Discovery and multimerization of cross-reactive single-domain antibodies against SARS-like viruses to enhance potency and address emerging SARS-CoV-2 variants.

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.

Hexamerization of Anti-SARS CoV IgG1 Antibodies Improves Neutralization Capacity.

The SARS-CoV-2 pandemic and particularly the emerging variants have deepened the need for widely available therapeutic options. We have demonstrated that hexamer-enhancing mutations in the Fc region of anti-SARS-CoV IgG antibodies lead to a noticeable improvement in IC50 in both pseudo and live virus neutralization assay compared to parental molecules. We also show that hexamer-enhancing mutants improve C1q binding to target surface. To our knowledge, this is the first time this format has been explored for application in viral neutralization and the studies provide proof-of-concept for the use of hexamer-enhanced IgG1 molecules as potential anti-viral therapeutics.

Structure-Based Design of Selective LONP1 Inhibitors for Probing In Vitro Biology.

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.

Importance of model size in quantum mechanical studies of DNA intercalation.

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.

Development of a virtual reality platform for effective communication of structural data in drug discovery.

In drug discovery, structural knowledge of a target enables structure-based design approaches and thereby reduces the time and labor required to develop a therapy. Whilst molecular graphics frameworks coupled with computational analysis are now ubiquitous tools for the structural and computational biologist, sharing the detailed visualization and derived structural information with non-expert users still presents a challenge. Here we describe an intuitive virtual world for viewing, manipulating, and modifying chemical and macromolecular structures in a fully immersive and collaborative 3D environment. By reducing the barriers to viewing and interacting with structural data, structural analysis can be democratized to a general scientist, which in turn fosters novel collaboration, ideas, and findings in structural biology and structure-based drug discovery.

Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations.

Crystallization of protein-protein complexes can often be problematic and therefore computational structural models are often relied on. Such models are often generated using protein-protein docking algorithms, where one of the main challenges is selecting which of several thousand potential predictions represents the most near-native complex. We have developed a novel technique that involves the use of steered molecular dynamics (sMD) and umbrella sampling to identify near-native complexes among protein-protein docking predictions. Using this technique, we have found a strong correlation between our predictions and the interface RMSD (iRMSD) in ten diverse test systems. On two of the systems, we investigated if the prediction results could be further improved using potential of mean force calculations. We demonstrated that a near-native (<2.0 Å iRMSD) structure could be identified in the top-1 ranked position for both systems. © 2016 Wiley Periodicals, Inc.

Substrate tunnels in enzymes: structure-function relationships and computational methodology.

In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein, and, in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years because of implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with buried active sites and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.

Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates.

PURPOSE: Predicting atoms in a potential drug compound that are susceptible to oxidation by cytochrome P450 (CYP) enzymes is of great interest to the pharmaceutical community. We aimed to develop a computational approach combining ligand- and structure-based design principles to accurately predict sites of metabolism (SoMs) in a series of CYP2C9 substrates. METHODS: We employed the reactivity model, SMARTCyp, ensemble docking, and pseudo-receptor modeling based on quantitative structure-activity relationships (QSAR) to account for influences of both the inherent reactivity of each atom and the physical structure of the CYP2C9 binding site. RESULTS: We tested ligand-based prediction alone (i.e. SMARTCyp), structure-based prediction alone (i.e. AutoDock Vina docking), the linear combination of the SMARTCYP and docking scores, and finally a pseudo-receptor QSAR model based on the docked compounds in combination with SMARTCyp. We found that by using the latter combined approach we were able to accurately predict 88% and 96% of the true SoMs, within the top-1 and top-2 predictions, respectively. CONCLUSIONS: We have outlined a novel combination approach for accurately predicting SoMs in CYP2C9 ligands. We believe that this method may be applied to other CYP2C9 ligands as well as to other CYP systems.

Including ligand-induced protein flexibility into protein tunnel prediction.

In proteins with buried active sites, understanding how ligands migrate through the tunnels that connect the exterior of the protein to the active site can shed light on substrate specificity and enzyme function. A growing body of evidence highlights the importance of protein flexibility in the binding site on ligand binding; however, the influence of protein flexibility throughout the body of the protein during ligand entry and egress is much less characterized. We have developed a novel tunnel prediction and evaluation method named IterTunnel, which includes the influence of ligand-induced protein flexibility, guarantees ligand egress, and provides detailed free energy information as the ligand proceeds along the egress route. IterTunnel combines geometric tunnel prediction with steered molecular dynamics in an iterative process to identify tunnels that open as a result of ligand migration and calculates the potential of mean force of ligand egress through a given tunnel. Applying this new method to cytochrome P450 2B6, we demonstrate the influence of protein flexibility on the shape and accessibility of tunnels. More importantly, we demonstrate that the ligand itself, while traversing through a tunnel, can reshape tunnels due to its interaction with the protein. This process results in the exposure of new tunnels and the closure of preexisting tunnels as the ligand migrates from the active site.

Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes.

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.