Design of de novo protein pores with custom geometries

Proteins, nature`s building blocks of live, have distinct three-dimensional structures that are made from a string of amino acids. In nature, there are twenty different types of amino acids that can be used to make a protein. In a highly regulated process the amino acids are strung together and arranged into a distinct structure by a cell to make proteins. The amino acid sequence is also called the proteins primary structure. It tends to build stable local structure elements, called secondary structure elements. Those can be spiral structures (helices), elongated stretches (strands) or swirly random structures (loops). With only these three building blocks, nature can build all the proteins we know, through a process called protein folding, in which the helices, strands and loops fold into a stable configuration that represents a low energy state for a particular amino acid sequence. Remarkable, even for small protein structures, this process would take the present time of the universe, if all the conformations it could adopt were to be explored. This also means, if we can solve the protein folding problem efficiently, we could make proteins from scratch according to our needs. Because of their versatility, biological macromolecules have been used for a long time as catalysts in chemical synthesis or in bioremediation of several compounds. They are applied as protein drugs to cure diseases or to generate highly pure chemical compounds as well as to degrade environmental pollutants, while at the same time they produce only minimal amounts of waste and exhibit an excellent resource balance. For most applications of protein based nanomaterials and general protein engineering, rigid protein building blocks are desirable, but naturally occurring proteins are only marginally stable. We previously developed a general procedure for designing new protein structures, by taking a set of equations first derived by Francis Crick in 1953, which describe helical protein structures, and combining them with sophisticated computational modeling. This enabled us to generate new protein structures of arbitrary size and with unprecedented stabilities. Our designs are stable above 95C - a temperature at which most natural proteins have long started to degrade - and in highly degrading conditions. In this project, we will expand the range of designable structures to ones that exhibit central cavities and pores and investigate their functionalization with a specific focus on protein geometries that should be ideal for sequencing DNA or filtering toxic compounds (e.g. mercury) out of water.

Since the beginning of protein science, it has been clear that proteins have tremendous potential to tackle and solve a variety of biomedical and biotechnological challenges. This is the major reason why they are used widely e.g. as drugs to treat diseases or to generate highly pure chemical compounds, while at the same time producing only minimal amounts of waste and exhibiting an excellent resource balance. Our main interest is in de novo protein design - a field that uses our best understanding of protein biochemistry and biophysics to identify a minimum energy amino acid sequence composition that allows the protein to fold exactly into a desired shape and exhibit an envisioned function. In this project, we could show that it is possible to design genetically encoded, large helical protein structures resembling soluble protein pores with atomic level accuracy. To do so, a we used a method that uses a set of equations, which accurately describe the geometries of helical protein structures and computer assisted amino acid sequence design to sample the folding space of the envisioned proteins computationally. The resulting designed proteins were highly idealistic in terms of geometry and showed very high thermodynamic stability, with their experimentally determined structures close to identical to the design models and nearly perfect packing of amino acid side chains between the helices. Moreover, we also developed a computational method that uses a well known machine learning model to perform rapid amino acid exchange scans over entire protein sequences. This opens the possibilities for protein engineers to quickly explore amino acid sequence space for their proteins of interest.