Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Mapping Proteins to Functions
Part 1
dsdht.wikispaces.com
Points to remember
Proteins are single, unbranched chains of
amino acid monomers.
There are 20 different amino acids
Th...
Proteins Functional Classes

Why do we care about protein
function?
• Diagnose reasons for the disease.
• Discover new dru...
Data used for prediction of protein function
•
•
•
•
•
•

Amino acid sequences
Protein structure
Genome sequences
Phylogen...
The concept of protein function is highly
context-sensitive and not very well-defined.
infact, this concept typically acts...
Characterization on protein function

Molecular function, cellular function and
Phenotypic function are hierarchically
rel...
Gene Ontology classification scheme categorizes
protein function into cellular component,
molecular function and biologica...
GO Format

•
•
•
•
•
•

Figure adapted from [Ashburner et al. 2000])

Wide coverage
Standardized format
Hierarchical struc...
Molecular function
• Molecular function describes activities, such as
catalytic or binding activities, at the molecular le...
Biological process
• A biological process is series of events
accomplished by one or more ordered assemblies
of molecular ...
Cellular component
• A cellular component is just that, a component of
a cell that is part of some larger object
• It may ...
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene
lists using DAVID Bioinformatics Reso...
DAVID (Gene Ontology Enrichment)
Youtube Videos
http://www.youtube.com/watch?v=xIu9mm6b7N0
http://www.youtube.com/watch?v=...
Sequence & Structure based methods
Part2
Basic Set of Protein Annotations

• Protein name
- descriptive common name for the protein
eg. “kinase”
• Gene symbol
-mne...
Sequence Similarity Evidence
• pairwise alignments -two protein’s amino acid sequences aligned next to
each other so that ...
Important terms to understand
• Homologs – two sequences have evolved from the same
common ancestor they may not share sam...
Taken from http://ae.igs.umaryland.edu/docs/FunctionalAnnotApril.pdf
Sequence similarity, sequence homology, and
functional homology

• Sequence similarity means that the sequences
are simila...
Existing Sequence based function prediction methods
Homology based approaches
•
•
•
•

BLAST
FASTA
SSEARCH
PSI-BLAST-itera...
Drawbacks of BLAST and FASTA
• Provide functional annotation typically to half
of the genes in a genome since homologous
s...
Enhanced Sequence based methods
•

PFP – Kihara lab. (http://kiharalab.org/web/pfp.php)

•

The PFP algorithm uses PSI-BLA...
When Homology searches fail
• Sometimes no orthologs or even paralogs can be
identified by sequence similarity searches, o...
Protein domains
• Many eukaryotic proteins consist of multiple
globular domains that can fold independently
• These domain...
Which domain resource should I use?
• SMART is focused on signal transduction domains
• Pfam is very actively developed an...
Function prediction from post translational
modifications
• Proteins with similar function may
not be related in sequence
...
The concept of ProtFun

http://www.cbs.dtu.dk/services/ProtFun/
Function prediction on the
human prion sequence
############## ProtFun 1.1 predictions ##############
>PRIO_HUMAN
# Functi...
ProtFun data sets
• Labeling of training and test data
– Cellular role categories: human SwissProt sequences
were categori...
Structure based methods

Three standard databases dominate the structure data
landscape:
PDB-Structure data from NMR and ,...
Structure based methods
Protein Folds Super Secondary Structures

Biological function

Adapted from Martin 1998. Protein f...
Approaches for deriving functional information from 3D
structure

Adapted from From Structure to function.Thorton etal, 20...
http://www.jove.com/video/3259/a-protocol-forcomputer-based-protein-structure-function
ProFunc server methods
•
•
•
•
•
•
•
•
•
•
•
•
•

Sequence-based methods:
BLAST search against the UniProt Knowledgebase.
...
References
• http://www.sciencedirect.com/science/article/pii/S0022283698921
441
• http://www.nature.com/ng/journal/v25/n1...
Mapping protein to function
Mapping protein to function
Mapping protein to function
Mapping protein to function
Mapping protein to function
Mapping protein to function
Upcoming SlideShare
Loading in …5
×

