Transcript MBG305_LS_10

Applied Bioinformatics
Week 10
Theory I
• Protein Sequences
• Protein Families
• Protein Domains
• Computer Learning
– Garbage in -> Garbage out
• Prediction based on learned Examples
Protein Sequence
• Primary Sequence consisting of 20 amino acids
• Secondary Structure consists of 3 types
– Helix – Strand – Coil
• Tertiary structure Combinations of
secondary structures
– Unlimited number of combinations possible
– But limited number of motives found
– Architectures are build hierarchicaly
• Quaternary structure
– AKA protein-protein interactions are not
part of this course
http://njms2.umdnj.edu/biochweb/education/bioweb/PreK2010/AminoAcids.htm
http://www.usermeds.com/medications/amino-acids
http://www.weightlossandnutritionsecrets.com/all-about-amino-acids/
PRIDE
• The PRIDE PRoteomics IDEntifications database is a
centralized, standards compliant, public data
repository for proteomics data
• It contains experimental evidence for its entries
• http://www.ebi.ac.uk/pride//
Protein Sequences
• Swissprot = UniProtKB
– http://www.expasy.ch/sprot
– http://www.ebi.ac.uk/swissprot/
• As in Genebank for nucleotide sequences we need
a unique identifier for each protein sequence
• Let’s look at EBI now
UniProtKB
• The Universal Protein Resource (UniProt) is
a comprehensive resource for protein
sequence and annotation data. (KB:
Knowledge Base)
• Often manually reviewed and annotated
information
UniProt
Including splice
variants and isoforms
Protein Information
Clicking on the Member name (Accession Number)
will provide detailed information about the protein
Protein Information
Protein Information
Protein Information
Protein Information
Protein Information
Protein Information
Protein Information
Machine Learning
• For example clustering
• UniRef90
• UniRef50
Learning
• Many Facts -> Rules/Knowledge
• Learning = Deducing rules from facts
• Computer/Machine learning?
– Same idea
Computer Learning
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Neural Networks
Support Vector Machines
Naive Bayes Classifiers
Self Organizing Maps
Decision Trees
• And many other algorithms
Data
• Training data needs to be chosen carefully
– Example sub cellular targeting of proteins
• What needs to be predicted?
– Localization
– Leader peptide cleavage site
• Where does the data come from
– Best would be sequences validated by experimental
results
• How many?
– Difficult to answer this one
– More is good, but rare events will not be learned well
– Better is manual editing choosing many possibilities
and not over representing some of them in the dataset
Data
• Yes! preparing the dataset is crucial and
takes most of the time
• Applying the learner will not take long
• All outcomes of the samples need to be
known (target, cleavage site)
– Negative examples are just as important
• Divide the dataset into two parts
– One will be used for learning
– The other for validating the learned rules
Validation
• The dataset can be automatically divided
into different training and validation sets
• This can be performed many times and the
best result (rule set) can later be used to
predict new sequences
• That’s machine learning in brief
• We just touched the surface of it
Classification General Idee
Practical Considerations
• You want to predict the sub cellular target of a
protein
– Which species are you working with?
– Which species did the training data come from?
– You can try a few known examples
• Read the publication
– How precise is the prediction
– For localization
– For prediction of the leader peptide
• If possible, try different approaches
Clustering (Machine Learning)
• Basically same idea as in MSA
– Similar sequences are aligned first
– Similar datasets are clustered first
• The initial clusters are combined into super
clusters (hierarchical clustering)
– Similar to forming a guide tree
• New measurements can be assigned to known
clusters
– Information can be inferred
Protein Families
• Based on
– Clusters of protein sequences
– Domains (basically blocks of above)
• Many domains are annotated
– Good place to find these is
– http://www.ebi.ac.uk/InterProScan
Practice I
Protein Information
• In many cases we would like to get
additional information about a protein
– Molecular mass
– pI
– Subcellular targeting
• http://www.expasy.org/tools
– Many calculations, etc for proteins
Tools at Expasy
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Prediction/ Characterizing Tools
Pattern and Profile searches
PTM predictions
Topology Prediction
Structure
– Primary (Analysis)
– Secondary (Prediction)
– Tertiary (Prediction, Analysis)
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Protein Information
Localization
• You want to predict the sub cellular localization
of a protein
Let’s tackle this problem
• Get a protein from swissprot
– O82533 (Gene: AtFtsZ2-1)
• Annotation: Chloroplast targeting
• Try a few prediction tools to see if you can
confirm the annotation
Localization Prediction
• Choose tools from Expasy for example
• ChloroP
• SignalP
• Predotar
Theory II
Substitution Matrices
First Substitution Matrices
Substitution Matrices
Sequence relationships may be hidden by
changes in sequence
Mutations
Evolution
Approximate matches are needed
Selectionist Model
Some mutations are neutral
Not disturbing the function much
Not disturbing the structure much
These accumulate over time (evolution)
Some mutations are disruptive
L <> Q
Frameshift insertions or deletions
More elaborate Matrices
Format
Table 20 X 20
Probability of change for each combination
Symmetric
190 distinct entries + 20
Examples
Unitary
GCM
BLOSUM
PAM
Genetic Code Matrix
Considers the minimum number of base changes
(0,1,2,3)
Are amino acids different in only one base
chemically significantly different?
Not a very good matrix
Although mutation on the genetic level
Selection is on the protein level
A priori
Example
Jukes Cantor Model
Amino Acid Substitutions
A priori
driven by amino acid properties
Size
Hydrophobicity
Charge
...
Determined from example
PAM matrices
Percent Accepted Mutation: Unit of
evolutionary change for protein sequences
[Dayhoff78].
