Protein Sequence Analysis

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Protein Sequence Analysis

• By
• Rashmi Shrivastava
• Lecturer
• School of Biotechnology
• Devi Ahilya Vishwavidyalaya
• Indore

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Introduction

• Genomes of most organism have been deciphered.
• Further step is to identify key regions,
  speciallly protein coding regions.
• Assigning functions to individual proteins
• Predicting molecular structures of the proteins.
• Developing protein interaction network.
• Utilizing the information obtained for structure
  based drug design, discovering new drug targets,
  Creating mutations to alter properties/ create
  desired property in proteins and so
  on...............

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• By themselves the letters(amino acid sequence/
  genome sequence) have no meaning.
• our aim is to create sentence------proteins
• words--------
  motifs (recognize patterns and signatures)
• To investigate the meaning of sequences there are
  two approaches-
• pattern recognition techiques- detect
  similarity between sequences.
• ab initio prediction methods-prediction of
  structure and thus the function

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Protein databases(The source of information)

• Primary and Secondary databases
• Primary sequence databases-
• Entrez-protein
• PIR- Developed at NBRF
• Swiss-Prot
• TrEMBL

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Secondary database

• -Results of analysis of primary databases
• -PROSITE/InterPro-protein families characterized
  by presence of single most conserved motif
  (domains) by multiple sequence alignment
• -PRINTS-protein families are characterized by
  several conserved motifs to develop a fingerprint
  or signature for a particular family.
• BLOCKS and Pfam
• Profiles-variable regions between conserved
  motifs contain information about insertions and
  deletionsdistant sequence relationship
• Enzyme and KEGG- Functional classification

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Structure classification databases

• SCOP(Structural classification of proteins)
  classify on Hierarchy Family, superfamily and
  fold
• CATH(Class, Architecture, Topology,
  Homology)-Hierarchial domain classification of
  proteins
• C-gross secondary structure content
• A- Arrangement of secondary elemnts
• T-Overalll shape and connectivity
• H- gt 35 sequence identity
• Protein Data Bank (PDB)

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• Sequence alignment

Pair wise
Multiple
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Pair wise Sequence Alignment
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Sequence alignment
Global Sequence Alignment
Local sequence alignment
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Algorithm

• Global sequence alignment-
• Needleman Wunch
• Local Sequence alignment-
• Smith Waterman

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Identity Similarity In alignment the sequence
which is already in database is known as Subject
and the sequence for which the alignment is
going on is termed as query or probe sequence. If
the aligned Probe residue is same with the
Subject residue then it is identical but if they
are of same nature (Glutamate Aspartate) then
they are similar.
VLSPADKTNVKAAWGKVGAHAGYEG
.
VLSEGEWQLVLHVWAKVEADVAGHG Total
Residue 25 Identical Residue 09 Similar
(not identical)01 Gap00 Percent Similarity
40.000 ( and .) (Identity similarity) Percent
Identity 36.000 ( only)
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Alignment

• ATCAGAGTC
• 
  
• TTC----AGTC
• ATCAGAGTC
• TTCAG----TC
• ATCAGAGTC
• TTCA----GTC

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Aligning Sequences.
actaccagttcatttgatacttctcaaa
Sequence 1 Sequence 2
taccattaccgtgttaactgaaaggacttaaagact
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Gap Insertion
V K L A W A A K G N E A A P A K A A V D H Y V A
A
V K A W A A K G N E A E G L S A A P D J K V A A P
Total Residue 25 Identical Residue 04
Gap00 Percent Identity 16.00
V K L A W A A K G N E A A P A K A A V D H Y V A A
V K _ A W A A K G N E A E G L S A A P D J K V A
A
Total Residue 25 Identical Residue 18
Gap01 Percent Identity 72.00
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Scoring System
Proteins can differ in close organisms. Some
substitutions are more frequent than other
substitutions. Chemically similar amino acids
can be replaced without severely effecting the
proteins function and structure
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Matrices formed to score alignment

• Sparse Matrices
• Based on identical residue matching
• Problem Faced
• Diagnostic power is relatively poor, as all the
• identical matches carry equal weighting
• 2. Mathematically significant but biologically
• insignificant.

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To solve this problem
Scoring matrices has been devised that weight
matches between non identical residues,
according to observed substitution rates across
large evolutionary distances. This scoring
matrices are mathematically insignificant
but biologically significant specially for
aligning sequences of very low identity.
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Percent Accepted Mutation (PAM or Dayhoff)
Matrices

• Similar sequences organized into phylogenetic
  trees
• Number of amino acid changes counted
• Relative mutabilities evaluated
• 20 x 20 amino acid substitution matrix calculated

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• PAM 1 1 accepted mutation event per 100 amino
  acids PAM 250 250 mutation events per 100
• PAM 1 matrix can be multiplied by itself N times
  to give transition matrices for sequences that
  have undergone N mutations

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• Derived from global alignments of closely related
  sequences.
• Matrices for greater evolutionary distances are
  extrapolated from those for lesser ones.
• The number with the matrix (PAM40, PAM100) refers
  to the evolutionary distance greater numbers are
  greater distances.
• Does not take into account different evolutionary
  rates between conserved and non-conserved
  regions.