Mapping protein to function

3,846 views

Published on

Published in: Education, Technology
  • Be the first to comment

Mapping protein to function

  1. 1. Mapping Proteins to Functions Part 1 dsdht.wikispaces.com
  2. 2. Points to remember Proteins are single, unbranched chains of amino acid monomers. There are 20 different amino acids There are four levels of protein structureprimary,secondary,tertiary and quaternary. A protein’s amino acid sequence determines its three-dimensional structure (conformation).
  3. 3. Proteins Functional Classes Why do we care about protein function? • Diagnose reasons for the disease. • Discover new drugs. • Understand Mechanism of action of processes in the system.
  4. 4. Data used for prediction of protein function • • • • • • Amino acid sequences Protein structure Genome sequences Phylogenetic data Microarray expression data Protein interaction networks and protein complexes • Biomedical literature
  5. 5. The concept of protein function is highly context-sensitive and not very well-defined. infact, this concept typically acts as an umbrella term for all types of activities that a protein is involved in, be it cellular, molecular or physiological.
  6. 6. Characterization on protein function Molecular function, cellular function and Phenotypic function are hierarchically related. Predicting function: from genes to genomes. Bork etal 1998.
  7. 7. Gene Ontology classification scheme categorizes protein function into cellular component, molecular function and biological process. In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about entities Read more at http://www.answers.com/topic/ontology-computer-science http://www.nature.com/ng/journal/v25/n1/full/ng0500_25.html http://www.geneontology.org/
  8. 8. GO Format • • • • • • Figure adapted from [Ashburner et al. 2000]) Wide coverage Standardized format Hierarchical structure Disjoint Categories Multiple functions Dynamic nature
  9. 9. Molecular function • Molecular function describes activities, such as catalytic or binding activities, at the molecular level • GO molecular function terms represent activities rather than the entities that perform the actions, and do not specify where or when, or in what context, the action takes place • Examples of broad functional terms are catalytic activity or transporter activity; an example of a narrower term is adenylate cyclase activity
  10. 10. Biological process • A biological process is series of events accomplished by one or more ordered assemblies of molecular functions • An example of a broad GO biological process terms is signal transduction; examples of more specific terms are pyrimidine metabolism or alpha-glucoside transport. • It can be difficult to distinguish between a biological process and a molecular function.
  11. 11. Cellular component • A cellular component is just that, a component of a cell that is part of some larger object • It may be an anatomical structure (for example, the rough endoplasmic reticulum or the nucleus) or a gene product group (for example, the ribosome, the proteasome or a protein dimer) • The cellular component categories are probably the best defined categories since they correspond to actual entities
  12. 12. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009;4(1):44-57.
  13. 13. DAVID (Gene Ontology Enrichment) Youtube Videos http://www.youtube.com/watch?v=xIu9mm6b7N0 http://www.youtube.com/watch?v=zedjRViji2c Try out the microarray list given below for analyzing Proteins. 31741_at 31734_at 32696_at 37559_at 41400_at 35985_at 39304_g_at 41438_at 35067_at 32919_at 35429_at 36674_at 967_g_at 36669_at 39242_at 39573_at 39407_at 33346_r_at 40319_at 2043_s_at 1788_s_at 36651_at 41788_i_at 35595_at 36285_at 39586_at 35160_at 39424_at 36865_at 2004_at 36728_at 37218_at 40347_at 36226_r_at 33012_at 37906_at 32872_at
  14. 14. Sequence & Structure based methods Part2
  15. 15. Basic Set of Protein Annotations • Protein name - descriptive common name for the protein eg. “kinase” • Gene symbol -mnemonic abbreviation for the gene - eg “recA” • EC number -what the protein is doing in the cell and why -eg “involved in glycolysis” • Supporting evidence - accession numbers of BER and HMM matches - whatever information you used to make the annotation • Unique Identifier - eg locus ids
