Proteome Analyst Transparent High-throughput Protein Annotation: Function, Localization and Custom Predictors

Proteome Analyst Duane Szafron, Paul Lu, Russell Greiner, David Wishart, Zhiyong Lu, Brett Poulin, Roman Eisner, John Anvik,Cam Macdonell

Proteome Analyst Proteome one of many ‘-omes’ set of all proteins in an organism Analysis prediction of protein function or localization from sequence data

Analyze a Protein We have examples of annotated proteins in various protein classes. We have more examples of unannotated proteins.

Analyze a Protein We have examples of annotated proteins in various protein classes. We have more examples of unannotated proteins. What do we do?

Analyze a Protein We have examples of annotated proteins in various protein classes. We have more examples of unannotated proteins. What do we do? Find homologues to each protein and assume similar function.

Analyze a Protein We have examples of annotated proteins in various protein classes. We have more examples of unannotated proteins. What do we do? Find homologues to each protein and assume similar function. Find characteristics of each protein that affect function.

Analyzing Proteins One Protein?

Analyzing Proteins One Protein? Just do it.

Analyzing Proteins One Protein? Just do it. 5 Proteins?

Analyzing Proteins One Protein? Just do it. 5 Proteins? Post-doc familiar with protein classes.

Analyzing Proteins One Protein? Just do it. 5 Proteins? Post-doc familiar with protein classes. 50 Proteins?

Analyzing Proteins One Protein? Just do it. 5 Proteins? Post-doc familiar with protein classes. 50 Proteins? grad student

Analyzing Proteins One Protein? Just do it. 5 Proteins? Post-doc familiar with protein classes. 50 Proteins? grad student 5000 proteins?

Analyzing Proteins One Protein? Just do it. 5 Proteins? Post-doc familiar with protein classes. 50 Proteins? grad student 5000 proteins? summer students

Proteome Analyst

High-throughput Transparent Prediction of Protein Function Protein Localization Custom Classification

Machine Learning Task Training INPUT: sequences, classes OUTPUT: Classifier Analysis INPUT: sequences, Classifier OUTPUT: classes

Machine Learning Task Training INPUT: sequences, classes OUTPUT: Classifier Analysis INPUT: sequences, Classifier OUTPUT: classes, explanation

Training INPUT sequences, classes PA Tools sequences  features ML Algorithm features, classes  Classifier OUTPUT Classifier

Training: INPUT >class A<Training Seq 1 MVGSGLLWLALVSCILTQASAVQRGYGN PIEASSYGL... >class B<Training Seq 2 LLDEPFRSTENSAGSQGCDKNMSGWYRF VGEGGVRMS... >class B<Training Seq 3 EVIAYLRDPNCSSILQTEERNWVSVTSP VQASACRNI....

Training: INPUT >class A<Training Seq 1 MVGSGLLWLALVSCILTQASAVQRGYGN PIEASSYGL... >class B<Training Seq 2 LLDEPFRSTENSAGSQGCDKNMSGWYRF VGEGGVRMS... >class B<Training Seq 3 EVIAYLRDPNCSSILQTEERNWVSVTSP VQASACRNI.... classes protein sequences

Training: PA Tools sequences  features

Training: PA Tools sequences  features Homology Tools (BLAST) sequence  homologues homologues  annotations annotations  features

Homology Tool sequence  features sequence homologues annotationsfeatures seq DB BLAST retrieve parse

Homology Tool sequence  features sequence homologues annotationsfeatures seq DB BLAST retrieve parse DBSOURCE swissprot: locus MPPB_NEUCR,... xrefs (non-sequence databases):... InterProIPR001431,... KEYWORDS Hydrolase; Metalloprotease; Zinc; Mitochondrion; Transit peptide; Oxidoreductase; Electron transport; Respiratory chain.

