Bridging Genetically-Encoded Chemistry with Machine Learning to discover the properties of new Chemical matter

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In this program, we will employ genetically-encoded libraries of chemical compounds and genetic-encoding of the spatial presentation of these compounds to build new platform technologies that allow predicting chemical, physical and biological properties of a wide class of chemical compounds and chemical reactions. In the last 10 years, the Derda Lab built technologies that aim to accelerate molecular discovery. We developed strategies for chemical synthesis of million-to-billion scale (106-109) collection of chemical compounds from peptides displayed on the surface of bacteriophage. Conveniently, structures of the synthesized compounds are encoded in DNA sequences in phage genome. Thus, they can be tracked, selected, and managed using DNA sequencing. Our group repurposed next-generation sequencing (NGS)-technology used for genome sequencing and clinical diagnostics-to allow for routine measurement of the structure and concentration of 105-107 molecules in such mixtures. Using our third cycle of NSERC DG funding (2022-27), we will advance the utility of GE-libraries and develop brand-new directions in GE discovery of ultra-slow reactions (Aim 1); GE glycan library and machine learning in mirror-world glycobiology (Aim 2) and GE multivalent scaffolds to study dynamic ice-nucleation processes (Aim 3). The specific research program will be solving fundamental problems in three areas: Aim 1 investigates new approach to discovery and development of chemical reactions in water. By lowering the detection limit for detection of new reactions by a factor of million to a billion, we will be able to detect ultra-slow reactions and then use GE-technology to optimize them. We anticipate that approaches that start from non-obvious ultra-slow transformations will give rise to reactions that might not be obvious to chemical intuition. Aim 2 investigates molecular recognition between proteins and chiral structures using a combination of genetically-encoded screening and machine learning. Our recent interest is in training ML models using readily available and abundant data from genetically-encoded screens. Quality of such data, however, is quite low. We will be testing whether the quality of ML models can be improved using cross-chiral datasets that contact N molecules and N mirror images (enantiomers) of these molecules. If successful, such approach can dramatically improve the quality of training of many ML algorithms that aim to solve problems that involve chiral molecules (molecular recognition, drug discovery, catalysis). Aim 3 continues fundamental investigation of the autocatalytic ice nucleation started by Derda Lab in 2015. We will use genetically-encoded precise multivalent architectures built from M13 phage to investigate the role of spatial presentation of ice-binding motifs on ice nucleation activity. Using this knowledge we will build the light-triggered multivalent constructs that nucleate ice in supercooled water upon irradiation with light.