In Silico and In Vivo Studies of a Tumor-Penetrating and Interfering Peptide with Antitumoral Effect on Xenograft Models of Breast Cancer.

The combination of a tumor-penetrating peptide (TPP) with a peptide able to interfere with a given protein-protein interaction (IP) is a promising strategy with potential clinical application. Little is known about the impact of fusing a TPP with an IP, both in terms of internalization and functional effect. Here, we analyze these aspects in the context of breast cancer, targeting PP2A/SET interaction, using both in silico and in vivo approaches. Our results support the fact that state-of-the-art deep learning approaches developed for protein-peptide interaction modeling can reliably identify good candidate poses for the IP-TPP in interaction with the Neuropilin-1 receptor. The association of the IP with the TPP does not seem to affect the ability of the TPP to bind to Neuropilin-1. Molecular simulation results suggest that peptide IP-GG-LinTT1 in a cleaved form interacts with Neuropilin-1 in a more stable manner and has a more helical secondary structure than the cleaved IP-GG-iRGD. Surprisingly, in silico investigations also suggest that the non-cleaved TPPs can bind the Neuropilin-1 in a stable manner. The in vivo results using xenografts models show that both bifunctional peptides resulting from the combination of the IP and either LinTT1 or iRGD are effective against tumoral growth. The peptide iRGD-IP shows the highest stability to serum proteases degradation while having the same antitumoral effect as Lin TT1-IP, which is more sensitive to proteases degradation. Our results support the development of the TPP-IP strategy as therapeutic peptides against cancer.

In silico discovery of non-psychoactive scaffolds in Cannabis halting SARS-CoV-2 host entry and replication machinery.

Aim: Coronavirus disease still poses a global health threat which advocates continuous research efforts to develop effective therapeutics. Materials & methods: We screened out an array of 29 cannabis phytoligands for their viral spike-ACE2 complex and main protease (Mpro) inhibitory actions by in silico modeling to explore their possible dual viral entry and replication machinery inhibition. Physicochemical and pharmacokinetic parameters (ADMET) formulating drug-likeness were computed. Results: Among the studied phytoligands, cannabigerolic acid (2), cannabigerol (8), and its acid methyl ether (3) possessed the highest binding affinities to SARS-CoV-hACE2 complex essential for viral entry. Canniprene (24), cannabigerolic methyl ether (3) and cannabichromene (9) were the most promising Mpro inhibitors. Conclusion: These non-psychoactive cannabinoids could represent plausible therapeutics with added-prophylactic value as they halt both viral entry and replication machinery.

Heterogeneous Infectivity and Pathogenesis of SARS-CoV-2 Variants Beta, Delta and Omicron in Transgenic K18-hACE2 and Wildtype Mice.

The emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2.

From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling.

Background: Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies. Aim: Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease. Methods: We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics. Results: By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

Strawberry and Ginger Silver Nanoparticles as Potential Inhibitors for SARS-CoV-2 Assisted by In Silico Modeling and Metabolic Profiling.

SARS-CoV-2 (COVID-19), a novel coronavirus causing life-threatening pneumonia, caused a pandemic starting in 2019 and caused unprecedented economic and health crises all over the globe. This requires the rapid discovery of anti-SARS-CoV-2 drug candidates to overcome this life-threatening pandemic. Strawberry (Fragaria ananassa Duch.) and ginger (Zingiber officinale) methanolic extracts were used for silver nanoparticle (AgNPs) synthesis to explore their SARS-CoV-2 inhibitory potential. Moreover, an in silico study was performed to explore the possible chemical compounds that might be responsible for the anti-SARS-CoV-2 potential. The characterization of the green synthesized AgNPs was carried out with transmission electron microscope (TEM), Fourier-transform infrared, spectroscopy ultraviolet-visible spectroscopy, zeta potential, and a dynamic light-scattering technique. The metabolic profiling of strawberry and ginger methanolic extract was assessed using liquid chromatography coupled with high-resolution mass spectrometry. The antiviral potential against SARS-CoV-2 was evaluated using an MTT assay. Moreover, in silico modeling and the molecular dynamic study were conducted via AutoDock Vina to demonstrate the potential of the dereplicated compounds to bind to some of the SARS-CoV-2 proteins. The TEM analysis of strawberry and ginger AgNPs showed spherical nanoparticles with mean sizes of 5.89 nm and 5.77 nm for strawberry and ginger, respectively. The UV-Visible spectrophotometric analysis showed an absorption peak at λmax of 400 nm for strawberry AgNPs and 405 nm for ginger AgNPs. The Zeta potential values of the AgNPs of the methanolic extract of strawberry was -39.4 mV, while for AgNPs of ginger methanolic extract it was -42.6 mV, which indicates a high stability of the biosynthesized nanoparticles. The strawberry methanolic extract and the green synthesized AgNPs of ginger showed the highest antiviral activity against SARS-CoV-2. Dereplication of the secondary metabolites from the crude methanolic extracts of strawberry and ginger resulted in the annotation of different classes of compounds including phenolic, flavonoids, fatty acids, sesquiterpenes, triterpenes, sterols, and others. The docking study was able to predict the different patterns of interaction between the different compounds of strawberry and ginger with seven SARS-CoV-2 protein targets including five viral proteins (Mpro, ADP ribose phosphatase, NSP14, NSP16, PLpro) and two humans (AAK1, Cathepsin L). The molecular docking and dynamics simulation study showed that neohesperidin demonstrated the potential to bind to both human AAK1 protein and SARS-CoV-2 NSP16 protein, which makes this compound of special interest as a potential dual inhibitor. Overall, the present study provides promise for Anti-SARS-CoV-2 green synthesized AgNPs, which could be developed in the future into a new anti-SARS-CoV-2 drug.