  16. 16. Sequence Similarity Evidence • pairwise alignments -two protein’s amino acid sequences aligned next to each other so that the maximum number of amino acids match • Multiple alignment - 3 or more amino acid sequences aligned to each other so that the maximum number of amino acids match in each column • Protein families - clusters of proteins that all share sequence similarity and presumably similar function • Motifs -short regions of amino acid sequence shared by many proteins. A motif can be found in number of different proteins where it carries out similar functions.
  17. 17. Important terms to understand • Homologs – two sequences have evolved from the same common ancestor they may not share same function • Orthologs – a type of homolog where two sequences are in different species that arose from a common ancestor. Speciation have created the tow copies of the sequence. • Paralogs- a type of homolog where the two sequences have arisen due to a gene duplication within one species.They initially have the same function but as time goes byone copy will be free to evolve new functions, as the other copy will maintain the original function. • Xenologs – a type of ortholog where two gene sequences have arisen due to horizontal transfer (by means of reproduction)
  18. 18. Taken from http://ae.igs.umaryland.edu/docs/FunctionalAnnotApril.pdf
  19. 19. Sequence similarity, sequence homology, and functional homology • Sequence similarity means that the sequences are similar – no more, no less • Sequence homology implies that the proteins are encoded by genes that share a common ancestry. • Functional homology means that two proteins from two organisms have the same function. • Sequence similarity or sequence homology does not guarantee functional homology
  20. 20. Existing Sequence based function prediction methods Homology based approaches • • • • BLAST FASTA SSEARCH PSI-BLAST-iterates searches by using a sequence profile computed from a multiple sequence alignment obtained from the search from the previous round. Subsequence based approaches • Motifs and domains http://molbiol-tools.ca/Motifs.htm Feature based approaches • normalized Van der Waals volume, polarity, charge and surface tension, which are averaged over all the residues to in the sequence obtain the feature-value vector for the protein to train a classifier • SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi)
  21. 21. Drawbacks of BLAST and FASTA • Provide functional annotation typically to half of the genes in a genome since homologous sequences are not found at accepted significance thresholds. • Automated methods of annotation transfer between similar sequences contribute to error propagation.
  22. 22. Enhanced Sequence based methods • PFP – Kihara lab. (http://kiharalab.org/web/pfp.php) • The PFP algorithm uses PSI-BLAST (version 2.2.6) to predict probable GO function annotations in three categories—molecular function, biological process, and cellular component—with statistical significance scores (Pvalue) For each sequence retrieved by PSI-BLAST ,the associated GO terms are scored. GO terms are scored according to a) frequency of association to similar sequences b) degree of similarity those sequences share with the query • • where s(fa) is the final score assigned to the GO term fa, N is the number of similar sequences retrieved by PSIBLAST, Nfunc(i) is the number of GO terms annotating sequence i, E_value(i) is the E-value given to the sequence i, fj is a GO term annotating sequence i, and b is the constant value, 2 = (log10100), which keeps the score positive. P(fa|fj) is the association score for fa given fj obtained from the function association matrix (FAM). c(fa, fj) is number of times fa and fj are assigned simultaneously to each sequence in UniProt, and c(fj) is the total number of times fj appeared in Uni- Prot, l is the size of one dimension of the FAM (i.e. the total number of unique GO terms), and ε is the pseudocount.
  23. 23. When Homology searches fail • Sometimes no orthologs or even paralogs can be identified by sequence similarity searches, or they are all of unknown function. • No functional information can thus be transferred based on simple sequence homology • By instead analyzing the various parts that make up the complete protein, it is nonetheless often possible to predict the protein function
