 2 -1 -3 -3 0 -2 -3 -1 3 0 -3 -2 -2 16 A 4 -2 -1 -2 -1 -1 -1 3 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 -1 ... 37 S 2 -1 0 -1 -1 0 0 0 -1 -2 -3 0 -2 -3 -1 4 1 -3 -2 -2 38 G 0 -3 -1 -2 -3 -2 -2 6 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4 39 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -3 -2 0 40 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 41 Y -2 -2 -2 -3 -3 -2 -2 -3 2 -2 -1 -2 -1 3 -3 -2 -2 2 7 -1 42 A 4 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0

note that a given amino acid (such as tryptophan) in your query protein can receive different scores for matching tryptophan—depending on the position in the protein

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PSI-BLAST is performed in five steps

1. Select a query and search it against a protein database

2. PSI-BLAST constructs a multiple sequence alignment then creates a “profile” or specialized position-specific scoring matrix (PSSM)

3. The PSSM is used as a query against the database

4. PSI-BLAST estimates statistical significance (E values)

1. Repeat steps [3] and [4] iteratively, typically 5 times.At each new search, a new profile is used as the query

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SRC protein

Tyrosine kinase Enzyme putting a phophate group on tyrosine AA

(phosphorylation) Activates an inactive protein, eventually

activates cell-division proteins NP_005408

>gi|4885609|ref|NP_005408.1| proto-oncogene tyrosine-protein kinase Src [Homo sapiens]

MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSS

DTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYV

APSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKL

DSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGC

FGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLL

DFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYT

ARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPEC

PESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL

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Examining Crystal Structure Cn3D: NCBI structure viewer and modeling tool DeppView: SWISSPROT JMOL

NCBI Structure database Links to NCBI MMDB (Molecular Modeling

Database) MMDB contains experimentally verified protein

structures

SRC – MMDB ID 56157, PDB ID 1FMK

View Structure from NCBI Structure database Opens up Cn3D window Click to rotate; Ctrl_click to zoom; Shift_clcik to

move Rendering and coloring menus

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Tertiary structure 3D arrangment of all atoms in the module Considers arrangement of helical and sheet sections,

conformations of side chains, arrangement of atoms of side chains, etc.

Experimentally determined by X-ray crystallography –

measure diffraction patterns of atoms

NMR (Nuclear Magnetic Resonance) spectroscopy – use protein samples in aqueous solution

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• Tertiary structure of α-lactalbumin myoglobin

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Protein families Groups of genes of identical or similar sequence are

common Sometimes, repetition of identical sequences is

correlated with the synthesis of increased quantities of a gene product e.g., a genome contains multiple copies of ribosomal

RNAs Human chromosome 1 has 2000 genes for 5S rRNA

(sedimentation coefficient), and chr 13, 14, 15, 21 and 22 have 280 copies of a repeat unit made up of 28S, 5.8S and 18S

Amplication of rRNA genes evolved because of heavy demand for rRNA synthesis during cell division

These rRNA genes are examples of protein families having identical or near identical sequences

Sequence similarities indicate a common evolutionary origin

α- and β-globin families have distinct sequence similarities evolved from a single ancestral globin gene

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Protein families and superfamilies Dayhoff classification, 1978

Protein families – at least 50 % AA sequence similar (based on physico-chemical AA features)

Related proteins with less similarity (35%) belong to a superfamily, may have quite diverse functions

α- and β-globins are classified as two separate families, and together with myoglobins form the globin superfamily

families have distinct sequence similarities evolved from a single ancestral globin gene

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Protein family database Pattern or secondary database derived from sequences

a pattern may be the most conserved aspects of sequence families

The most conserved part may vary between species Use scoring system to account for some variability Position-specific scoring matrix (PSSM) or Profile

Contrast to a pairwise alignment, having the same weight regardless of positions

Protein family databases are derived by different analytical techniques But, trying to find motifs, conserved regions, considered to

reflect shared structural or functional characteristics Three groups: single motifs, multiple motifs, or full domain

alignments

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Protein family databases

Data source Stored info

PROSITE Swiss-Prot Regular expressions (patterns) of single most conserved motif

Profiles Swiss-Prot Weighted matrices (profiles) of position-sensitive weights

PRINTS Swiss-Prot and TrEMBL

Aligned motifs (fingerprints)

