Interdisciplinary Life Science - PULSe Great research is a matter of choice

Alan Friedman

Alan Friedman Profile Picture

Assistant Professor of Biological Sciences
Ph.D., Yale, 1989


Contact Info:

afried@purdue.edu
765-494-5911


Training Group(s):
Immunology and Infectious Diseases
Biomolecular Structure and Biophysics


Current Research Interests:

Our research seeks to understand biological structure and its relationship to function by employing a combination of experimentation (structural biology/biophysics) and computation (computational biology/bioinformatics) to answer questions that neither can answer alone. The questions include elucidating structural information from challenging systems and interpreting the interactions that are revealed by structure elucidation to understand function.

In elucidating structural information we use the unique ability of computational methods to simulate outcomes and sift through enormous numbers of possibilities in order to plan the most informative experiments to conduct. We then employ traditional biophysical and biochemical techniques (e.g. crystallography, solution x-ray scattering, ultracentrifugation, cross-linking, site-directed mutagenesis) particularly in variant and hybrid methodologies that we are developing (e.g. planned disulfide-trapping, planned stability mutagenesis and decomposition of x-ray scattering from heterogeneous solutions) to probe the real behavior of these systems. The data is then analyzed also with the help of computational methods of our own devising.

In interpreting structure to explain function we have developed a series of methods for making and using chimeras of homologous proteins to probe the interactions between protein units. These methods also employ computation to design the most informative sets of chimeras. Chimeras are then created robotically using our recently-developed gene assembly planning and robotic control software and analyzed for stability and activity by well-established means. Here too, computational models and analyses help us interpret the experimental data.

The model biological systems that we employ for both kinds of studies are drawn from our longstanding interests in the damage that results to macromolecules upon aging and their repair by cellular processes and by the interaction of organisms as they form symbiotic, mutualistic or parasitic relationships. These are both questions where macromolecular structure intersects with important evolutionary strategies to determine the health and longevity of organisms and ecosystems.



Selected Publications:

Saftalov, L., Smith, P. A., Friedman, A. M. & Bailey-Kellogg, C. 2006. Site-Directed Combinatorial Construction of Chimaeric Genes: General Method for Optimizing Assembly of Gene Fragments. Proteins. 64:629-42.

Ye, X., Friedman, A. M. & Bailey-Kellogg, C. 2006. Hypergraph model of multi-residue interactions in proteins: Sequentially-constrained partitioning algorithms for optimization of site-directed protein recombination. Lecture Notes in Computer Science, Research in Computational Molecular Biology, 10th Annual International Conference, RECOMB 2006. 3909:15-29.

Zheng, W., Ye, X., Friedman, A. M. & Bailey-Kellogg, C. 2007. Algorithms for selecting breakpoint locations to optimize diversity in protein engineering by site-directed protein recombination. Life Sciences Society, 6th Annual International Conference on Computational Systems Bioinformatics, CSB2007, 31-40.

Williamson, T. E., Craig, B. A., Kondrashkina, E., Bailey-Kellogg, C. & Friedman, A. M. 2007. Association constants and low-resolution structures of self-associating systems by singular value decomposition of solution scattering data. Biophysical Journal, In press.

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