The intrinsic (gas-phase) stacking energies of natural and artificial nucleobases were explored using density functional theory (DFT) and correlated ab initio methods. Ranking the stacking strength of natural nucleobase dimers revealed a preference in binding partner similar to that seen from experiments, namely G > C > A > T > U. Decomposition of these interaction energies using symmetry-adapted perturbation theory (SAPT) showed that these dispersion dominated interactions are modulated by electrostatics. Artificial nucleobases showed a similar stacking preference for natural nucleobases and were also modulated by electrostatic interactions. A robust predictive multivariate model was developed that quantitively predicts the maximum stacking interaction between natural and a wide range of artificial nucleobases using molecular descriptors based on computed electrostatic potentials (ESPs) and the number of heavy atoms. This model should find utility in designing artificial nucleobase analogs that exhibit stacking interactions comparable to those of natural nucleobases. Further analysis of the descriptors in this model unveil the origin of superior stacking abilities of certain nucleobases, including cytosine and guanine.
Computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology and has proven useful in fields such as protein engineering and drug design. Geometric tunnel prediction programs, such as Caver3.0 and MolAxis, are computationally efficient methods to identify potential ligand entry and egress routes in proteins. Although many geometric tunnel programs are designed to accommodate a single input structure, the increasingly recognized importance of protein flexibility in tunnel formation and behavior has led to the more widespread use of protein ensembles in tunnel prediction. However, there has not yet been an attempt to directly investigate the influence of ensemble size and composition on geometric tunnel prediction. In this study, we compared tunnels found in a single crystal structure to ensembles of various sizes generated using different methods on both the apo and holo forms of cytochrome P450 enzymes CYP119, CYP2C9, and CYP3A4. Several protein structure clustering methods were tested in an attempt to generate smaller ensembles that were capable of reproducing the data from larger ensembles. Ultimately, we found that by including members from both the apo and holo data sets, we could produce ensembles containing less than 15 members that were comparable to apo or holo ensembles containing over 100 members. Furthermore, we found that, in the absence of either apo or holo crystal structure data, pseudo-apo or –holo ensembles (e.g. adding ligand to apo protein throughout MD simulations) could be used to resemble the structural ensembles of the corresponding apo and holo ensembles, respectively. Our findings not only further highlight the importance of including protein flexibility in geometric tunnel prediction, but also suggest that smaller ensembles can be as capable as larger ensembles at capturing many of the protein motions important for tunnel prediction at a lower computational cost.
Abstract Coronaviruses have been the causative agent of three epidemics and pandemics in the past two decades, including the ongoing COVID-19 pandemic. A broadly-neutralizing coronavirus therapeutic is desirable not only to prevent and treat COVID-19, but also to provide protection for high-risk populations against future emergent coronaviruses. As all coronaviruses use spike proteins on the viral surface to enter the host cells, and these spike proteins share sequence and structural homology, we set out to discover cross-reactive biologic agents targeting the spike protein to block viral entry. Through llama immunization campaigns, we have identified single domain antibodies (VHHs) that are cross-reactive against multiple emergent coronaviruses (SARS-CoV, SARS-CoV-2, and MERS). Importantly, a number of these antibodies show sub-nanomolar potency towards all SARS-like viruses including emergent CoV-2 variants. We identified nine distinct epitopes on the spike protein targeted by these VHHs. Further, by engineering VHHs targeting distinct, conserved epitopes into multi-valent formats, we significantly enhanced their neutralization potencies compared to the corresponding VHH cocktails. We believe this approach is ideally suited to address both emerging SARS-CoV-2 variants during the current pandemic as well as potential future pandemics caused by SARS-like coronaviruses.
LONP1 is an AAA+ protease that maintains mitochondrial homeostasis by removing damaged or misfolded proteins. Elevated activity and expression of LONP1 promotes cancer cell proliferation and resistance to apoptosis-inducing reagents. Despite the importance of LONP1 in human biology and disease, very few LONP1 inhibitors have been described in the literature. Herein, we report the development of selective boronic acid-based LONP1 inhibitors using structure-based drug design as well as the first structures of human LONP1 bound to various inhibitors. Our efforts led to several nanomolar LONP1 inhibitors with little to no activity against the 20S proteasome that serve as tool compounds to investigate LONP1 biology.
Abstract The convergence of DFT‐computed interaction energies with increasing binding site model size was assessed. The data show that while accurate intercalator interaction energies can be derived from binding site models featuring only the flanking nucleotides for uncharged intercalators that bind parallel to the DNA base pairs, errors remain significant even when including distant nucleotides for intercalators that are charged, exhibit groove‐binding tails that engage in noncovalent interactions with distant nucleotides, or that bind perpendicular to the DNA base pairs. Consequently, binding site models that include at least three adjacent nucleotides are required to consistently predict converged binding energies. The computationally inexpensive HF‐3c method is shown to provide reliable interaction energies and can be routinely applied to such large models.
In Mary Shelley's Frankenstein , Victor Frankenstein's well-intentioned research goes awry, creating a monster. The novel was first adapted to film in 1910, and many movie remakes and variations followed. We asked young scientists to craft their own Frankenstein-inspired science fiction by pitching
