Personalized health: harnessing the power of...

© Burkhard Rost

Personalized health:harnessing the

power of diversityBurkhard Rost

TUM Munich Comp Biol @ INF & IAS & WZW

Columbia U NYC - Biochemistry

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org

© Burkhard Rost

Personalized health: harnessing the

power of diversity

2Google “rost TEDx”

© Burkhard Rost

Mapping genome diversity

© Burkhard Rost 4

Genes to proteins

DNA / Genes Protein

information, library, manual machinery of life

human genome: 3 billion letters

human proteome: ~20 thousand proteins

DN

A ©

PDB

id 3

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h C

hang

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A H

endr

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on (2

010)

Nat

ure

267:

1074

-80

4000 books

© Burkhard Rost 5

Anna Tramontano La Sapienza Rome, Italy

Like for every good manual:

You hardly ever find what you look for.

When you find it, it is difficult to understand.

© Burkhard Rost

2008: 1000 Genomes Project (1000 individuals) • NIH - NCHGR: National Center for Human Genome Research, USA • Sanger/EBI, England • BGI/Shenzen, China

Global reference for human genetic variation‘Reference genome’Nature (2015) 526, 28-74

• 26 populations• 88 million variants (84.7 million SNPs)• 3.6 million short insertions/deletions (indels) • 60,000 structural variants

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Mapping the diversity

© Burkhard Rost 7

we differ by 10,000 amino acids (letters)

same letterdifferent letter

© Burkhard Rost

* E Bianconi et al. 2013 Annals Hum Biol 40:463-718

most cells in you have the same library!

4000 books

I may have 55 trillion cells *

© Burkhard Rost

10,000 variantshave an effect?

© Burkhard Rost

In 2050, we might answer by experiments ...

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Question: do the 10,000 variants between us have an effect?

© Burkhard Rost

... meanwhile, we use computers

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Question: do the 10,000 variants between us have an effect?

© Burkhard Rost 12

Oncogene K-Ras

structure (PDB id 4lpk): JM Ostrem et al. & KM Shokat (2013) Nature 503:548-51

Andrea Schaffer-

hans©

S© Burkhard Rost

S

Slide from:

© Burkhard Rost 13

Oncogene K-Ras: single G12C mutation

structure (PDB id 4lpk-rainbow/4l8g-red): JM Ostrem et al. & KM Shokat (2013) Nature 503:548-51

Andrea Schaffer-

hans©

S© Burkhard Rost

S

Slide from:

rainbow: K-Ras 4lpk WT red: K-Ras 4l8g G12C

© Burkhard Rost 14

Oncogene K-Ras / Rash

3gft: Y Tong et al. & H Park (unpublished)3lbn: G Buhrman et al. & C Mattos (2010) PNAS 107:4931-6.

green: 3gft K-Ras - human lime: 3lbn Rash - human

Andrea Schaffer-

hans©

S© Burkhard Rost

S

Slide from:

85% PIDE (pairwise identical residues)

© Burkhard Rost 15

human: K-Ras/Rash & fly: Rab6

3gft: Y Tong et al. & H Park (unpublished)3lbn: G Buhrman et al. & C Mattos (2010) PNAS 107:4931-6.2y8e: M Walden, HT Jenkins, TA Edwards (2011) Acta Crystallogr F 67:744

Andrea Schaffer-

hans©

S© Burkhard Rost

S

Slide from:

green: 3gft K-Ras - human lime: 3lbn Rash - human orange: 2y8e Rab6 - fly

85% PIDE

85%85an

an28%

PIDE: pairwise identical residues

© Burkhard Rost 16

human - fly - bacteria

3gft: Y Tong et al. & H Park (unpublished) / 4IW3: JS Scotti (unpublished)3lbn: G Buhrman et al. & C Mattos (2010) PNAS 107:4931-6.2y8e: M Walden, HT Jenkins, TA Edwards (2011) Acta Crystallogr F 67:744

green: 3gft K-Ras - human lime: 3lbn Rash - human orange: 2y8e Rab6 - fly purple: 2y8e hydroxylase

P putida

Andrea Schaffer-

hans©

S© Burkhard Rost

S

Slide from:

PIDE: pairwise identical residues

19%

© Burkhard Rost 17

Evolution is history!

