Biological nanopore sequencing of long digitally-encoded polymers – 01Pores

Our society produces data at the impressive rate of few exabytes per day; trend that is continuously escalating creating for the future significant storage and archiving issues. At the current pace, supports based on silicon or magnetic tapes will become soon unsustainable, thus that new alternative concepts and technologies need to be explored to store information at higher density. Synthetic information-containing polymers have recently emerged as a potential solution as they can offer unparalleled chemical flexibility to optimize storage density and long-term stability. However, analytical methods for decoding the molecular information stored in these synthetic polymers hold significant drawbacks, which to date prevent the widespread use of this emerging technology. In this proposal we respond to this technological need by developing a framework based on biological nanopores, which are used to decipher digital information stored in synthetic sequence-defined polymers. This project is a synergistic collaboration between French researchers (CNRS, Strasbourg) specialized in digital polymer synthesis and Swiss scientists (École Polytechnique Fédérale de Lausanne, EPFL) expert in biological pores and nanopore sensing. The applicants have already reported a proof-of-feasibility, in which engineered pores of the aerolysin pore-forming toxin family were used to sequence digital oligo(phosphodiester)s. The present project aims to advance the state of the art in order to develop further this bio-inspired platform for retrieval of large data volumes for real-world application. To do so, both the molecular structure of the polymers and the reading strategy will be optimized to match the analytical needs. First, the chemical properties and information content of the monomeric bits will be investigated using molecular modelling and simulations, in order to understand which parameters (e.g. size, rigidity, bulkiness) will affect the polymer-pore interactions. These rationally designed monomeric bits will be prepared by automated phosphoramidite chemistry, polymerized and characterized in order to obtain linear chains with sizes ranging from 10-mers to 100-mers, containing terminal oligonucleotides to better guide pore translocation. Existing aerolysin pore mutants, along with newly engineered variants and alternative pores from the aerolysin superfamily, will be tailored to match polymer features in order to accurately decrypting the information therein encoded. The concurrent development of a deep learning framework will facilitate and automatize the decoding of digital polymers, contributing at the same to guide the discovery of the optimal alphabet of digitally-encoded moieties tailored for aerolysin-based pores. As the field of information-containing macromolecules is still in its infancy, this bio-inspired platform developed to define and decode them will surely open exciting novel opportunities to deliver practical solutions for the field of molecular data storage but also for other areas such as materials traceability and anti-counterfeiting technologies.