Philip E. Bourne, PhD, FACMINational Center for Biotechnology Information

philip.bourne@nih.govhttp://www.slideshare.net/pebourne

August 30, 2016, University of Virginia

Systems Biology & Pharmacology from a Structural Perspective

The past 2.5 years has very much been devoted to leading data science at the NIH and while this is predominantly a talk setting the context for my research, followed by current and future research, elements of work in open data science will inevitably creep in ….

Immediate Apology

The Study of Specific Structure-Function Relationships in Understanding Living Systems is Well Established

Clegg et al. 1980 Nature 5788:298-300

ApoferritinIron storage

The Study of Specific Structure-Function Relationships in Understanding Living Systems is Well Established

Clegg et al. 1980 Nature 5788:298-300

ApoferritinIron storage

Biologically active molecule

Premise: The Use of Macromolecular Structure en masse is an Underutilized Tool in Understanding Living Systems

Currently 122,000 structures & ~10TB

A first step is to accurately and extensibly represent macromolecular structure …

mmCIF - Topology

Bourne et al. 1997 Meth. Enz. 277 571-590Developed under the auspices of the IUCR

save__atom_site.Cartn_x _item_description.description; The x atom site coordinate in angstroms specified according to a set of orthogonal Cartesian axes related to the cell axes as specified by the description given in _atom_sites.Cartn_transform_axes.; _item.name '_atom_site.Cartn_x' _item.category_id atom_site _item.mandatory_code no _item_aliases.alias_name '_atom_site_Cartn_x' _item_aliases.dictionary cifdic.c94 _item_aliases.version 2.0 loop_ _item_dependent.dependent_name '_atom_site.Cartn_y' '_atom_site.Cartn_z' _item_related.related_name '_atom_site.Cartn_x_esd' _item_related.function_code associated_esd _item_sub_category.id cartesian_coordinate _item_type.code float _item_type_conditions.code esd _item_units.code angstroms

mmCIF - Extract from the Dictionary

Bourne et al. 1997 Meth. Enz. 277 571-590

RCSB Protein Data Bank 1999-2014

RCSB Protein Data Bank 1999-2014

Gu & Bourne (Ed) 2009

Samish, Bourne & Najmanovich Bioinformatics 2015 31:146-150

~~

.. and more specifically systems pharmacology… but why?

Theory: One Drug, One Gene, One DiseaseBernard M. Nat Rev Drug Disc 8(2009), 959-968

Practice:

Can we predict drug efficacy and toxicity?Can we reuse old drugs?Can we design personalized medicines?

~200 drugs with identified effects http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm

Driving Question:

Can we improve the drug discovery process through the use of structural systems pharmacology?

De novo Prediction of Drug Clinical Response Using Structural Systems Pharmacology

Output: arrhythmia

Xie et al 2015 PLOS Comp Biol 10(5):e1003554

Each input space is very large.. For illustrative purposes consider the biomolecule (aka target) space …

Reconstruction of Genome-Scale 3D Drug-Target Interaction Models - GeneSAR

Integrating chemical genomics and structural systems biology

MDsimulation

Mj

Q

Refinedinteractionmodel

MjQ

SMAPProtein-liganddocking

Mj

Q

Mi

3D model of novelTarget

3D model ofannotated target

Initialinteractionmodel

Querychemical

Networkmodeling

Experimentalsupport

Generalized NetworkEnrichment of Structure-Activity Relationships

Xie & Bourne 2008 PNAS 105(14):5441-6Xie et al 2012 Ann Rev Pharm & Tox 52:361-79Xie et al 2016 Ann Rev Pharm & Tox in press

Similar binding sites may bind similar ligands A 3D object recognition problem

• Globally different, but locally similar• Dynamic• Scalable

SMAP – Determining Binding Site Similarity Across Protein Space

SMAP - Geometric Potential of the Protein Structure

Why? Large search space Challenge: inherent flexibility

and errors in predicted structures

Representation of the protein structure - Ca atoms only- Delaunay tessellation - Graph representation

Geometric Potential (GP)

0.20.1)cos(

0.1

iDiPiPGP

neighbors

a100 0

Geometric Potential Scale

0

0.5

1

1.5

2

2.5

3

3.5

4

0 11 22 33 44 55 66 77 88 99

Geometric Potential

binding sitenon-binding site

Algorithm

Xie & Bourne 2007 BMC Bioinformatics 4:S9

SMAP - Sequence-order Independent Profile-Profile Alignment (SOIPPA)

