Rockburst Laboratory Tests Database- Application of Data Mining Techniques
Mining proteomes for short motifs (possible potential as bioactive peptides)
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Mining proteomes for short motifs (possible potential as bioactive peptides) • Proteomes – Man – pathogens – food organisms • Computation – Evolutionary conservation – Evolutionary convergence – Predicting SLiM-like properties
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Mining proteomes for short motifs (possible potential as bioactive peptides). Proteomes Man pathogens food organisms Computation Evolutionary conservation Evolutionary convergence Predicting SLiM -like properties. Short linear motifs SLiMPRED predictor. - PowerPoint PPT Presentation
Transcript of Mining proteomes for short motifs (possible potential as bioactive peptides)
Mining proteomes for short motifs(possible potential as bioactive peptides)
• Proteomes – Man– pathogens – food organisms
• Computation– Evolutionary conservation– Evolutionary convergence– Predicting SLiM-like properties
Short linear motifs SLiMPRED predictor
α-Helix β-Sheet Polyproline IILIG_EH1_1 LIG_Dynein_DLC8_1 LIG_CAP-Gly_1LIG_GLEBS_BUB3_1 LIG_PDZ_1 LIG_SH3_1LIG_IQ LIG_PP1 LIG_SH3_2LIG_MDM2 LIG_PP2B_1 LIG_SH3_3LIG_NRBOX LIG_SH2_GRB2 LIG_SH3_5LIG_Sin3_1 LIG_SH2_SRC LIG_TRAF2_1LIG_Sin3_3 LIG_SH2_STAT3 LIG_TRAF6
LIG_SH2_STAT5 LIG_WW_1LIG_SIAH_1LIG_TRFH_1LIG_WRPW_1
Restricted training set to protein-binding motifs including:
Training a short linear motif predictor (SLiMPred)
α-Helix β-Sheet Polyproline II Othersequences 30 49 30 141Unique ELMs 7 11 8 31SLiM residues 387 324 218 1239197,410 Non-SLiM residues
Most motifs lie in disordered regions of proteinsExisting predictor ANCHOR predicts protein-binding within disordered regions
SLiMPred (blue) v ANCHOR (red)
Alpha-helix Beta-sheet
Polyproline-II helix Other
SLIMPred has some predictive ability in ordered regions too
Disordered regions
Ordered regions
SLiMPred: predicting motif-like regions along a protein
DisorderSLIMPredRelative LocalConservation
http://bioware.ucd.ie Mooney et al J Mol Biol (2012) 415:193-204
Which kinds of interactions should we use in searching for novel motifs?
ALL Yeast 2 hybrid
complex
• http://bioinfo-casl.ucd.ie/empa/programberlin/2-uncategorised/52
Potential workflows to identify novel peptides from proteins
• Conservation analysis• SLiMPrints
• Convergent evolution analysis • SLiMFinder
• Extracellular peptides • PeptideRanker
• Intracellular peptides• SLiMPred• ANCHOR
• Known structure of protein ligand, candidate peptide sequence• Pepsite (Trabuco et al Nucleic Acids Res. 2012)
• Known structure with linear peptide in complex• Predict cyclised peptide mimetic (CYCLOPS virtual library; Duffy et al 2012).
SLiMFinder human known versus the newKnown true positive motifs are discovered With variations in many protein interaction sets
Novel motifs are much sparser, often only discovered once
• All the human motif discovery results are available in an online searchable database (search on genes or motifs) bioware.soton.ac.uk/slimdb