Pfam Swiss-Prot and TrEMBl

multiple sequence alignment of a protein domain or conserved region

Blocks interPro/PRINTS Aligned motifs (blocks)

eMOTIF Blocks/PRINTS Permissive regular expressions

Pattern or secondary database derived from sequences

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Page 36: Chap. 11 Protein Structures. Amino Acid R: large white and gray C: black Nitrogen: blue Oxygen: red Hydrogen: white General structure of amino acids an.

Single Motif Method Regular expression

PROSITE PDB 1ivy

Carboxypet_Ser_His (PS00560) [LIVF]-x2-[[LIVSTA]-x[IVPST]-[GSDNQL]-[SAGV]-

[SG]-H-x-[IVAQ]-P-x(3)-[PSA] [] – any of the enclosed symbols X- any residue (3) – number of repeats

Fuzzy regular expression Build regular expressions with info on shared

biochemical properties of AA Provide flexibility according to AA group

clustering

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Multiple motif methods PRINTS

Encode multiple motifs (called fingerprints) in ungapped, unweighted local alignments

BLOCKS Derived from PROSITE and PRINTS Use the most highly conserved regions in protein

families in PROSITE Use motif-finding algorithm to generate a large number

of candidate blocks Initially, three conserved AA positions anywhere in the

alignment are identified and used as anchors Blocks are iteratively extended and ultimately encoded

as ungapped local alignments Graph theory is used to assemble a best set of blocks

for a given family Use position specific scoring matrix (PSSM), similar to a

profile

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Full domain alignment Profiles

Use family-based scoring matrix via dynamic programming

Has position-specific info on insertions and deletions in the sequence family

Hidden Markov Model (HMM) PFAM, SMART, TIGRFAM represent full domain

alignments as HMMs PFAM

Represents each family as seed alignment, full alignment, and an HMM

Seed contains representative members of the family

Full alignment contains all members of the family as detected with HMM constructed from seen alignment

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Structure-based Sequence Alignment Well-known that sequence alignment is not correct by

sequence similarity alone and that similar structure but no sequence similarity

Sequence alignment is augmented by structural alignments COMPASS< HOMSTRAD< PALI, ..

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Protein Structure Comparison/Classification

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Protein structures Domain

Polypeptide chain in a protein folds into a ‘tertiary ’ structure

One or more compact globular regions called domains

The tertiary structure associated with a domain region is also described as a protein fold

Multi-domain Proteins with polypeptide chains fold into several

domains Nearly half the known globular structures are

multidomain, more than half in two domains Automatic structure comparison methods are

introduced in 1970s shortly after the first crystal structures are stored in PDB

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Structure comparison algorithms Two main components in structure comparison

algorithms Scoring similarities in structural features Optimization strategy maximizing similarities

measured Most are based on geometric properties from 3D

coordinates Intermolecular method

Superpose structures by minimizing distance between superposed position

Intra Compare sets of internal distances between

positions to identify an alignment maximizing the number of equivalent positions

Distance is described by RMSD (Root Mean Square Deviation), squared root of the average squared distance between equivalent atoms

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Inter vs. Intra

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RMSD

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Distant homolog Structure is more

conserved than sequences during evolution

Structural similarity between distant homologs can be found Pairwise

sequence similarity

SSAP structural similarity score in parenthesis (0 – 100)

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Distant homolog

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Structural variations in protein families

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Structure comparison algorithms SSAP, 1989

Residue level, Intra, Dynamic programming DALI, 1993

Residue fragment level, intra, Monte Carlo optimization

COMPARER, 1990 Multiple element level, both, Dynamic

programming

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Structure classification hierarchy Class level -- proteins are grouped according to

their structural class (composition of residues in a α -helical and β-strand conformations) Mainly- α, mainly- β, alternating α- β, α plus

β (mainly- α and – β are segregated) Architecture

the manner by which secondary structure elements are packed together (arrangement of sec. structures in 3D space)

Fold group (topology) Orientation of sec. structures and the

connectivity between them Superfamily Family

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Hierarchy example

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Protein Structure databases PDB

Over 20,000 entries deduced from X-ray diffraction, NMR or modeling

Massively redundant 1FMK, 1BK5, 2F9C, ..