Chris Sander & Reinhard Schneider 1991 Proteins 9:56-68B Rost 1999 Prot Engin 12:85-94

© Burkhard Rost 18

Yana Bromberg, Rutgers University

SNAP (Screening for Non-Acceptable Point mutations)

© Burkhard Rost

Y Bromberg & B Rost 2007 NAR 35:3823-35 M Hecht, Y Bromberg & B Rost 2013 JMB 425:3937-48 19

SNAP predict effects of variants

SNAP mines wealth of experimental results by machine learning

EFFECT

NEUTRAL

MaximilianHecht TUM

Yana Bromberg Rutgers

1 variant

original

no function different function

© Burkhard Rost 20

Hecht M., et al. J. Mol. Biol. 2013.Hopf T.A., et al. Cell. 2012.

Landscape of mutability

Max Hecht

• Ultimately: Every ‘life-compatible’ mutation will be observed

• SNAP2 predicts the effect for every possible variant

• Which of the high-effect predictions are phenotypically important?

BLO

SU

M a

nd P

HAT

filte

rIn

ter-

resi

due

cont

acts

(Evf

old)57

© Burkhard Rost

How well does method reproduce what we DO know experimentally?

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© Burkhard Rost

Are monogenic disease-variants

(OMIM) predicted to effect function?

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© Burkhard Rost

J Reeb, Y Bromberg & B Rost (2016) PLoS Comp Biol, submitted30

Jonas Reeb

OMIA & OMIM equally predicted

© Burkhard Rost

J Reeb, Y Bromberg & B Rost (2016) PLoS Comp Biol, submitted31

Jonas Reeb


© Burkhard Rost

J Reeb, Y Bromberg & B Rost (2016) PLoS Comp Biol, in press32

Jonas Reeb

© Burkhard Rost 32


© Burkhard Rost

Now we can answer today:Do our 10,000 differences

matter?

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© Burkhard Rost

… but those that matter do not happen often

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© Burkhard Rost

… but those that matter do not happen often

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right?

© Burkhard Rost 43

Common variants have more effect …

Cum

ulat

ive fr

actio

n

0%

25%

50%

75%

100%

-100 -75 -50 -25 0 25 50 75 100

differences between us

neutral effectPredicted impact of mutation (SNAP score)

100% 75% 50% 25% 0 25% 50% 75% 100%

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ddidifffferences between us

erences between u

100% 75% 50% 25% 0 25% 50% 75% 100%

RARE

FREQUENT

© Burkhard Rost 44

Common variants have MORE effect

© Burkhard Rost Pauling et al. (1949) Science 110: 543, Chui & Dover (2001) Curr Opin Pediatr 12: 22 P AC Allison 1954 British Med J 1:290 46

Sickle cell anemia: single amino acid change

Sickle Cell Disease: Autosomal recessive disorder E6V in HBB causes interaction w/ F85 and L88 Formation of fibrils Abnormally shaped red blood cells, leads to sickle cell anemia Manifestation of disease vastly different over patients

2hbs

E6V

4hhb

“bad” mutation increases malaria resistance Slide:Predrag

Radivojac

© Burkhard Rost

Precision medicine =

personalized drugs?

© Burkhard Rost 49

One drug tailored to each?

© Burkhard Rost 50

Fewer than 40 new drugs each year

New Drug= new NME (New Molecular Entity) approved by Federal Drug Agency (FDA), USAdata from: M Allison (2012) Nat Biotech 30, 41-49 doi:10.1038/nbt.2083 Fig. 2

Year New

drug

s

one new drug takes about 14 years and $2 billion to develop

data from: M Allison (2012) Nat Biotech 30, 41-49 Fig. 2

© Burkhard Rost

Precision medicine(diagnosis/choice,

food)NOT

personalized drugs51

© Burkhard Rost

from SAV to more

© Burkhard Rost (TUM Munich)

Impact of sequence variation


Y Bromberg & B Rost 2007 NAR 35:3823-35 Y Bromberg, PC Kahn & B Rost 2013 PNAS 110:14255-60

human glucokinase K Kamata et al. & Y Nagata (2004) Structure 12:429-38

Projects • predict effects • variation 2 phenotype

GOALs: • BIG DATA:build repository for sequencing patients in greater Munich (with Helmholtz/MPI) • technology:pipeline for analysis


Learning from the mutability landscape

M Hecht, Y Bromberg & B Rost (2013) News from the protein mutability landscape. JMB, in revision.


