Molecular analysis of the type III interferon complex and its applications in protein engineering.

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity.

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib's mechanism of Mpro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1-M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring.

A combined machine learning-physics-based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

Toward wide-spectrum antivirals against coronaviruses: Molecular characterization of SARS-CoV-2 NSP13 helicase inhibitors.

To date, effective therapeutic treatments that confer strong attenuation against coronaviruses (CoVs) remain elusive. Among potential drug targets, the helicase of CoVs is attractive due to its sequence conservation and indispensability. We rely on atomistic molecular dynamics simulations to explore the structural coordination and dynamics associated with the SARS-CoV-2 Nsp13 apo enzyme, as well as their complexes with natural ligands. A complex communication network is revealed among the five domains of Nsp13, which is differentially activated because of the presence of the ligands, as shown by shear strain analysis, principal components analysis, dynamical cross-correlation matrix analysis, and water transport analysis. The binding free energy and the corresponding mechanism of action are presented for three small molecules that were shown to be efficient inhibitors of the previous SARS-CoV Nsp13 enzyme. Together, our findings provide critical fresh insights for rational design of broad-spectrum antivirals against CoVs.

Modeling the Binding Mechanism of Remdesivir, Favilavir, and Ribavirin to SARS-CoV-2 RNA-Dependent RNA Polymerase.

Recent efforts to repurpose drugs to combat COVID-19 have identified Remdesivir as a candidate. It acts on the RNA-dependent, RNA polymerase (RdRp) of the SARS-CoV-2 virus, a protein complex responsible for mediating replication of the virus's genome. However, its exact action mechanism, and that of other nucleotide analogue inhibitors, is not known. In this study, we examine at the molecular level the interaction of this drug and that of similar nucleotide analogue inhibitors, ribavirin and favilavir, by relying on atomistic molecular simulations and advanced sampling. By analyzing the binding free energies of these different drugs, it is found that all of them bind strongly at the active site. Surprisingly, however, ribavirin and favilavir do not bind the nucleotide on the complementary strand as effectively and seem to act by a different mechanism than remdesivir. Remdesivir exhibits similar binding interactions to the natural base adenine. Moreover, by analyzing remdesivir at downstream positions of the RNA, we also find that, consistent with a "delayed" termination mechanism, additional nucleotides can be incorporated after remdesivir is added, and its highly polar 1'-cyano group induces a set of conformational changes that can affect the normal RdRp complex function. By analyzing the fluctuations of residues that are altered by remdesivir binding, and comparing them to those induced by lethal point mutations, we find a possible secondary mechanism in which remdesivir destabilizes the protein complex and its interactions with the RNA strands.

Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease.

There is an urgent need to repurpose drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent computational-experimental screenings have identified several existing drugs that could serve as effective inhibitors of the virus' main protease, Mpro, which is involved in gene expression and replication. Among these, ebselen (2-phenyl-1,2-benzoselenazol-3-one) appears to be particularly promising. Here, we examine, at a molecular level, the potential of ebselen to decrease Mpro activity. We find that it exhibits a distinct affinity for the catalytic region. Our results reveal a higher-affinity, previously unknown binding site localized between the II and III domains of the protein. A detailed strain analysis indicates that, on such a site, ebselen exerts a pronounced allosteric effect that regulates catalytic site access through surface-loop interactions, thereby inducing a reconfiguration of water hotspots. Together, these findings highlight the promise of ebselen as a repurposed drug against SARS-CoV-2.

Role of Ligand-Driven Conformational Changes in Enzyme Catalysis: Modeling the Reactivity of the Catalytic Cage of Triosephosphate Isomerase.

We have previously performed empirical valence bond calculations of the kinetic activation barriers, Δ G‡calc, for the deprotonation of complexes between TIM and the whole substrate glyceraldehyde-3-phosphate (GAP, Kulkarni et al. J. Am. Chem. Soc. 2017 , 139 , 10514 - 10525 ). We now extend this work to also study the deprotonation of the substrate pieces glycolaldehyde (GA) and GA·HPi [HPi = phosphite dianion]. Our combined calculations provide activation barriers, Δ G‡calc, for the TIM-catalyzed deprotonation of GAP (12.9 ± 0.8 kcal·mol-1), of the substrate piece GA (15.0 ± 2.4 kcal·mol-1), and of the pieces GA·HPi (15.5 ± 3.5 kcal·mol-1). The effect of bound dianion on Δ G‡calc is small (≤2.6 kcal·mol-1), in comparison to the much larger 12.0 and 5.8 kcal·mol-1 intrinsic phosphodianion and phosphite dianion binding energy utilized to stabilize the transition states for TIM-catalyzed deprotonation of GAP and GA·HPi, respectively. This shows that the dianion binding energy is essentially fully expressed at our protein model for the Michaelis complex, where it is utilized to drive an activating change in enzyme conformation. The results represent an example of the synergistic use of results from experiments and calculations to advance our understanding of enzymatic reaction mechanisms.