Functional Analysis of Proteins and Proteomes

1
Functional Analysis of Proteins and Proteomes

• CSB2003 Tutorial
• Steve Bennett, Ph.D.
• steve_at_bennett.org

2
Introduction

• Although genetic material contains all the
  information required for cellular function, DNA
  itself does not carry out much the work in cells.
• Rather, it is the products of those genes,
  proteins and sometimes regulatory and catalytic
  RNAs, that carry out the chemical and mechanical
  work in biological systems.

3
Central Dogma in Biology

• DNA
• RNA (sequence, structure)
• Protein (sequence, structure)

4
Introduction

• With the completion of numerous genome projects,
  resources and focus are shifting from genomes to
  proteomes.
• Once researchers have an accurate collection of
  gene sequences, the next question is what these
  genes do.

5
Introduction

• Although there are numerous definitions of
  function with respect to proteins, here we
  define it as precisely that what it is that a
  particular gene product, or protein, does in the
  cell.
• Examples
• Molecular motor (kinesin, myosin)
• Zinc-finger transcription factor

6
Introduction

• In this tutorial, I will first give a general
  protein introduction, followed by historical and
  current computational approaches for assigning
  function to a protein.
• Background
• Overview of some selected algorithms and
  approaches
• Demos and software examples

7
Forming a Peptide Bond
Creates the Primary Structure, or protein sequence
8
Polypeptide Chain
The chemical nature of the R groups determine
the amino acid sequence of the peptide
9
Translation
10
Planar Peptide Bond
11
Alpha Helix Structure
12
Alpha Helix End-On
13
Alpha Helix Variable Pitch
14
Anti-Parallel Beta Sheets
15
Corrugated Beta Sheets
16
Beta Turn at End of Anti-Parallel Sheet
17
Protein Database (PDB) Growth

• 19,225 released atomic coordinate entries
• 17,315 proteins, peptides, and viruses
• 1,892 nucleic acids, protein-nucleic acid
  complexes

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Structural Challenges

• Compare all known structures to each other
• Classify and organize all structures in a
  biological way
• Find common folding patterns and structural
  motifs
• Compute evolutionary distances between protein
  structures
• Study interactions between structures and other
  molecules (Protein Docking)
• Use known structures to predict structure from
  sequence (Protein Threading)
• Many more ...

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Classification of Protein Structures

• Class
• Similar secondary structure content
• All a all b ab a/b etc
• Fold (Architecture)
• Major structural similarity
• SSEs in similar arrangement
• globin-like fold, TIM barrel fold
• Superfamily (Topology)
• Probable common ancestry
• globins phycocyanin
• Family
• Clear evolutionary relationship
• Sequence similarity usually gt 25

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Class
Fold / Architecture
Superfamily
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Classes of Protein Structures

• Mainly ?
• Mainly ?
• ????
• Parallel ? sheets, ?-?-? units

• ???
• Anti-parallel ? sheets, segregated ? and ?
  regions
• helices mostly on one side of sheet

22
Classes of Protein Structures

• Others
• Multi-domain, membrane and cell surface, small
  proteins, peptides and fragments, designed
  proteins

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Folds / Architectures

• ??? and ???
• Closed
• Barrel
• Roll, ...
• Open
• Sandwich
• Clam, ...

• Mainly ?
• Bundle
• Non-Bundle
• Mainly ?
• Single sheet
• Roll
• Barrel
• Clam
• Sandwich
• Prism
• 4/6/7/8 Propeller
• Solenoid

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eg. The TIM Barrel Fold
25
Growth in PDB Folds
Gold Old Folds White New Folds
26
Databases of Folds

• SCOP
• Murzin AG, Brenner SE, Hubbard T, Chothia C
• Structural Classification of Protein Structures
• Manual assembly by inspection
• All nodes are annotated (eg. All-alpha,
  alpha/beta)
• Structural similarity search using 3dSearch
  (Singh and Brutlag)
• CATH
• Dr. C.A. Orengo, Dr. A.D. Michie, Dr. S. Jones,
  Dr. M.B. Swindells, Dr. G. Hutchinson, Dr. A.
  Martin, Dr. D.T. Jones, Prof. J.M. Thornton
• Class - Architecture - Topology - Homologous
  Superfamily
• Manual classification at Architecture level
• Automated topology classification using the SSAP
  algorithm No structural similarity search

