nathan nehrt, ms - clustering disease connections revealed by dmdm
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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
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