Research Interests
My research interests lie at the interface of computational biology and medicine. Specifically, I use state-of-the-art computational methods to identify genomic and structural determinants of human disease. At the nucleotide level, I combine novel computational techniques along with carefully designed genomic experiments (e.g. expression profiling) to identify disease-relevant genes and regulatory elements. Examples include the discovery of TRAM, a key component of the LPS-TLR4 pathway, with the Golenbock lab, the identification of CCL8 as a key survival factor for Listeria infected mice (Fitzgerald lab), and an in-depth characterization of siRNA off-targets. At the protein level, I investigate the relationship between protein structure, function, and evolution. Examples, include the relationship between sequence conservation and protein recognition sites, prediction of specificity-determining residues, and the proximity of somatic mutations to binding sites. Many of the methods that I have developed to analyze proteins are available through Pfaat, a popular bioinformatics application.Computational Genomics
Genomic experiments (e.g. microarrays) provide an unbiased approach to study complex biological systems. However, they provide a number of statistical challenges (e.g. multiple hypothesis testing) and extracting biologically meaningful results is not trivial. I am particularly interested in methods that identify sets of genes that are statistically enriched for a particular feature and indicative of a phenotype. For example, the volcano plot (below) reveals the phenotypic effects of a siRNA treatment. The down-regulated transcripts are enriched with 3'UTRs possessing a seed-match (green diamonds) to the siRNA of interest, and up-regulated transcripts are enriched with GO terms related to immune response (blue circles) - a characteristic often associated with siRNA.
Structural Bioinformatics
Our knowledge of protein recognition sites is limited to a handful of co-crystal structures. My work investigates the relationship between sequence and structure, with the goal of predicting binding sites. For example, in c-Met, the residues that bind HGF are under significant evolutionary constraint (2nd figure,red/orange). In contrast, the corresponding residues in the related Ron are not under significant evolutionary constraint (3rd figure, yellow/green/purple). This example highlights the importance of appropriate sequence selection when using conservation scores to predict binding sites. Additioanlly, it can be seen that residues peripheral to the binding site are often conserved. |
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| c-Met structure with HGF binding site residues colored red and gold. | Conservation scores for c-Met orthologs mapped to the structure of c-Met. | Conservation scores for Ron orthologs mapped to the structure of c-Met. |
Pfaat 
I am the project leader for Pfaat, a Java application that allows one to edit, analyze, and annotate multiple sequence alignments. Pfaat seamlessly integrates multiple sequence alignments, phylogentic trees, and x-ray crystal structures. Its annotation features are a key component, as they provide a framework for further sequence, structure and statistical analysis.
Publications
- Caffrey DR, Zhao J, Song Z, Schaffer ME, Haney S, Subramanian RR, Seymour AB, Hughes JD
siRNA off-target effects can be reduced at concentrations that match their individual potency
PLoS ONE. 2011 Jul Pubmed
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- Hornung V, Ablasser A, Charrel-Dennis M, Bauernfeind F, Horvath G, Caffrey DR, Latz E, Fitzgerald KA
AIM2 recognizes cytosolic dsDNA and forms a caspase-1-activating inflammasome with ASC
Nature. 2009 Jan 21; - Caffrey DR, Lunney EA, Moshinsky DJ.
Prediction of specificity-determining residues for small-molecule kinase inhibitors.
BMC Bioinformatics. 2008 Nov 25;9:491 Pubmed
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Caffrey DR, Dana PH, Mathur V, Ocano M, Hong EJ, Wang YE, Somaroo S, Caffrey BE, Potluri S, Huang ES.
PFAAT version 2.0: a tool for editing, annotating, and analyzing multiple sequence alignments.
BMC Bioinformatics. 2007 Oct 11;8:381 Pubmed
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- Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES.
Structure-based maximal affinity model predicts small-molecule druggability.
Nat Biotechnol. 2007 Jan;25(1):71-5 Pubmed - Lauw FN, Caffrey DR, Golenbock DT.
Of mice and man: TLR11 (finally) finds profilin.
Trends Immunol. 2005 Aug 16 Pubmed
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- Caffrey DR, Somaroo S, Hughes JH, Mintseris J, Huang ES.
Are protein-protein interfaces more conserved in sequence than the rest of the protein surface?
Protein Science 2004 Jan;13(1):190-189 Pubmed Supp data
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- Fitzgerald KA, Rowe DC, Barnes BJ, Caffrey DR, Visintin A, Latz E, Monks B, Pitha PM, Golenbock DT.
LPS-TLR-4 signaling to IRF-3/7 and NF-kB involves the Toll adapters TRAM and TRIF.
J Exp Med 2003 oct;198(7):1043-1055 Pubmed
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- Hokamp K, Shields DC, Wolfe KH, Caffrey DR.
Wrapping up BLAST and other applications for use on Unix clusters.
Bioinformatics 2003 Feb:19(3):441-2 Pubmed
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- Cotter PJ, Caffrey DR, Shields DC.
Improved database searches for orthologous sequences by conditioning on outgroup sequences.
Bioinformatics 2002 Jan;18(1):83-91 Pubmed.
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- Caffrey DR, O'Neill LAJ, Shields DC.
A method to predict residues conferring functional differences between related proteins: Application to MAP kinase pathways.
Protein Science 2000 Apr;9(4):655-670. Pubmed. Supp data
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- Caffrey DR, O'Neill LAJ, Shields DC.
The Evolution of the MAP Kinase Pathways: Coduplication of interacting proteins leads to new signalling cascades.
J Mol Evol 1999 Nov;49(5):567-582 Pubmed Supp data
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