Assistant Professor of Microbiology
With the deluge of molecular sequence and structure data, computational molecular biology or bioinformatics is taking center stage in any research that has to do with molecular biology. My laboratory is developing an applying computational models to the basic questions of life: how do genes and genomes evolve? How is life connected in the most basic molecular level?
PNPT1 has several different functions in different organisms and within the same organism. All functions are hard to predict, computationally. PNPT1 was a target in the CAFA experiment of function annotations, led by Iddo Friedberg. 56 methods competed to correctly predict the function of this protein, and 50,000 others.
- Domain architecture of human PNPT1 protein according to the Pfam classification
- Molecular Function terms (six of which are leaves) associated with the hPNPT1 protein. Colored circles represent the predicted terms for three representative methods as well as two baseline methods. None of the predictions are perfect, but some are quite useful
Proteins facilitate virtually all of life's processes: from the immune system to embryonic development, from metabolism to cell division, proteins act by binding to other molecules, serving as structural elements of organisms, catalyzing reactions, and regulating processes on various levels. On the one hand, Protein structures are diverse and complex, attesting to the many roles these molecules play in facilitating life. On the other, there are intriguing commonalities even among the most different proteins. Most importantly, we are inundated with data coming at us from genomics projects, which in many cases provides us with little real information as to what these proteins do. All this presents the computational biologist with unique challenges and opportunities.
I am interested in locating “structural signatures” that span different protein folds. My working hypothesis is that there are short local structural commonalities between proteins that otherwise share no obvious structure or function. Detecting these commonalities can help us understand protein evolution, folding, and design.
Another interest of mine is the prediction of protein function. Genomics, proteomics and various other “-omics” inundate us with sequence and structure information, but the biological functions of those proteins in many cases still eludes us. Computational prediction of protein and gene function is a new and rapidly growing research field in bioinformatics I am the co-organizer of the automated computational protein function prediction meetings: AFP. The AFP meetings bring together researchers to discuss various methods for protein function prediction. My personal interest in function prediction lies in predicting function from protein structure. My lab has recently started work on predicting gene function based on its genomic context in bacteria. I am using both genomic and metagenomic data towards that end.
Metagenomics is a new field comprising of the study of genomic material extracted directly from the environment. New second generation sequencing technologies have enabled the study of whole populations of genomes taken from microbial communities in the field, as opposed to single species clonal cultures in the lab. Metagenomics offers a way to study how genomes evolve to cope with the microbial biotic and abiotic environments. Together with Rachael Morgan-Kiss' lab, we are studying the microbial communities isolated from Antarctic lakes. I am also studying how metabolic pathways evolve using the comparative genomic opportunities offered by environmental genomic data.
One aspect of bioinformatics is collaboration: bioinformatics labs often have the skills and the know-how to help push experimental research to new frontiers, and generate verifiable hypotheses. My lab has several such collaborations in which we apply computational tools to experimental research problems presented to us.
- Radivojac P, Clark WT, Ronen-Oron T, Schnoes AM, Wittkop T,..., Bairoch A, Linial M, Babbit PC, Brener SE, Orengo C, Rost B, Mooney SD and Friedberg I. A large-scale evaluation of computational protein function prediction methods. (2013) Nature Methods
- Oberlin AT, Jurkovic DA, Balish MF, Friedberg I Biological Database of Images and Genomes: tools for community annotations linking image and genomic information. (2013) Database
- Schwartz S, Friedberg I, Ivanov IV, Davidson LA, Goldsby JS, Dahl DB, Herman D, Wang M, Donovan SM, Chapkin RS. A metagenomic study of diet-dependent interaction between gut microbiota and host in infants reveals differences in immune response (2012) Genome Biol. 13(4):r32
- Wooley JC, Godzik A and Friedberg I A Primer on Metagenomics (2010) PLoS Computational Biology
- Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJ. Biopython: freely available Python tools for computational molecular biology and bioinformatics (2009) Bioinformatics 25(11):1422-3
- Godzik A, Jambon M. and Friedberg I. Computational protein function prediction: are we making progress? (2007) Cellular and Molecular Life Sciences 64(19-20):2505-11
- Friedberg I, Nika K, Tautz L, Saito K, Cerignoli F, Friedberg I, Godzik A and Mustelin T. Identification and characterization of Dusp27, a novel dual-specific protein phosphatase (2007) FEBS Letters 581(13):2527-33.
- Friedberg I and Godzik A. Functional Differentiation of Proteins: Implications for Structural Genomics (2007) Structure 15(4):405-415
- Friedberg I, Harder T, Kolodny R, Sitbon E, Li Z and Godzik A. Using an alignment of fragment strings for comparing protein structures (2007) Bioinformatics 23: e219-e224
- Friedberg I. Automated Function Prediction: the Genomic Challenge (2006) Brief Bioinform 7(3):225-242
For a full list of publications see: http://iddo-friedberg.org/papers.html