  24. 24. Protein domains • Many eukaryotic proteins consist of multiple globular domains that can fold independently • These domains have been mixed and matched through evolution • Each type of domain contributes towards the molecular function of the complete protein • Numerous resources are able to identify such domains from sequence alone using HMMs
  25. 25. Which domain resource should I use? • SMART is focused on signal transduction domains • Pfam is very actively developed and thus tends to have the most up-to-date domain collection • InterPro is useful for genome annotation since the domains are annotated with GO terms • CDD is conveniently integrated with the NCBI BLAST web interface
  26. 26. Function prediction from post translational modifications • Proteins with similar function may not be related in sequence • Still they must perform their function in the context of the same cellular machinery • Similarities in features such like PTMs and physical/chemical properties could be expected for proteins with similar function
  27. 27. The concept of ProtFun http://www.cbs.dtu.dk/services/ProtFun/
  28. 28. Function prediction on the human prion sequence ############## ProtFun 1.1 predictions ############## >PRIO_HUMAN # Functional category Amino_acid_biosynthesis Biosynthesis_of_cofactors Cell_envelope Cellular_processes Central_intermediary_metabolism Energy_metabolism Fatty_acid_metabolism Purines_and_pyrimidines Regulatory_functions Replication_and_transcription Translation Transport_and_binding Prob 0.020 0.032 0.146 0.053 0.130 0.029 0.017 0.528 0.013 0.020 0.035 => 0.831 Odds 0.909 0.444 2.393 0.726 2.063 0.322 1.308 2.173 0.081 0.075 0.795 2.027 # Enzyme/nonenzyme Enzyme Nonenzyme Prob 0.250 => 0.750 Odds 0.873 1.051 Prob 0.070 0.031 0.057 0.020 0.010 0.017 Odds 0.336 0.090 0.180 0.426 0.313 0.334 # Enzyme class Oxidoreductase Transferase Hydrolase Isomerase Ligase Lyase (EC (EC (EC (EC (EC (EC 1.-.-.-) 2.-.-.-) 3.-.-.-) 4.-.-.-) 5.-.-.-) 6.-.-.-)
  29. 29. ProtFun data sets • Labeling of training and test data – Cellular role categories: human SwissProt sequences were categorizes using EUCLID – Enzyme categories: top-level enzyme classifications were extract from human SwissProt description lines – Gene Ontology terms were transferred from InterPro • The sequences were divided into training and test sets without significant sequence similarity • Binary predictors were for each category
  30. 30. Structure based methods Three standard databases dominate the structure data landscape: PDB-Structure data from NMR and ,X-ray SCOP- organizes the available structures in a hierarchy so as to elicit the evolutionary relationships between them.Family, Superfamily and Fold CATH-(Class, Architecture, Topology and Homologous superfamily)
  31. 31. Structure based methods Protein Folds Super Secondary Structures Biological function Adapted from Martin 1998. Protein folds and functions
  32. 32. Approaches for deriving functional information from 3D structure Adapted from From Structure to function.Thorton etal, 2000,Nature .
  33. 33. http://www.jove.com/video/3259/a-protocol-forcomputer-based-protein-structure-function
  34. 34. ProFunc server methods • • • • • • • • • • • • • Sequence-based methods: BLAST search against the UniProt Knowledgebase. FASTA search against sequences of structures in the Protein Data Bank. InterProScan Superfamily search Residue conservation mapped onto structure Genome location analysis Structure-based methods: Fold matching using MSDfold and DALI Helix-Turn-Helix motif search Nest analysis Surface clefts analysis Template methods Enzyme active sites Ligand binding sites DNA binding sites Reverse template search
  35. 35. References • http://www.sciencedirect.com/science/article/pii/S0022283698921 441 • http://www.nature.com/ng/journal/v25/n1/full/ng0500_25.html • http://www.nature.com/nprot/journal/v4/n1/full/nprot.2008.211.h tml • http://www.nature.com/nprot/journal/v4/n1/full/nprot.2008.211.h tml • http://www.cs.helsinki.fi/bioinformatiikka/mbi/courses/0708/itb/slides/itb0708_slides_83-116.pdf (BLAST and FASTA) • http://kiharalab.org/web/paper/HawkinsChitaleLubanKihara_Protei ns09.pdf • http://www.ebi.ac.uk/thorntonsrv/databases/profunc/doc/profunc_tutorial.pdf

×