Effect of Cholesterol on Biomimetic Membrane Curvature and Coronavirus Fusion Peptide Encapsulation.

Biomimetic cubic phases can be used for protein encapsulation in a variety of applications such as biosensors and drug delivery. Cubic phases with a high concentration of cholesterol and phospholipids were obtained herein. It is shown that the cubic phase structure can be maintained with a higher concentration of biomimetic membrane additives than has been reported previously. Opposing effects on the curvature of the membrane were observed upon the addition of phospholipids and cholesterol. Furthermore, the coronavirus fusion peptide significantly increased the negative curvature of the biomimetic membrane with cholesterol. We show that the viral fusion peptide can undergo structural changes leading to the formation of hydrophobic α-helices that insert into the lipid bilayer. This is of high importance, as a fusion peptide that induces increased negative curvature as shown by the formation of inverse hexagonal phases allows for greater contact area between two membranes, which is required for viral fusion to occur. The cytotoxicity assay showed that the toxicity toward HeLa cells was dramatically decreased when the cholesterol or peptide level in the nanoparticles increased. This suggests that the addition of cholesterol can improve the biocompatibility of the cubic phase nanoparticles, making them safer for use in biomedical applications. As the results, this work improves the potential for the biomedical end-use applications of the nonlamellar lipid nanoparticles and shows the need of systematic formulation studies due to the complex interplay of all components.

Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19.

Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect a characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposure assessment.

Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly.

Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real-world applications. Here, a novel graph-based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto-regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment-based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS-COV-2.

Corrigendum: Development and Delivery Systems of mRNA Vaccines.

[This corrects the article DOI: 10.3389/fbioe.2021.718753.].

Development and Delivery Systems of mRNA Vaccines.

Since the outbreak of SARS-CoV-2, mRNA vaccine development has undergone a tremendous drive within the pharmaceutical field. In recent years, great progress has been made into mRNA vaccine development, especially in individualized tumor vaccines. mRNA vaccines are a promising approach as the production process is simple, safety profiles are better than those of DNA vaccines, and mRNA-encoded antigens are readily expressed in cells. However, mRNA vaccines also possess some inherent limitations. While side effects such as allergy, renal failure, heart failure, and infarction remain a risk, the vaccine mRNA may also be degraded quickly after administration or cause cytokine storms. This is a substantial challenge for mRNA delivery. However, appropriate carriers can avoid degradation and enhance immune responses, effector presentation, biocompatibility and biosafety. To understand the development and research status of mRNA vaccines, this review focuses on analysis of molecular design, delivery systems and clinical trials of mRNA vaccines, thus highlighting the route for wider development and further clinical trials of mRNA vaccines.

Computational optimization of angiotensin-converting enzyme 2 for SARS-CoV-2 Spike molecular recognition.

Since the beginning of the Covid19 pandemic, many efforts have been devoted to identifying approaches to neutralize SARS-CoV-2 replication within the host cell. A promising strategy to block the infection consists of using a mutant of the human receptor angiotensin-converting enzyme 2 (ACE2) as a decoy to compete with endogenous ACE2 for the binding to the SARS-CoV-2 Spike protein, which decreases the ability of the virus to enter the host cell. Here, using a computational framework based on the 2D Zernike formalism we investigate details of the molecular binding and evaluate the changes in ACE2-Spike binding compatibility upon mutations occurring in the ACE2 side of the molecular interface. We demonstrate the efficacy of our method by comparing our results with experimental binding affinities changes upon ACE2 mutations, separating ones that increase or decrease binding affinity with an Area Under the ROC curve ranging from 0.66 to 0.93, depending on the magnitude of the effects analyzed. Importantly, the iteration of our approach leads to the identification of a set of ACE2 mutants characterized by an increased shape complementarity with Spike. We investigated the physico-chemical properties of these ACE2 mutants and propose them as bona fide candidates for Spike recognition.