Large-scale protein flexibility analysis of single nucleotide polymorphisms using molecular dynamics simulations

Marc Offman© Marc Offman (TUM Munich)


Molecular dynamics of SNP in Gaucher disease

Fig. 1: Marc N Offman, M Krol, B Rost, I Silman, JL Sussman & AH Futerman (2011) Validation of a molecular dynamics protein structure PREDICTION: Comparison of an MD model with the X-ray structure of the N370S acid-β-glucosidase mutant that causes Gaucher disease.PEDS 24, 773-5.

Marc Offman

Tony Futerman Weizmann Inst

Joel Sussman Weizmann Inst

© Burkhard Rost

build complete record of all SAV

effects upon function

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© Burkhard Rost 69

11 medically important TMH predicted

OCTN1 Adiponectin receptor 1 MT-ND1

Crohn‘s disease, rheumatoid arthritis

diabetes, obesity, cancer

LHON, MELAS, Alzheimer, Parkinson

© Thomas Hopf - TUM & Debbie Marks - HMS TA Hopf et al & C Sander & DS Marks (2012) Cell 10 May doi: 10.1016

Chris Sander Harvard Dana Faber

Thomas Hopf TUM

Debora Marks Harvard Medical

© Burkhard Rost Thomas Hopf (2016) Phenotype prediction from evolutionary sequence covariation. PhD TUM, Munich. 70

Predict effect of pairs of SAVs

Thomas Hopf TUM Munich

Debora Marks Harvard Medical

© Burkhard Rost Thomas Hopf (2016) Phenotype prediction from evolutionary sequence covariation. PhD TUM, Munich. 71

Predict effect of pairs of SAVs

Thomas Hopf TUM Munich

© Burkhard Rost

Our genetic differences matter!

They contribute to our individuality AND susceptibility to

problems.

Possibly bad for us, possibly good for offspring.

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© Burkhard Rost 74

Evolution risks diversity that brings change

Cum

ulat

ive fr

actio

n

neutral effectPredicted impact of mutation (SNAP score)

0%

25%

50%

75%

100%

-100 -75 -50 -25 0 25 50 75 100

diff (we - gorilla)

differences

between us

disease mutants

© Burkhard Rost 77

got.show: prediction of odds to survive

TUM courseJavaScript + DataMining

Guy Yachdav

Tatyana Goldberg

Christian Dallago

> 10 TV shows > 5 radio shows >600 printed newspapers >1.2 billion potential readers

© Burkhard Rost 78

Thanks & Bye

$$ NIH €€ AvH

Andrea Schafferhans

Lothar Richter

Tim Karl

Laszlo Kajan r

Tatyana Goldberg

Yannick Mahlich

Guy Yachdav

YYMaximilian Hecht

Edda Kloppmann

Marlena DrabikK

Dominik Achten

Chris Schäfer

k DCh i Eddanik

Yana Bromberg

Rutgers U

Janet Kelso

MPI Leipzig

SvantePääbo

MPI Leipzig

Avner Schlessinger

Mount Sinai

Yanay Ofran

Bar-Ilan U

Michal Linial

Hebrew U

Karima Djabali

TUM

MichalYA Reinhard Schneider

U Luxembourg

Chris Sander

Sloan Kettering

J t Svante Karima Lena Rost

BIS

Stephan KramerMainz U

arg

Tobias Hamp

Mikael Bodén

UQ Brisbane

UQ IMB Brisbane

Nicholas Hamilton

Mark Ragan

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