Eugene I. Shakhnovich
Multiscale Theoretical and Experimental Studies of Physical Chemical Principles of Living Matter.
Mutations increase genetic diversity and affect molecular properties of proteins (stability, activity, solubility,etc.), while natural selection is the "executive force" of evolution, operating at the level of organisms and populations. How does the molecular information contained within a protein that as been affected by mutations propagate through the intricately wired cellular context to eventually become a property “visible” by evolution, i.e., fitness? Experimentally, we address this fundamental biological question by a bottom-up genetic approach. To this end, we use a chromosomal editing technique to rationally introduce mutations within the ORF of the model proteins (dihydrofolate reductase, adenylate kinase). Knowing the identity of the mutated protein and the effects of mutations on its molecular properties allows us to disentangle molecular and mechanistic components contributing to the pleiotropic fitness effects of mutations. We combine analytical measurements (microcalorimetry, spectrosocopy, etc.) to detect mutation-induced perturbations in protein structure and function with systems biology techniques (proteomics, metabolomics, transcriptomics) to unravel the global, cellular effects of these perturbations.
Computational and Theoretical Research
We are also developing theoretical and computational methods that bridge the small scale of molecular biophysics and the large scale of population genetics, with an eye towards arriving at a unified understanding of evolution. This approach leads us to biological problems as diverse as molecular evolution, explaining genomic correlations, and viral evolution. We are working on a broader understanding on how properties of molecules in nature are not only shaped by Physics and Chemistry, but also by the evolutionary forces of mutation, drift, and selection.
Another aspect of our interests lies in computational Drug Discovery, aiming to develop new tools and apply them to concrete problems. New inhibitors are tested in-house or by collaborators.
Previously, this lab made fundamental contributions to study of protein folding using broad spectrum of approaches from analytical statistical mechanical theory to lattice model simulations and more recently to all-atom simulations of folding processes. We discovered the criteria for polypeptide sequences to be protein-like (''energy-gap theory''), the nucleation mechanism and folding nucleus (a theoretical discovery which was subsequently fully confirmed in experiments). We also developed (using lattice models) stochastic protein design that was used later by other groups to design new proteins. The lab actively studies fundamental aspects of protein chemistry, including protein folding, aggregation and protein-protein interactions using a range of computational approaches and experiments.
Woodard, J.C., Dunatunga, S. & Shakhnovich, E.I. A Simple Model of Protein Domain Swapping in Crowded Cellular Environments. Biophysical Journal 110, 11, 2367–2376 (2016).
Jacobs, W.M. & Shakhnovich, E.I. Structure-based prediction of protein-folding transition paths. Biophysical Journal 111, 5, 925–936 (2016).
Rodrigues, J.V., et al. Biophysical principles predict fitness landscapes of drug resistance. Proceedings of the National Academy of Sciences 113, 13, E1470–E1478 (2016).
Jacquin, H., Gilson, A.I., Shakhnovich, E.I., Cocco, S. & Monasson, R. Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models. PLoS Comput Biol 12, 5, e1004889 (2016).
Bershtein, S., Serohijos, A.W.R. & Shakhnovich, E.I. Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations. Current Opinion in Structural Biology 42, 31–40 (2017).
Srinivasan, B., Rodrigues, J.V., Tonddast-Navaei, S., Shakhnovich, E. & Skolnick, J. Rational design of novel allosteric dihydrofolate reductase inhibitors showing antibacterial-effects on drug-resistant E. coli escape-variants. ACS Chemical Biology (2017).
12 Oxford Street, Cambridge, MA 02138