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Protein Structure databases SCOP (Structural Classification of Proteins)

Multi-domain protein is split into its constituent domains Known structures are classified according to evolutionary

and structural relationship Domains in SCOP are grouped by species and

hierarchically classified into families, superfamilies, folds and classes Family level – group together domains with celar

sequence similarities Superfamily – group of domains with structural and

functional evidence for their descent from a common evolutionary ancestor

Gold – group of domains with the same major secondary structure with the same chain topology

Domains identified manually by visually inspecting structures

Proteins in the same superfamily often have the same function

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Protein Structure databases CATH (Class, Architecture, Topology, Homology)

Homology – clustered domains with 35% sequence identity and shared common ancestry

800 fold families, 10 of which are super-folds 2009 www.cs.uml.edu/~kim/580/08_cath.pdf

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Structure classification Most structure classifications are established at

the domain level Thought to be an important evolutionary unit and

easier to determine domain boundaries from structural data than from sequence data

Criteria for assessing domain regions within a structure The domain possesses a compact globular

structure Residues within a domain make more internal

contacts than to residues in the rest of polypeptide

Secondary structure elements are usually not shared with other regions of the polypeptide

There is evidence for existence of this region as an evolutionary unit

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CATH classifications

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Multi-domain structures

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Protein Function/Structure Prediction

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Protein Function Prediction In the absense of experimental data, function of a

protein is usually inferred from its sequence similarity to a protein of known function The more similar the sequence, the more similar the

function is likely to be Not always true

Can clues to function be derived directly from 3D structure

Definition of function Function can be described at many levels:

biochemical, biological processes, pathways, organ level

Proteins are annotated at different degrees of functional specificity: ubiquitin-like dome, signaling protein, ..

GO (Gene Ontology) scheme

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Protein Function Prediction Sequence-based – largely unreliable

Profile-based Profiles are constructed from sequences of whole protein

families with families are grouped by 3D structure or function (as in Pfam)

Start with sequences matched by an initial search, iteratively pull in more remote homologues

More sensitivity than simple sequence comparison because profiles implicitly contain information on which residues within the family are well conserved and which sites are more variable

Structure-based Fold-based

Proteins sharing simlar functions often shave similar folds, resulting from descent from a common ancestral protein

Sometimes, function of proteins alter during evolution with the folds unchanged

Thus, fold match is not always reliable Surface clefts and binding pockets

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Chap. 12 RNA Structures

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Stem-loop structureRNA structure

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A loop structure A loop between i and j when base at i pairs with base

at j Base at i+1 pairs with at base j Or base at i pairs with base at j-1 Or a multiple loop

RNA structure

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Search for minimum free energy Gibbs free energy at 37

degrees (C) Free energy increments of

base pairs are counted as stacks of adjacent pairs Successive CGs: -3.3

kcal/mol Unfavorable loop initiation

energy to constrain bases in a loop

RNA secondary structure

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Ad-hoc approach Simply look at a strand and find areas where base

pairing can occur Possible to find many locations where folds can

occur Prediction should be able to determine the most

likely one What should be the criteria ?

1980, Nussinov-Jacobson Algorithm More stable one is the most likely structure Find the fold that forms the greatest number of

base pairs (base-pairing lowers the overall energy of the strand, more stable)

Checking for all possible folds is impossible -> dynamic programming

RNA structure prediction

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Create an nxn matrix for a sequence with n bases

Initialize the diagonal to 0 Fill the matrix with the largest number of base

pairs (S)

w(I,j) = 1 if base I can be paired with base j

Nussinov-Jacobson Algorithm

S(i+1, j-1) + w(i,j)S(i,j) = max [ S(i+1, j) ] S(i, j-1)

max[S(I,k) + S(k+1,j)}