Homology Tool sequence  features sequence homologues annotationsfeatures seq DB BLAST retrieve parse

Training: PA Tools sequences  features Homology Tools (BLAST) sequence  homologues homologues  annotations annotations  features Pattern Tools (PFAM, ProSite, …) sequences  motifs motifs  features

Pattern Tool sequence  features sequence patterns features pattern DB find parse

Pattern Tool sequence  features sequence patterns features pattern DB find parse Pfam; PF00234; tryp_alpha_amyl; 1. PROSITE; PS00940; GAMMA_THIONIN; 1. PROSITE; PS00305; 11S_SEED_STORAGE; 1.

Pattern Tool sequence  features not included in current results sequence patterns features pattern DB find parse

Training: ML Algorithm features, classes  Classifier

Training: ML Algorithm features, classes  Classifier any ML Algorithm may be used default = naïve Bayes consistently near-best accuracy (SVM, ANN slightly better) efficient (for high-throughput) easy to interpret

Training: OUTPUT Classifier

Analysis (Classification) INPUT sequences PA Tools sequences  features Classifier features  classes, explanation OUTPUT classes

Analysis: INPUT >Seq 1 DTILNINFQCAYPLDMKVSLQAALQPIV SSLNVSVDG... >Seq 2 AVELSVESVLYVGAILEQGDTSRFNLVL RNCYATPTE... >Seq 3 HVEENGQSSESRFSVQMFMFAGHYDLVF LHCEIHLCD....

Analysis: INPUT >Seq 1 DTILNINFQCAYPLDMKVSLQAALQPIV SSLNVSVDG... >Seq 2 AVELSVESVLYVGAILEQGDTSRFNLVL RNCYATPTE... >Seq 3 HVEENGQSSESRFSVQMFMFAGHYDLVF LHCEIHLCD.... protein sequences

Analysis: PA Tools sequences  features

Analysis: PA Tools sequences  features Homology Tools (BLAST) sequence  homologues homologues  annotations annotations  features Pattern Tools (PFAM, ProSite, …) sequences  motifs motifs  features

Analysis: Classification features  classes

Analysis: Classification features  classes naïve Bayes returns probabilities of each class for each sequence efficient (for high-throughput) easy to interpret

Analysis: Classification features  classes, explanation

Analysis: Classification features  classes, explanation

Analysis: Classification features  classes, explanation

Analysis: Classification features  classes, explanation

Analysis: Classification features  classes, explanation

Results: General Function GeneQuiz classification 5-fold x-val accuracy on 14 classes

Results: General Function GeneQuiz classification 5-fold x-val accuracy on 14 classes E. Coli (2370) 82.5% Yeast (2359) 78.8% Fly (3842) 76.6%

Results: Specific Function K+ Ion Channel Proteins 5-fold x-val accuracy on 78 sequences, 4 classes

Results: Specific Function K+ Ion Channel Proteins 5-fold x-val accuracy on 78 sequences, 4 classes Accuracy 1st effort97.4% 2nd effort100%

Results: Localization Sub-cellular localization prediction 3146 sequences from 10 classes

Results: Localization Sub-cellular localization prediction 3146 sequences from 10 classes AccuracyCoverage Nair and Rost81.5%36.9% Proteome Analyst87.8%100%

Results Sub-cellular localization prediction 3146 sequences from 10 classes AccuracyCoverage Nair and Rost81.5%36.9% Proteome Analyst87.8%100%

Proteome Analyst High-throughput Transparent Prediction of Protein Function Protein Localization Custom Classification

Acknowledgements Student developers Cynthia Luk Samer Nassar Kevin McKee Biologists Warren Gallin Kathy Magor Data Nair and Rost

Acknowledgements Funding PENCE – Protein Engineering Network of Centres of Excellence NSERC - National Science and Engineering Research Council Sun Microsystems AICML - Alberta Ingenuity Centre for Machine Learning

Acknowledgements Many ‘-ome’ jokes my wife, Jen

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