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Databases of Folds

• FSSP
• L. L. Holm and C. Sander
• Fully automated using the DALI algorithm (Holm
  and Sander)
• No internal node annotations
• Structural similarity search using DALI
• Pclass
• A. Singh, X. Liu, J. Chang, D. Brutlag
• Fully automated using the LOCK and 3dSearch
  algorithms
• All internal nodes automatically annotated with
  common terms
• JAVA based classification browser
• Structural similarity search using 3dSearch

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Protein Structure Prediction
Sequence of 984 amino acids
PISPIETVPVKLKPGMDGPKVKQWPLTEEKIKALVEICTEMEKEGKISKI
G PENPYNTPVFAIKKKDSTKWRKLVDFRELNKRTQDFWEVQLGIPHPAG
LKK KKSVTVLDVGDAYFSVPLDEDFRKYTAFTIPSINNETPGIRYQYNV
LPQGW KGSPAIFQSSMTKILEPFKKQNPDIVIYQYMDDLYVGSDLEIGQ
HRTKIEE LRQHLLRWGLTTPDKKHQKEPPFLWMGYELHPDKWTVQPIVL
PEKDSWTVN DIQKLVGKLNWASQIYPGIKVRQLCKLLRGTKALTEVIPL
TEEAELELAEN REILKEPVHGVYYDPSKDLIAEIQKQGQGQWTYQIYQE
PFKNLKTGKYARM RGAHTNDVKQLTEAVQKITTESIVIWGKTPKFKLPI
QKETWETWWTEYWQA TWIPEWEFVNTPPLVKLWYQLEKEPIVGAETFYV
DGAANRETKLGKAGYVT NKGRQKVVPLTNTTNQKTELQAIYLALQDSGL
EVNIVTDSQYALGIIQAQP DKSESELVNQIIEQLIKKEKVYLAWVPAHK
GIGGNEQVDKLVSAGI PISPIETVPVKLKPGMDGPKVKQWPLTEEKIKA
LVEICTEMEKEGKISKIG PENPYNTPVFAIKKKDSTKWRKLVDFRELNK
RTQDFWEVQLGIPHPAGLKK KKSVTVLDVGDAYFSVPLDEDFRKYTAFT
IPSINNETPGIRYQYNVLPQGW KGSPAIFQSSMTKILEPFKKQNPDIVI
YQYMDDLYVGSDLEIGQHRTKIEE LRQHLLRWGLTTPDKKHQKEPPFLW
MGYELHPDKWTVQPIVLPEKDSWTVN DIQKLVGKLNWASQIYPGIKVKQ
LCKLLRGTKALTEVIPLTEEAELELAEN REILKEPVHGVYYDPSKDLIA
EIQKQGQGQWTYQIYQEPFKNLKTGKYARM RGAHTNDVKQLTEAVQKIT
TESIVIWGKTPKFKLPIQKETWETWWTEYWQA TWIPEWEFVNTPPLVKL
WYQ
HIV reverse transcriptase
3D coordinates of 7404 atoms
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Abstracting the problem
3D coords of C-alpha backbone
3D coords of all atoms
3D coords of secondary structure elements
C-alpha groups
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Defining the secondary structure of a protein
sequence
Alpha helix and anti-parallel beta sheet
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The Secondary Structure Prediction Problem

• Given a protein sequence
• NWVLSTAADMQGVVTDGMASGLDKD...
• Predict a secondary structure sequence
• LLEEEELLLLHHHHHHHHHHLHHHL...
• 3-state problem ARNDCQEGHILKMFPSTWYVn -gt
  L,H,En

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Amphipathic helix End view
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Amphipathic helix backbone sidechains
34
Amphipathic helixhydrophobic sidechains
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Amphipathic helixhydrophobic sidechains
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Amphipathic helixsidechain periodicity
Sequence NLAKMVVKTAEAILKD
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Structural Correlations in Alpha-Helices
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Structural Correlations inBeta-Strands
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Functional Analysis of Proteins
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Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

Seqs of known function
?
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Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

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Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

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Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

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Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

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Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

46
Sequence methods

• The earliest general approach for assigning
  function to a protein sequence was to compare the
  sequence of unknown function to a sequence (or
  sequences) of known function.

