Xing, Eric | Search NASA

DOE OSTI2024

Interaction of N-methylmesoporphyrin IX with a hybrid left-/right-handed G-quadruplex motif from the promoter of the SLC2A1 gene

Abstract Left-handed G-quadruplexes (LHG4s) belong to a class of recently discovered noncanonical DNA structures under the larger umbrella of G-quadruplex DNAs (G4s). The biological relevance of these structures and their ability to be targeted with classical G4 ligands is underexplored. Here, we explore whether the putative LHG4 DNA sequence from the SLC2A1 oncogene promoter maintains its left-handed characteristics upon addition of nucleotides in the 5′- and 3′-direction from its genomic context. We also investigate whether this sequence interacts with a well-established G4 binder, N-methylmesoporphyrin IX (NMM). We employed biophysical and X-ray structural studies to address these questions. Our results indicate that the sequence d[G(TGG)3TGA(TGG)4] (termed here as SLC) adopts a two-subunit, four-tetrad hybrid left-/right-handed G4 (LH/RHG4) topology. Addition of 5′-G or 5′-GG abolishes the left-handed fold in one subunit, while the addition of 3′-C or 3′-CA maintains the original fold. X-ray crystal structure analyses show that SLC maintains the same hybrid LH/RHG4 fold in the solid state and that NMM stacks onto the right-handed subunit of SLC. NMM binds to SLC with a 1:1 stoichiometry and a moderate-to-tight binding constant of 15 μM−1. This work deepens our understanding of LHG4 structures and their binding with traditional G4 ligands.

Seth, Paul↗

DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