Prioritized Research for the Prevention, Treatment, and Reversal of Chronic Disease: Recommendations From the Lifestyle Medicine Research Summit.

Declining life expectancy and increasing all-cause mortality in the United States have been associated with unhealthy behaviors, socioecological factors, and preventable disease. A growing body of basic science, clinical research, and population health evidence points to the benefits of healthy behaviors, environments and policies to maintain health and prevent, treat, and reverse the root causes of common chronic diseases. Similarly, innovations in research methodologies, standards of evidence, emergence of unique study cohorts, and breakthroughs in data analytics and modeling create new possibilities for producing biomedical knowledge and clinical translation. To understand these advances and inform future directions research, The Lifestyle Medicine Research Summit was convened at the University of Pittsburgh on December 4-5, 2019. The Summit's goal was to review current status and define research priorities in the six core areas of lifestyle medicine: plant-predominant nutrition, physical activity, sleep, stress, addictive behaviors, and positive psychology/social connection. Forty invited subject matter experts (1) reviewed existing knowledge and gaps relating lifestyle behaviors to common chronic diseases, such as cardiovascular disease, diabetes, many cancers, inflammatory- and immune-related disorders and other conditions; and (2) discussed the potential for applying cutting-edge molecular, cellular, epigenetic and emerging science knowledge and computational methodologies, research designs, and study cohorts to accelerate clinical applications across all six domains of lifestyle medicine. Notably, federal health agencies, such as the Department of Defense and Veterans Administration have begun to adopt "whole-person health and performance" models that address these lifestyle and environmental root causes of chronic disease and associated morbidity, mortality, and cost. Recommendations strongly support leveraging emerging research methodologies, systems biology, and computational modeling in order to accelerate effective clinical and population solutions to improve health and reduce societal costs. New and alternative hierarchies of evidence are also be needed in order to assess the quality of evidence and develop evidence-based guidelines on lifestyle medicine. Children and underserved populations were identified as prioritized groups to study. The COVID-19 pandemic, which disproportionately impacts people with chronic diseases that are amenable to effective lifestyle medicine interventions, makes the Summit's findings and recommendations for future research particularly timely and relevant.

Molecular docking simulation reveals ACE2 polymorphisms that may increase the affinity of ACE2 with the SARS-CoV-2 Spike protein.

There is increasing evidence that ACE2 gene polymorphism can modulate the interaction between ACE2 and the SARS-CoV-2 spike protein affecting the viral entry into the host cell, and/or contribute to lung and systemic damage in COVID-19. Here we used in silico molecular docking to predict the effects of ACE2 missense variants on the interaction with the spike protein of SARS-CoV-2. HDOCK and FireDock simulations identified 6 ACE2 missense variants (I21T, A25T, K26R, E37K, T55A, E75G) with higher affinity for SARS-CoV-2 Spike protein receptor binding domain (RBD) with respect to wild type ACE2, and 11 variants (I21V, E23K, K26E, T27A, E35K, S43R, Y50F, N51D, N58H, K68E, M82I) with lower affinity. This result supports the hypothesis that ACE2 genetic background may represent the first "genetic gateway" during the disease progression.