47
Sequence methods

• One such method for doing this is sequence
  alignment in which two sequences are aligned to
  determine how similar they are to one another.

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Sequence methods

• If an alignment is of sufficient quality, one
  might assign the function of the known sequence
  to be that of the unknown sequence as well.

Scorealignment gt threshold
Function Zn finger transcription factor
function assignment
49
Sequence Alignment

• Well briefly discuss two different alignment
  approaches, pairwise sequence alignment and
  multiple sequence alignment before moving on to
  other topics.
• Alignments are most often in one of two forms
  local or global.

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Amino Acid Similarity

• To discuss alignment methods, we first need to
  discuss methods for determining if characters in
  different sequences are similar.
• Identity
• Biochemical properties
• PAM, BLOSUM matrices

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PAM Matrices

• Percent Accepted Mutation Matrices (Dayhoff)
• Examine amino acid changes in groups of related
  proteins with at least 85 sequence similarity.
• The differing amino acids are assumed to be
  accepted over evolutionary time.
• Counts are normalized and used to estimate a
  matrix representing all possible amino acid
  changes.

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PAM Matrices
53
BLOSUM Matrices
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Dot Matrices

• Dot matrices create an n x m matrix from the
  two sequences to be compared.
• A match is scored in the matrix by strict
  character identity, chemical similarity of the
  amino acids, or the use of a symbol comparison
  matrix such as PAM or BLOSUM.
• Mark the matrix location of each match with a
  dot. Connected regions of similarity will
  appear as diagonal lines.

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Dot Matrices

• A D S C T F G V V L I
• A
• E º
• S
• C
• V
• V
• L
• V º

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Dot Matrices

• Drosophila SLIT

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Dot Matrices SLIT vs. itself
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Dot Matrices

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Dot Matrices

• Improving signal-to-noise in dot matrices
• Sliding Window
• Scoring a match at position i, j in the matrix
  is not independent downstream positions are
  considered as well. This helps screen out
  spurious matches in favor of meaningful local
  regions of similarity.
• Variable Stringency
• Used with the sliding window method denotes
  how many characters in the window must match for
  a hit to be declared at position i, j.

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Dot Matrices

• Advantages
• Intuitive and Straightforward
• Immediate visualization of similar subsequences
• Limitations
• Although related subsequences are easily seen, it
  is unclear what the best alignment is between
  the.
• Difficult to assess the quality of different
  alignments no scoring system

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Dynamic Programming

• Considerable improvement to the basic dot matrix
  approach DP generates a provably-optimal
  alignment between a pair of sequences.
• Produces a score which can be evaluated for
  statistical significance given the aligned
  sequences and conditions.
• Allows for the inclusion of gaps without the
  extremely large number of computations required
  in any direct computation.
• Can be used for both local and global alignments.

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Dynamic Programming

• As observed for dot matrices, DP uses a scoring
  system that favors identical and similar amino
  acids, and penalizes dissimilar amino acids and
  gaps.
• Values for the scoring system are usually derived
  from amino acid substitution tables such as PAM
  or BLOSUM matrices. Each position in a potential
  alignment is evaluated according to these
  substitution tables.
• The scores for all positions are then summed to
  generate an overall log-odds score for the
  alignment.

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Dynamic Programming

• Similar to the dot matrix algorithm, we construct
  an n x m matrix consisting of the 2 sequences to
  be aligned and a gap row, allowing each
  sequence to begin with a gap if necessary.
• Instead of marking a dot, we calculate a
  running best score that depends on the scores of
  the cells calculated previously. The matrix is
  built left to right, to bottom.

