Computational protein design: applications in protein evolution and material science

A general understanding of the complex phenomenon of protein evolution requires the accurate description of the constraints that define the subspace of proteins with mutations that do not appreciably reduce the fitness of the organism. Such constraints can have multiple origins, and here such constrained evolutionary trajectories are represented by a Markovian process throughout a set of protein-like structures artificially constructed to be topological inter- mediates between the structure of two natural occurring proteins. The number and type of intermediate steps defines how constrained the total evolutionary process is. The Caterpillar coarse-grained protein representation allows for both quantitative protein design and folding, making it the perfect tool for constructing the evolutionary trajectories. In fact, with computer simulations it is shown that, for a large set of real protein structures, the model produces designed sequences with similar physical properties to the corresponding natural occurring sequences. Ultimately, the Caterpillar model is unique in the combination of its fundamental three features: its simplicity, its ability to produce natural foldable designed sequences, and its structure prediction precision. By using the Caterpillar model we derive an analytic formulation of the transition rates between each of the intermediate structures. The results indicate that compact structures with a high number of hydrogen bonds are more probable and have a higher likelihood to arise during evolution. Knowledge of the transition rates allows for the study of complex evolutionary pathways represented by trajectories through a set of intermediate structures. Finally we show how the fundamental principle that we observed while studying protein evolution can be transferred to a purely artificial system of patchy colloidal particles linked into strings.

References:

[1] I. Coluzza, PLoS One 9, e112852 (2014).

[2] I. Coluzza, J. T. MacDonald, M. I. Sadowski, W. R. Taylor, and R. a. Goldstein, PLoS One 7, e34228 (2012).

[3] I. Coluzza, P. van Oostrum, B. Capone, E. Reimhult, and C. Dellago, Phys. Rev. Lett. 110, 075501 (2013).