Clustering disease connections revealed by DMDM:

Domain Mapping of Disease Mutations

Nathan L. Nehrt, MS1, Thomas A. Peterson1, Asa O. Adadey1, Maricel G. Kann, PhD1

1University of Maryland, Baltimore County, Baltimore, MD

Protein Domains

Protein domains are highly conserved structural and functional subunits of the protein

Domains fold and function independently, and frequently mediate interactions with other proteins

Human CFTR

Different Ways to View Mutation

Traditional gene-centric view

Relate the mutation to the entire gene

New domain-centric view

Relate the mutation to a specific domain

Domain View vs. Gene View

Domain view gives the functional context of the mutation

Same Protein:Different Domains, Different Diseases

Zhong & Vidal et al. Molecular Systems Biology (2009)

Gene Disease Domain

Same Protein:Different Domains, Different Diseases

SPTBSpectrin beta chain, erythrocytic

actin binding domains helix forming domains

Elliptocytosis mutationsSpherocytosis mutation

Domain View vs. Gene View

Domain view gives the functional context of the mutation

Domain view reduces the space of inquiry

Reduces the space of inquiry

≈22,000 human genes

≈34,500 human RefSeq proteins

Over 550,000 human proteins from all databases listed in NCBI

Fewer than 4,500 human protein domains

Domain View vs. Gene View

Domain view gives the functional context of the mutation

Domain view reduces the space of inquiry

Majority of disease mutations in coding regions occur inside domains

Majority of Disease Mutations are Inside Domains

Swiss-Prot

PolymorphismsSwiss-Prot

Disease Mutations

Inside

52% Inside

82%

Outside

18% Outside

48%

http://bioinf.umbc.edu/DMDM/

DMDM Search Page

Data

Proteins: RefSeq and SwissProt human proteins

Domains: CDD, Pfam, SMART, COG

Variants: non-synonymous coding variants from OMIM, Swiss-Prot, dbSNP

Method

Created HMMs from domain alignments (HMMER)

Aligned domain models to proteins (HMMER)

Mapped variants to protein and domain positions

HMMER: http//hmmer.wustl.edu/

Shared Domain

Protein 2

Disease B

DMDM Domain View

A A,CB

Protein 3

Disease A Disease C

Disease Mutation

Non-Disease SNP

Protein 1

Disease A

284

Disease Connections

Shared Domain

Protein 2

Disease B

DMDM Domain View

A A,CB

Protein 3

Disease A Disease C

Protein 1

Disease A

Disease Connection

Disease ConnectionsMAP4K2p.Met210LysMaturity Onset Diabetes of the Young, Type 2

RETp.Thr946MetMultiple Endocrine Neoplasia, Type 2B

STKc_MAPK - Position 284

Disease Connections:Same Domain Position, Dissimilar Proteins

Maturity Onset Diabetes of the Young & Multiple Endocrine Neoplasia

Colon & Lung Cancer

Bardet-Biedl Sydrome

Type 6 & Type 10

46 XY Disorder of Sex Dev. & 46XY Gonadal Dysgenesis*

* filtered out when searching only for dissimilar proteins

Connections found:Discovery of non-obvious

molecular similarities of diseases

Noise in our dataset

Different Disease Categories

Same Disease Category

Different Types of Related Diseases

Same Disease

Translational Impact

Transfer of information

between connected diseases

Transfer of information can help us to:

Better understand the molecular basis of the connected diseases

Improve the prediction of deleteriousness of newly discovered mutations

Apply existing drugs to the treatment of different diseases

Identify new drug targets from domain hotspots

Challenges

Disease clustering and categorization

How do we distinguish between different diseases?

Semantic similarity

How do we assign disease categories?

Phenotypic similarity - based on the Disease Ontology or Human Phenotype Ontology

Challenges

Evaluation of disease connections

How do we determine when a connection is novel, significant, translatable?

Literature search (PubMed articles where both disease titles present)

Identification of existing drugs (DrugBank)

Working on statistical methods for determining significance of connections (Yue et al., created scoring method for domain hotspots)

Experimental validation of molecular similarity of diseases

Yue et al. Human Mutation (2010)

Challenges

We need your help!

Thank You

DMDM Authors:

Tom Peterson, Asa Adadey, Ivette Santana-Cruz, Yanan Sun, Andrew Winder, and Dr. Maricel Kann

Additional members of the Kann Lab:

Guisong Wang, Emily Doughty, Olumide Omobo

References

Peterson, T.A., Adadey, A.O., Santana-Cruz, I., Sun, Y., Winder, A., Kann, M.G. (2010) DMDM: domain mapping of disease mutations. Bioinformatics, 26, 2458-2459.

Yue, P. et al. (2010) Inferring the Functional Effects of Mutation through Clusters of Mutations in Homologous Proteins. Human Mutation, 31, 264-271.

Zhong, Q. et al. (2009) Edgetic perturbation models of human inherited disorders. Molecular Systems Biology, 5:321