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Dynamic Programming

• Specifically, given two sequences, p and q
• p p1p2pipn
• q q1q2qjqm
• then the score at each position i in sequence p
  and position j in sequence q (that is, the score
  Sij in each matrix cell) is given by

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Example VDFS and VET
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Example VDFS and VET
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Example VDFS and VET
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Example VDFS and VET
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Example VDFS and VET
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Example VDFS and VET
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Example VDFS and VET
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Example VDFS and VET

• VDFS
• V E -T

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Dynamic Programming

• Smith-Waterman more widely used implementation
  for local alignments.
• Software packages
• BESTFIT (Smith-Waterman)
• GAP (Needleman-Wunsch)
• On the web http//motif.stanford.edu/alion/

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Dynamic Programming

• Advantages
• Alignments are optimal
• Quantitative score associated with the alignments
• Limitations
• Costly in time and space hardware required for
  database-sized searches (increasingly important
  for modern bioinformatics applications).

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Rapid Database Searching

• Goal 1 Execute with less demands in time and
  space than dynamic programming.
• Goal 2 Perform reasonably well using
  heurisitics, as compared to the DP optimal
  solution.
• Drawback Resulting sequence alignments are not
  guaranteed to be optimal.

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Rapid Database Searching FASTA

• Dynamic Programming approaches match single
  characters at a time
• FASTA matches groups of characters, called words
  or k-tuples, which are managed in a table.

ADCGPH
ADCGPH
ADCGPH
ADCGPH
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Rapid Database Searching FASTA
db1
db2
Assume k 3 8000 possible 3-character words.
db3
Scan each database sequence, recording the
position of each 3-tuple in a lookup table of
size 8000 keyed on the 3-tuple
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Rapid Database Searching FASTA
db1
AAC
db2
Assume k 3 8000 possible 3-character words.
AAC
db3
AAC

• Assume that the 3-tuple AAC occurs
• at position 12 in db1
• at position 52 in db2
• at position 20 in db3

After scanning the database sequences and
building the lookup table, the table element
corresponding to AAC would look like
AAC ? db112 , db252 , db320
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Rapid Database Searching FASTA
db1
AAC
db2
AAC
db3
AAC
AAC ? db112 , db252 ,
db320
Suppose a query sequence, q, has the 3-tuple AAC
at position 60. The table returns the 3 database
sequences and the locations where the matching
tuple occurs in those sequences. Assuming this is
done for another 3-tuple, DFE, we might
have 3-tuple db1 q AAC
12 60 DFE 56 104
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Rapid Database Searching FASTA
AAC ? db112 , db252 ,
db320
3-tuple db1 q AAC
12 60 DFE 56 104 Next,
FASTA compute the offsets between the locations
of matched tuples. Here, we see that for AAC and
DFE, the offest is identical, equal to 48. This
indicates that these 3-tuples are in-phase or
part of a larger locally-aligned region. FASTA
then rescores these local alignments using a
PAM250 matrix, and takes the 10 highest regions
of identity and performs a joining step in an
attempt to join the regions.
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Rapid Database Searching BLAST

• BLAST is a much faster algorithm than FASTA, and
  has been shown to be just as sensitive.
• As such, BLAST is considerably more widely used.
• Similar to FASTA in that it uses words, but the
  size is fixed at k 3.

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Rapid Database Searching BLAST

• BLAST first extracts all overlapping 3-tuples
  from a query sequence.
• Then, the tuples in the query are evaluated
  against the possible 8000 tuples using a BLOSUM
  matrix. This determines if inexact matches
  between query words and potential database words
  are above a certain threshold score. Those tuples
  remaining are assembled into a tree for rapid
  database search.

ADCGPH
ADC
DCG
CGP
GPH
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Rapid Database Searching BLAST

• Suppose we observed the tuple SEI in the query
  sequence.
• Step 1. Score against all 8000 tuples, keeping
  only those that are above our predetermined
  scoring threshold.
• SEI scored against SEI gives a score of 13 (S-S
  E-E I-I) in the BLOSUM matrix)
• SEI scored against SDI gives a score of 10
• SEI scored against SDG gives a score of only 2.
• Hence, if our cutoff score were 9, we would keep
  SEI and SDI, but not SDG when assembling the
  search tree.

