David John Huggins

Computational protein design is a foundational challenge in biotechnology, advantageous for engineering novel enzymes and therapeutics, yet its combinatorial complexity remains a bottleneck for classical optimization. We formulate fixed-backbone computational protein design as a quadratic Hamiltonian over rotamer variables to naturally map onto a hybrid photonic entropy computing platform, Dirac-3. To assess solution quality and runtime performance, we benchmark the photonic solver against an exact classical cost function network (CFN) solver, which provides provably optimal baselines. For protein instances ranging from 493 to 943 variables, Dirac-3 attains solutions within 0.16-2.47% of optimal energies. Empirical scaling analysis reveals a comparatively gentle effective runtime growth for the photonic solver over the measured regime, consistent with near-linear polynomial scaling, in contrast to the sharp super-polynomial growth observed for the classical baseline beyond approximately 1000 variables. These results suggest a near-term crossover regime in which hardware-aligned continuous-variable optimization may offer a practical promise for large computational protein design instances where exact classical methods become time-prohibitive.

Absolute (ABFE) and relative (RBFE) binding free energy calculations with all-atom molecular dynamics (MD) can significantly reduce costs in the early stages of drug discovery. We introduce a new implementation of the Binding Affinity Tool (BAT.py) software, which adds RBFE calculations using separated topologies (SepTop) to the already established ABFE fully automated workflow. SepTop combines the advantages of ABFE and RBFE, being applicable to ligands that have very little or no similarity, while at the same time avoiding common challenges of ABFE calculations, such as occluded binding sites and problematic conformational changes of the receptor upon ligand binding. Three different thermodynamic paths for the relative calculations were implemented into the BAT software using the AMBER and OpenMM simulation engines, and here we test them on the BRD4(2) benchmark system. We discuss their correlation with ABFE, standard RBFE, and experimental results, and also their associated computational cost.

We recently reported the discovery of TDI-015051, a first-in-class small-molecule inhibitor of the SARS-CoV-2 guanine-N7 methyltransferase nonstructural protein 14 (NSP14). NSP14 plays a critical role in viral RNA cap synthesis and its inhibition represents a novel antiviral approach. Utilizing systematic structure-activity relationship studies, potent non-nucleoside-based inhibitors with single-digit nanomolar cellular activity were identified from an HTS hit lacking cellular activity. Thermal shift assay data and available crystal structures led us to develop a model of the novel inhibitory ternary complex (NSP14, SAH, inhibitor), which was validated with a crystal structure of the complex. The advances described here enabled a successful proof-of-concept study that validated SARS-CoV-2 NSP14 as a novel drug target for COVID-19 and represent the first demonstration of pharmacological inhibition of viral methyltransferases as a viable avenue for an antiviral therapeutic.

This study compares molecules designed by drug discovery project teams from the Sanders Tri-Institutional Therapeutics Discovery Institute with molecules generated by two computational tools: MMPDB and REINVENT4. Seven different test cases with diverse chemotypes are studied in order to explore the potential of these computational tools in complementing human expertise in the early stages of drug discovery. By comparing the molecular structures and properties generated by MMPDB and REINVENT4 to those designed by project design teams, we aim to assess the value of such tools. The results indicate that MMPDB and REINVENT4 cover regions of chemical space larger than those covered by ideas from the drug discovery project teams. However, the chemical spaces covered by the two methods are quite different, and neither method completely covers the chemical space identified by the drug discovery project teams. Thus, the computational methods are complementary to one another and to drug discovery project team ideation. Effective application of generative molecule design tools has the potential to accelerate the identification of novel therapeutic candidates by expanding the chemical space explored during drug discovery and enabling optimal exploration.

Eleven-nineteen leukemia (ENL) is an epigenetic reader protein that drives oncogenic transcriptional programs in acute myeloid leukemia (AML). AML is one of the deadliest hematopoietic malignancies, with an overall 5-year survival rate of 27%. The epigenetic reader activity of ENL is mediated by its YEATS domain that binds to acetyl and crotonyl marks on histone tails and colocalizes with promoters of actively transcribed genes that are essential for leukemia. Prior to the discovery of TDI-11055, existing inhibitors of ENL YEATS showed in vitro potency, but had not shown efficacy in in vivo animal models. During the course of the medicinal chemistry campaign described here, we identified ENL YEATS inhibitor TDI-11055 that has an improved pharmacokinetic profile and is appropriate for in vivo evaluation of the ENL YEATS inhibition mechanism in AML.

Female Aedes aegypti mosquitoes can spread disease-causing pathogens when they bite humans to obtain blood nutrients required for egg production. Following a complete blood meal, host-seeking is suppressed until eggs are laid. Neuropeptide Y-like Receptor 7 (NPYLR7) plays a role in endogenous host-seeking suppression and previous work identified small molecule NPYLR7 agonists that suppress host-seeking and blood feeding when fed to mosquitoes at high micromolar doses. Using structure activity relationship analysis and structure-guided design we synthesized 128 compounds with similarity to known NPYLR7 agonists. Although in vitro potency (EC ₅₀ ) was not strictly predictive of in vivo effect, we identified 3 compounds that suppressed blood feeding from a live host when fed to mosquitoes at a 1 μM dose, a 100-fold improvement over the original reference compound. Exogenous activation of NPYLR7 represents an innovative vector control strategy to block mosquito biting behavior and prevent mosquito/human host interactions that lead to pathogen transmission.

Absolute binding free energy (ABFE) calculations with all-atom molecular dynamics (MD) have the potential to greatly reduce costs in the first stages of drug discovery. Here we introduce BAT2, the new version of the Binding Affinity Tool (BAT.py), designed to combine full automation of ABFE calculations with high-performance MD simulations, making it a potential tool for virtual screening. We describe and test several changes and new features that were incorporated into the code, such as relative restraints between the protein and the ligand instead of using fixed dummy atoms, sup- port for the OpenMM simulation engine, a merged approach to the application/release of restraints, support for cobinders and proteins with multiple chains, and many others. We also reduced the simulation times for each ABFE calculation, assessing the effect on the expected robustness and accuracy of the calculations.

Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC₅₀ in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.