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PAM 1
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PAM 250
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Scoring
A K W T N L K - - - - W A K V - A D V A G H
- G
A K - T N V KA K L P W G K V G G H V A G E Y G

• The score of the alignment in this system is
• -Matrix value at (A,A) (K,K) (T,T) (K,K)
  (W,W) (A,G)
• (penalty for gap insertion/deletion)gap
• - (penalty for gap extension)(total length of
  all gaps)

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• Henikoff, S. Henikoff J.G. (1992)
• Use blocks of protein sequence fragments from
  different families (the BLOCKS database)
• Amino acid pair frequencies calculated by summing
  over all possible pairs in block
• Different evolutionary distances are incorporated
  into this scheme with a clustering procedure
  (identity over particular threshold same
  cluster)

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• Target frequencies are identified directly
  instead of extrapolation.
• Sequences more than x identitical within the
  block where substitutions are being counted, are
  grouped together and treated as a single sequence
• BLOSUM 50 gt 50 identity
• BLOSUM 62 gt 62 identity

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BLOSUM

• A 4
• B -2 6
• C 0 -3 9
• D -2 6 -3 6
• E -1 2 -4 2 5
• F -2 -3 -2 -3 -3 6
• G 0 -1 -3 -1 -2 -3 6
• H -2 -1 -3 -1 0 -1 -2 8
• I -1 -3 -1 -3 -3 0 -4 -3 4
• K -1 -1 -3 -1 1 -3 -2 -1 -3 5
• L -1 -4 -1 -4 -3 0 -4 -3 2 -2 4
• M -1 -3 -1 -3 -2 0 -3 -2 1 -1 2 5
• N -2 1 -3 1 0 -3 0 1 -3 0 -3 -2 6
• P -1 -1 -3 -1 -1 -4 -2 -2 -3 -1 -3 -2 -2 7
• Q -1 0 -3 0 2 -3 -2 0 -3 1 -2 0 0 -1 5
• R -1 -2 -3 -2 0 -3 -2 0 -3 2 -2 -1 0 -2 1
  5
• S 1 0 -1 0 0 -2 0 -1 -2 0 -2 -1 1 -1 0
  -1 4
• T 0 -1 -1 -1 -1 -2 -2 -2 -1 -1 -1 -1 0 -1 -1
  -1 1 5
• V 0 -3 -1 -3 -2 -1 -3 -3 3 -2 1 1 -3 -2 -2
  -3 -2 0 4

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Thumb rules
Lower PAMs and higher Blosums find short local
alignment of highly similar sequences. Higher
PAMs and lower Blosums find longer weaker local
alignment.
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PAM vs. BLOSUM
Based on the basic assumptions and the
construction of each matrix PAM model is
designed to track evolutionary origin of
proteins. Blosum model is designed to find
conserved domains of proteins.
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Protein Structure

• Primary structure- The linear sequence
• of amino acids in a protein molecule
• Secondary structure- regions of local
• regularity within a protein fold (a
  helices, ß strands, turns etc)
• Super secondary structure- the arrangement of a
  helices and/or ß strands, into discrete folding
  units (ß-barrels, ß aß- units, greek key motifs
  etc.)
• Tertiary structure-The overall fold of a protein
  sequence formed by packing of its secondary
  and/or super- secondary structure elements.
• Quaternary structure- Arrangement of separate
  protein chains in a protein molecule

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From the Primary sequence to protein properties

• Predicting protein localization/ secretory nature
  by the presence of signal peptide and
  localization signal
• Transmembrane helix prediction to identify
  membrane proteins
• Calculation of physiochemical properties-pI, Mwt.
  
• Identification of coiled coiled regions

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Post translational modification prediction
www.expasy.org
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Kyte-Doolitle hydrophobicity plot

• Nature of amino acids- hydrophilic or Hydrophobic
• A window of 9-20 a,.a taken
• A value greater than 0 means hydrophobic

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From Sequence to Structure

• Secondary structure prediction- GOR, Predict
  protein, nnpredict
• Domain Prediction- SBASE, PRODOM

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Importance of protein secondary structure
prediction
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Basis of Secondary structure prediction

• Conservation in the multiple sequence alignment
• Hidden Markov Models and Neural networks
• 70-80 accuracy is achieved.

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Method used
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Key features of secondary structure prediction
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Chou Fasman Algorithm
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GOR
Multiple Sequence
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Some sites

• Predator