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Rapid Database Searching BLAST

• Step 2. Once the possible matching tuples are
  stored in the tree, database sequences are
  searched for exact matches to these possible
  scores.
• Matches are examined for regions that are on the
  same diagonal and within some distance, A of one
  another. These regions serve as starting points
  for a longer ungapped alignment between the
  words. These joined regions are then extended in
  each direction as long as the score is
  increasing.
• PSI-BLAST Iterative BLAST approach that includes
  conservation information within a family of
  proteins as opposed to just between two proteins.

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Multiple Sequence Alignments

• So far, we have only discussed pairwise
  alignments since they are the most commonly used,
  have optimal solutions.
• Multiple sequence alignments are vitally
  important to understanding true evolutionary
  conservation between sequences in a family.

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Multiple Sequence Alignments

• Allows for the extraction of probes for new
  members of a family (motifs / patterns).
• Helps identify the functionally important amino
  acids in a protein family. Amino acids not
  required for function or structural integrity
  will in general not be highly conserved within a
  family
• VTDIAYRCGFSDSNHFSTLFRREFNWSPRDI
• VTEIAYRCGFGDSNHFSTLFRREFNWSPRDI
• VFQISHRCGFGSNAYFCDVFKRKYNMTPSQF
• VFQISHRCGFGSNAYFCDAFKRKYGMTPSQF

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Representations for Similarity and Alignments

• Short, simple representations for conserved
  sequence information in MSAs make assigning new
  proteins to the family considerably easier.
• Short representations can suggest functional and
  biological conclusions regarding why certain
  amino acids are conserved at certain positions.
• Such representations can identify function in a
  protein that more global homology methods (such
  as BLAST) might miss.

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Profiles

• A highly conserved local region in an MSA is
  identified, then a profile (a type of PSSM) is
  constructed to describe it.
• 20 x n matrix with each column describing the
  scores, or probabilities of different amino acids
  appearing at a given position.

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PROSITE patterns

• Patterns (motifs, signatures, fingerprints) are
  short regular expression-like text strings that
  describe a conserved region.
• PROSITE is a manually curated database of
  profiles and patterns.
• Focuses on particular regions of family MSAs
  shown in the literature to be biologically
  important (usually catalytic sites, metal-binding
  sites, reduced cysteines, or ligand binding
  sites).

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PROSITE patterns

• Short conserved sequences from the MSA are then
  extracted as a core and used to search
  SWISS-PROT. If no additional sequences are found,
  the core is designated as the actual signature.
  If numerous false positives are picked up, then
  the core is increased in size until good
  discrimination is achieved, or until it is clear
  that good discrimination wont be possible.
• C-x(15)-A-x(3,4)-G-x(3)-C-x(2)-G-x(8,9)-P-x(7)-
  C

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Blocks

• Blocks are short, ungapped conserved regions in
  multiple sequence alignments.

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Blocks

• They are created from one of two starting with
  either
• Unaligned sequences from PROSITE families
• An existing MSA.
• Since PROSITEs manual curation limits is size,
  the BLOCKS database currently includes families
  from PRINTS-S and InterPro in addition to PROSITE.

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BLOCKS Contains Many Protein Families(Henikoff
Henikoff, 1999)
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Properties of eMOTIFshttp//emotif.stanford.edu/

• Discrete motifs that represent specific functions
• Highly specific motifs for searching entire
  proteomes
• Maintain sensitivity with multiple motifs
• Generate motifs automatically from protein
  alignments
• Resistant to sequence errors, misalignment
  misclassification
• Robust with respect to protein subclasses
• Generates structural motifs potential drug
  targets
• Biological generalization from known examples