L E R

V K D L

L E R

V K D L

Structure A Structure B

S = 8

S = 4

Algorithm

L E R

V K D L

S = 8

Xie & Bourne 2008 PNAS 105(14):5441-6

SMAP - Detection of Remote Functional Relationships Across the Protein Universe

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.1 0.2 0.3 0.4

True Positive RatioFa

lse

Posi

tive

Rat

io

PSI-BlastCESOIPPA

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.1 0.2 0.3 0.4

True Positive Ratio

Fals

e Po

sitiv

e R

atio

PSI-BlastCESOIPPA

Proteins with the same global shape Proteins with different global shape

Xie & Bourne, PNAS, 105(2008):5441

How well does this work in practice?

Consider this question from the perspective of a family of drug targets – protein kinases

A Timeline of Protein Kinase Drug Discovery

Muller et al. 2015 Nature Chemical Biology 11, 818-821

Drugs Targeting the Human Kinome• Tykerb – Breast cancer

• Gleevac – Leukemia, GI cancers

• Nexavar – Kidney and liver cancer

• Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive

Collins and Workman 2006 Nature Chemical Biology 2 689-700

10/16/13 ACSSA 25

PKA

Phosphoinositide-3 Kinase (D) and Actin-Fragmin Kinase (E)

ChaK (“Channel Kinase”)

PKA

The Devil is inThe Details

Scheeff & Bourne 2005 PLOS Comp Biol 1(5):e49

Functional Site Interaction Fingerprint

Zhao et al 2016 J. Med. Chem. 12:59(9) 4326-41

Characterize Interactions at the Proteome Scale

Zhao et al 2016 J. Med. Chem. 12:59(9) 4326-41

Can we Translate these in silico Approaches?

Yes: Limited Role in Applying this to GPCRs as Part of the SAB for Receptos Inc.

Yes:

Other Case Studies Side effect prediction

Xie et al. PLoS Comp. Biol., 3(2007):e217 Xie et al. PLoS Comp. Biol., 5(2009):e1000387

Drug repurposing Kinnings et al. PLoS Comp. Biol., 5(2009):e1000423 Xie et al. PLoS Comp. Biol. 7(2011): e1002037 Ng. et al. PSB Symposium (2014)

Polypharmacological drug design Durant et al. PLoS Comp. Biol. 6(2010):e100648 Chang et al. BMC Sys. Biol. 7(2013):102

Personalized medicine Xie et al. BMC Genomics 14(2013):S9

We have discussed drug discovery with respect to the target space…

Now consider this from a systems space perspective

Structural Coverage of E. coli MetabolismBrunk et al 2016 BMC Sys Biol, 10:26

Computational Evaluation of Drug Off-Target EffectsProteome

Drug binding site alignments

SMAP

Predicted drug targetsDrug and endogenous substrate binding site analysis

Competitively inhibitable targets

Inhibition simulations in context-specific model

COBRA Toolbox

Predicted causal targets and genetic risk factors

Metabolicnetwork

Scientificliterature

Tissue and biofluid localization data

Gene expression

data

Physiologicalobjectives

System exchange constraints

Flux states optimizing objective

Physiological context-specific

model

Influx

Efflux

Drug response phenotypesD

rug

targ

ets

Physiologicalobjectives

Causal drug targets

All targets

336 genes1587 reactions

Chang et al PLOS Comp. Biol. 2010 6(9): e1000938

What of the Future?

Data and tools are not easily used (aka not FAIR)Only 12% of output is even reportedWhat is available is siloed

It is not easy to stand on the shoulders of giantsCloud environments are potential technical solutionsGenomics is leading the way eg GA4GHWe need similar approached for systems pharmacology

Infrastructure Problems to be Solved

Services: APIs, Containers, Indexing,

Software: Services & Tools

scientific analysis tools/workflows

App store/User Interface

Enter the Commons

Shared Research Objects

StructureSpace

Cell ModelsChemical Space

Phenotypes

“Don’t worry about people stealing your ideas. If your ideas are any good, you’ll have to ram them down people’s throats.”

— Howard Aiken“… and hopefully get to say I told you so when they are shown to improve the human condition.”

-- Phil Bourne

AcknowledgementsAll 135 previous lab members Lei Xie

Zheng Zhao

PDB Team

Roger Chang (Palsson Lab)