Capstone Project Presentation

Friday 17rd December 2004 Stuart Young

Capstone Project PresentationCapstone Project Presentation

Predicting Deleterious Predicting Deleterious MutationsMutations

Young SP, Radivojac P, Mooney SDYoung SP, Radivojac P, Mooney SD



DeleteriousDeleterious““Hurtful or injurious to life Hurtful or injurious to life

or health; noxious”or health; noxious”(Oxford English Dictionary)(Oxford English Dictionary)

““Tis pity wine should be so Tis pity wine should be so deleterious, For tea and coffee deleterious, For tea and coffee leave us much more serious.leave us much more serious.””

((BYRONBYRON JuanJuan IV, 1821) IV, 1821)



SNPsSNPs

What is an SNP (single What is an SNP (single nucleotide polymorphism)?nucleotide polymorphism)? Why are SNPs important?Why are SNPs important? Some SNPs are Some SNPs are nonsynonymousnonsynonymous The molecular effects of SNPs The molecular effects of SNPs vary widelyvary widely



MOTIVATIONMOTIVATION

Improve on the existing Improve on the existing deleterious prediction methods deleterious prediction methods Use protein sequence, Use protein sequence, evolution and structure data evolution and structure data combined with machine learning combined with machine learning to identify potentially disease-to identify potentially disease-causing SNPscausing SNPs



SNP data is increasingly SNP data is increasingly availableavailable

Over 40 major online databasesOver 40 major online databases dbSNP is the primary SNP dbSNP is the primary SNP database (contains 5,000,000+ database (contains 5,000,000+ validated human SNPs) validated human SNPs) Many databases contain Many databases contain potentially disease-causing SNPs potentially disease-causing SNPs related to a particular diseaserelated to a particular disease



Deleterious effects of Deleterious effects of mutations on proteinsmutations on proteins

FunctionFunction StabilityStability ExpressionExpression Protein-Protein Protein-Protein InteractionsInteractions


Current Classification ToolsCurrent Classification Tools

Sequence Approaches BLOSUM62

An amino acid substitution score matrix

SIFT Collects sequence homologues in multiple alignments and identifies non-conservative changes in amino acidsNg P & Henikoff S, 'Predicting Deleterious Amino Acid Substitutions‘. Genome Research, 2001, 11:863-874.


Current Classification ToolsCurrent Classification Tools

Structural Approaches

Expert rulesUses evolutionary and structural dataSunyaev et al, 'Prediction of deleterious human alleles‘. Human Molecular Genetics, 2001, Vol. 10, No. 6, 593.

Decision Trees Improved performance based on

sequence and structural data Produces intuitive rules


Our foundation for the projectOur foundation for the project

Saunders CT & Baker D

‘‘Evaluation of Structural and Evolutionary

Contributions to Deleterious Mutation Prediction’

J. Mol. Biol. (2002) 322, 891–901

Structural and evolutionary Structural and evolutionary featuresfeatures Trained classifiers based on Trained classifiers based on two data sets - experimental two data sets - experimental mutations and human alleles mutations and human alleles



S & B - Training Sets Experimental mutations (~5,000)

HIV-1 proteaseE. Coli Lac repressor

T4 Lysozyme Human alleles (~350 mutations) 103 ‘hot’ human genes



Why two training sets? Unbiased human data is hard to get:

Many disease-associated mutations are discovered through genetics association studies and may not be causative (i.e., only linked with the causative allele) Effect of mutations is hard to measure

Experimental ‘whole gene mutagenesis’ data is used considered ‘unbiased’



Features used in S&B

Study

SIFT SIFT + Solvent Accessibility(SA) SIFT + normalized B-factor SIFT + Sunyaev expert rules SIFT + SA + B-factor



Hypothesis

Can we improve on the results of Saunders and

Baker by using more structural and sequence

properties?



Experimental Design Classification algorithm

Decision Trees Support Vector Neural Nets

Additional Features Amino acid relative frequencies Additional structural properties



Structural Property ValuesRuss Altman (Stanford) developed a vector representation of protein

structural sites Spheres (1.875Å → 7.5Å) centered on C-alpha atom of the mutation position 66 features Atom/residue counts within sphere and other features, e.g.:

Solubility Solvent accessibility



Amino Acid Windows

AA frequencies within a window on either side of the mutation position 20 AAs = 20 features LEFT and RIGHT → 40 features



Amino Acid Windows



Tools

Databases PDB - Protein structure data S-BLEST - Structural features

Software Perl 5.8.0 Matlab (NN, PRTools(DT), SVC)



List of Features Used BLOSUM62, disorder, secondary structure, molecular weight Grouped amino acid frequency windows of varying widths SIFT S-BLEST (vector contains four sub-shells spreading outward from site) Solvent accessibility (C-beta density, i.e., the number of C-beta atoms around the site)



Comparison with S&B

Results



1. Human Data Set Human allele dataset as train and test set Ensembles of decision trees for classification 20-fold cross validation Progressively added features to see their affect on performance Because structural data was not available for all mutation sites, we used a subset of the original Saunders and Baker training set



Best Features



1. Experimental Data Set Same as human data set but using experimental mutations for training and testing



Evaluation of S-BLEST Using a Random Subset of the Experimental Training

Set



3. Cross-classification Used the same features described above Trained on one dataset and tested on the other:

Human to experimental Experimental to human Experimental gene to exp. gene



Summary of Results

Human data set80% accuracy (up from 70%)

Experimental data set87% accuracy (up from 79.5%)



Conclusion

Prediction tools CAN identify deleterious mutations We believe that further study is warranted to identify over-fitted classifiers to further improve classification accuracy on real world data


AcknowledgementsAcknowledgements

PeopleAndrew Campen (CCBB IT, IUPUI)

Brandon Peters (CCBB, IUPUI)Haixu Tang (Capstone Coordinator, IUB)

FundingThis work was funded by a grant from the

Showalter Trust (Sean Mooney, PI), INGEN, and a IUPUI McNair Scholarship. The Indiana

Genomics Initiative (INGEN) Indiana University is supported in part by Lilly Endowment Inc.



Thank You

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