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eMOTIFshttp//emotif.stanford.edu/
fly..h...hst..krpfy.c
96
Generating Motifs fromAligned Protein Sequences
TEAESNMNDPVAEYQQYTDARQDLYELEVDYANLTEARENIAVLERDF
EEVTEAESNMNDLVSEYQQYTEVRANMNDLVAEYQQYSEAESNMNDL
VSEYQQYTEAREDLAALEKDYEEVTEAREDLAALERDYIEVSEARED
LAALEKDYEEVAEAREDLAALEKDYIEVSEAREDLAALEKDYEEVSE
AREDLAALERDYEEV
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Generating Motifs fromAligned Protein Sequences
TEAESNMNDPVAEYQQYTDARQDLYELEVDYANLTEARENIAVLERDF
EEVTEAESNMNDLVSEYQQYTEVRANMNDLVAEYQQYSEAESNMNDL
VSEYQQYTEAREDLAALEKDYEEVTEAREDLAALERDYIEVSEARED
LAALEKDYEEVAEAREDLAALEKDYIEVSEAREDLAALEKDYEEVSE
AREDLAALERDYEEV
TEARENIAVLERDFEEV SDVESDNNDPVAEYIQL A A LYE V
ANY Q A S Q K
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Generating Motifs fromAligned Protein Sequences
TEAESNMNDPVAEYQQYTDARQDLYELEVDYANLTEARENIAVLERDF
EEVTEAESNMNDLVSEYQQYTEVRANMNDLVAEYQQYSEAESNMNDL
VSEYQQYTEAREDLAALEKDYEEVTEAREDLAALERDYIEVSEARED
LAALEKDYEEVAEAREDLAALEKDYIEVSEAREDLAALEKDYEEVSE
AREDLAALERDYEEV
TEAREDLAALERDYEEV S K I A
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Generating Motifs fromAligned Protein Sequences
TEAESNMNDPVAEYQQYTDARQDLYELEVDYANLTEARENIAVLERDF
EEVTEAESNMNDLVSEYQQYTEVRANMNDLVAEYQQYSEAESNMNDL
VSEYQQYTEAREDLAALEKDYEEVTEAREDLAALERDYIEVSEARED
LAALEKDYEEVAEAREDLAALEKDYIEVSEAREDLAALEKDYEEVSE
AREDLAALERDYEEV
TEARENIAVLERDFEEV SDVESDNNDPVAEYIQL A A LYE V
ANY Q A S Q K
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Amino Acid Substitution Groups Based on Physical
Properties

• Only permit groups of amino acids
• sharing some chemical or physical property

Group


AG

ST

PAGST

QN

QNED

KR

VLI

VLIM

FYW

KRH

DE



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Allowable Amino AcidSubstitution Groups
fly..h...hst..krpfy.c
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Discovery of eMOTIFshttp//emotif.stanford.edu/
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Discovery of eMOTIFshttp//emotif.stanford.edu/
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• Each red dot is an eMOTIF
• Most specific eMOTIFs along pareto-optimal curve
• High Sensitivity gt Low Specificity
• High Specificity gt Low Sensitivity

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Protein Function with eMOTIF Searchhttp//emotif.
stanford.edu/
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Protein Function with eMOTIF-Searchhttp//emotif.
stanford.edu/
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3MOTIFs 3MATRICEShttp//3motif.stanford.edu/
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Searched for 3est
Visualization Features Conservation strength
shading Relative and overall solvent
accessibilities per residue, and for the eMOTIF
as a whole Accessibility shading Multiple display
and manipulation options
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Visualization Features
3est - cgg.lilv...wvilmvstaahc
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3motif Pipeline Construction Query
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eMotifs and SCOP

• eMotifs were observed to correlate strongly with
  SCOP classification, even when global sequences
  were not overly similar.
• eMotifs that were found to hit proteins in
  different SCOP locations were particularly
  interesting.

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eMATRIXPosition-Specific Scoring Matrices
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An eMATRIXhttp//ematrix.stanford.edu/
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eMATRIX Scanhttp//ematrix.stanford.edu/
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eMATRIX Scan Resultshttp//ematrix.stanford.edu/e
matrix-scan/
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eMATRIX Searchhttp//ematrix.stanford.edu/
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eMATRIX Search Resultshttp//ematrix.stanford.edu
/
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eMATRIX Makerhttp//ematrix.stanford.edu/
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3MATRIXhttp//3matrix.stanford.edu/
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ePROTEOMEA Functional Genomics
Databasehttp//eproteome.stanford.edu/
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BLOCKS Is Based On SeveralProtein Family
Databases
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eBLOCKs - Discovering Protein Motifshttp//eblock
s.stanford.edu/
Higher Specificity
A
B
C
Higher Sensitivity
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Building eBLOCKs with PSI-BLAST