A PAM unit is the amount of evolution that
will on average change 1% of the amino
acids within a protein sequence.
http://www.cs.iastate.edu/~cs544/Lectures/PAM_matrices.ppt
PAM matrices: Assumptions
Only mutations are allowed
Sites evolve independently
Evolution at each site occurs according to a simple
(“first-order”) Markov process
Next mutation depends only on current state and is
independent of previous mutations
Mutation probabilities are given by a substitution
matrix M = [mXY], where mxy = Prob(X Y
mutation) = Prob(Y|X)
http://www.cs.iastate.edu/~cs544/Lectures/PAM_matrices.ppt
The PAM Family
Define a family of substitution matrices —
PAM 1, PAM 2, etc. — where PAM n is used to
compare sequences at distance n PAM.
PAM n = (PAM 1)n
Do not confuse with scoring matrices!
Scoring matrices are derived from PAM
matrices to yield log-odds scores.
http://www.cs.iastate.edu/~cs544/Lectures/PAM_matrices.ppt
Generating PAM matrices
Idea: Find amino acids substitution statistics by comparing evolutionarily
close sequences that are highly similar
Easier than for distant sequences, since only few insertions and deletions took
place.
Computing PAM 1 (Dayhoff’s approach):
Start with highly similar aligned sequences, with known evolutionary trees
(71 trees total).
Collect substitution statistics (1572 exchanges total).
Let mij = observed frequency (= estimated probability) of amino acid Ai
mutating into amino acid Aj during one PAM unit
Result: a 20× 20 real matrix where columns add up to 1.
http://www.cs.iastate.edu/~cs544/Lectures/PAM_matrices.ppt
Dayhoff’s PAM matrix
All entries  104
http://www.cs.iastate.edu/~cs544/Lectures/PAM_matrices.ppt
Calculate a substitution
frequency matrix
A
A
MV
PAM250 (log ods)
BLOSUM matrices
Blocks Substitution Matrix. Scores for each
position are obtained frequencies of
substitutions in blocks of local alignments
of protein sequences [Henikoff &
Henikoff92].
For example BLOSUM62 is derived from
sequence alignments with no more than
62% identity.
http://www.cs.iastate.edu/~cs544/Lectures/BLOSUM_matrices.ppt
BLOSUM Scoring Matrices
BLOck SUbstitution Matrix
Based on comparisons of blocks of sequences derived from
the Blocks database
The Blocks database contains multiply aligned ungapped
segments corresponding to the most highly conserved
regions of proteins (local alignment versus global
alignment)
BLOSUM matrices are derived from blocks whose alignment
corresponds to the BLOSUM-,matrix number
http://www.cs.iastate.edu/~cs544/Lectures/BLOSUM_matrices.ppt
Conserved blocks in alignments
AABCDA...BBCDA
DABCDA.A.BBCBB
BBBCDABA.BCCAA
AAACDAC.DCBCDB
CCBADAB.DBBDCC
AAACAA...BBCCC
http://www.cs.iastate.edu/~cs544/Lectures/BLOSUM_matrices.ppt
Constructing BLOSUM r
To avoid bias in favor of a certain protein, first eliminate
sequences that are more than r% identical
The elimination is done by either
removing sequences from the block, or
finding a cluster of similar sequences and replacing it by a new
sequence that represents the cluster.
BLOSUM r is the matrix built from blocks with no more the
r% of similarity
E.g., BLOSUM62 is the matrix built using sequences with no more
than 62% similarity.
Note: BLOSUM 62 is the default matrix for protein BLAST
http://www.cs.iastate.edu/~cs544/Lectures/BLOSUM_matrices.ppt
Comparison
PAM is based on an evolutionary model using
phylogenetic trees
BLOSUM assumes no evolutionary model,
but rather conserved “blocks” of proteins
http://www.cs.iastate.edu/~cs544/Lectures/BLOSUM_matrices.ppt
Equivalent PAM and Blossum
matrices (according to H)
PAM100 ==> Blosum90
PAM120 ==> Blosum80
PAM160 ==> Blosum60
PAM200 ==> Blosum52
PAM250 ==> Blosum45
http://www.cs.iastate.edu/~cs544/Lectures/BLOSUM_matrices.ppt
PAM versus Blosum
Source: http://www.csit.fsu.edu/~swofford/bioinformatics_spring05/lectures/lecture03-blosum.pdf
Practice II
InterProScan
• Advantages
– Meta predictor
– Uses many tools
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BlastProDom
HMMPfam
HMMTigr
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http://genome.cshlp.org/content/12/1/47/F1.expansion
– Returns all results for your analysis
InterProScan
• http://www.ebi.ac.uk/InterProScan
Which Sequence
• Go to swissprot
• Select a sequence of interest
– E.g.: a translocase
– Should have some annotated function
• Paste the FASTA sequence
• Run InterProScan
InterProScan Results
So?
• First glance
– The colored highlights over the sequence are
domains
– The more the merrier
– None? New protein? Be happy!
• Boxes refer to a record in InterPro database
• The IPR… link summarizes the results
Summary
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Look at the IPR summary, if any
Select Table: For all matching proteins
Select the FASTA option on the following page
Add your original sequence to the FASTA coll.
Make an MSA
Protein Domains
• Use the same sequence
– Do the same analysis using NCBI CD server
– http://www.ncbi.nlm.nih.gov/Structure/cdd/wrp
sb.cgi
• NCBI may have domains that InterScanPro
doesn’t have and vice versa
CD Server
See the difference
More to test
• http://www.expasy.org/tools/#pattern
– Try
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Hits
HamapScan
SMART
ScanProsite (if time allows)