• 1) Compare the query to database with BLAST
• 2) Construct profile from significant
  similarities
• 3) Compare the profile to database
• 4) Repeat step 2 and 3 until convergence

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Generating Multiple OverlappingeBLOCKs from
PSI-BLAST Results
G2B1
G2B2
1
2
G3B1
G3B2
G3B3
G1B1
G1B2
1 Clustering Grouping 2 Aligning Trimming
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Clusters Are Organized Into Groupswith Varying
Specificity Sensitivity
Higher Specificity
A
B
C
Higher Sensitivity
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eBLOCKs Summary

• SWISS-PROT
• 79,449 Sequences
• Filtered Target Set
• Homologous, putative, fragment, hypothetical,
  probable, possible
• 57,266 Sequences
• PSI-BLAST Searches
• 17,415
• Final Number Of Groups
• 19,889
• Final Number Of Blocks
• 81,413

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eBLOCKs are More Comprehensive
148
Properties of 52,671 Novel eBLOCKs

• New eBLOCKs are the same width as BLOCKS blocks
• Average new eBLOCK 34 positions, others 37
• New eBLOCKs have fewer sequences than BLOCKS
  blocks
• Average new eBLOCK has 18 sequences, BLOCKS 27
• New eBLOCKs have similar information content
• New eBLOCKs have 2.82 bits/position, BLOCKS 2.88
  bits
• One half of new eBLOCKs (26,254) are in known
  families
• One half of new eBLOCKs (26,471) are in 6,713 new
  families

149
Example of New eBLOCK in a Known Family
14-3-3 Family of Proteins
68 Sequences, 72 Sequences
BL00796A, P29358G1B1 BL00796B,
P29358G1B2 BL00796C, P29358G1B4
(28, 33)
150
Catalytic Site of ATP Synthase
P-SAP-LIV-DNH-x(3)-S-x-S
PROSITE PS00152
eBLOCKs P19483G1B2
BLOCKS BL00152F
151
Protein Functional AnalysisUsing BLOCKS or
eBLOCKs
Motifs Significant at an Expectation of 10-4
Red eBLOCKs Black BLOCKS
152
Two Human Protein Sets

• Ensembl - (http//www.ensembl.org)
• 29,304 proteins (Feb 2002) from the human genome
  project
• Based on GenScan Models
• Shorter, more fragmentary protein sequences
• RefSeq (http//www.ncbi.nlm.nih.gov/)
• 21,724 -- curated (XP)
• 11,407 reviewed sequences (NP)
• Based on full length cDNAs
• Longer, more reliable protein sequences

153
eBLOCKs Assignments forRefSeq and ENSEMBL
Proteins
154
Web Access to eBLOCKshttp//eblocks.stanford.edu/
155
An Entry From eBLOCKs http//eblocks.stanford.edu
/
156
Another Entry from eBLOCKs http//eblocksanford.e
du/
157
A Sample Keyword Search http//eblocks.stanford.e
du/
158
Search A Sequence http//eblocks.stanford.edu/
159
Software demos

• Dot Matrices
• http//bioinf.ibun.unal.edu.co/java/dotlet/Dotlet.
  html
• Software Smith-Waterman alignments
• http//motif.stanford.edu/alion/
• Hardware Smith-Waterman alignments
• http//decypher.stanford.edu
• eMotif
• http//motif.stanford.edu/emotif/
• eMatrix
• http//motif.stanford.edu/ematrix/
• 3motif / 3matrix
• http//motif.stanford.edu/3motif/
• http//motif.stanford.edu/3matrix/
• eBlocks
• http//eblocks.stanford.edu/
• LOCK
• http//dlb3.stanford.edu/lock/
• SCOP / PDB
• http//scop.berkeley.edu

160
Conclusion

• Functional Analysis is more important than ever
  with the rate of growth of sequence databases.
• Important for understanding of biology give
  researchers a head start on how to experimentally
  examine proteins.
• Important in pharmaceuticals allows rapid
  discovery of targets.