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The multivalent binding effect has been the subject of extensive studies to modulate adhesion behaviors of various biological and engineered systems. However, precise control over the strong avidity-based binding remains a significant challenge. Here, a set of engineering strategies are developed and tested to systematically enhance the multivalent binding of peptides in a stepwise manner. Poly(amidoamine) (PAMAM) dendrimers are employed to increase local peptide densities on a substrate, resulting in hierarchically multivalent architectures (HMAs) that display multivalent dendrimer-peptide conjugates (DPCs) with various configurations. To control binding behaviors, effects of the three major components of the HMAs are investigated: i) poly(ethylene glycol) (PEG) linkers as spacers between conjugated peptides; ii) multiple peptides on the DPCs; and iii) various surface arrangements of HMAs (i.e., a mixture of DPCs each containing different peptides vs DPCs cofunctionalized with multiple peptides). The optimized HMA configuration enables significantly enhanced target cell binding with high selectivity compared to the control surfaces directly conjugated with peptides. The engineering approaches presented herein can be applied individually or in combination, providing guidelines for the effective utilization of biomolecular multivalent interactions using DPC-based HMAs.

No abstract

The human Betacoronavirus SARS-CoV-2 is a novel pathogen claiming millions of lives and causing a global pandemic that has disrupted international healthcare systems, economies, and communities. The virus is fast mutating and presenting more infectious but less lethal versions. Currently, some small-molecule therapeutics have received FDA emergency use authorization for the treatment of COVID-19, including Lagevrio (molnupiravir) and Paxlovid (nirmaltrevir/ritonavir), which target the RNA-dependent RNA polymerase and the 3CLpro main protease, respectively. Proteins downstream in the viral replication process, specifically the nonstructural proteins (Nsps1−16), are potential drug targets due to their crucial functions. Of these Nsps, Nsp4 is a particularly promising drug target due to its involvement in the SARS-CoV viral replication and double-membrane vesicle formation (mediated via interaction with Nsp3). Given the degree of sequence conservation of these two Nsps across the Betacoronavirus clade, their protein−protein interactions and functions are likely to be conserved as well in SARS-CoV-2. Through AlphaFold2 and its recent advancements, protein structures were generated of Nsp3 and 4 lumenal loops of interest. Then, using a combination of molecular docking suites and an existing library of lead-like compounds, we virtually screened 7 million ligands to identify five putative ligand inhibitors of Nsp4, which could present an alternative pharmaceutical approach against SARS-CoV-2. These ligands exhibit promising lead-like properties (ideal molecular weight and log P profiles), maintain fixed-Nsp4-ligand complexes in molecular dynamics (MD) simulations, and tightly associate with Nsp4 via hydrophobic interactions. Additionally, alternative peptide inhibitors based on Nsp3 were designed and shown in MD simulations to provide a highly stable binding to the Nsp4 protein. Finally, these therapeutics were attached to dendrimer structures to promote their multivalent binding with Nsp4, especially its large flexible luminal loop (Nsp4LLL). The therapeutics tested in this study represent many different approaches for targeting large flexible protein structures, especially those localized to the ER. This study is the first work targeting the membrane rearrangement system of viruses and will serve as a potential avenue for treating viruses with similar replicative function.

In this paper, we propose a sampling policy considering Bayesian risks. Various definitions of producer"s risk and consumer"s risk have been made. Bayesian risks for both producer and consumer are proven to give better information to decision makers than classical definitions of the risks. So considering the Bayesian risk constraints, we seek to find optimal acceptance sampling policy by minimizing total cost, including the cost of rejecting the batch, the cost of inspection, and the cost of defective items detected during the operation. Proper distributions to construct the objective function of the model are specified. In order to demonstrate the application of the proposed model, we illustrate a numerical example. Furthermore, the results of the sensitivity analysis show that lot size, the cost of inspection and the cost of one defective item are key factors in sampling policies. The acceptable quality level, the lot tolerance proportion defective, and Bayesian risks also affect the sampling policy, but variations of Downloaded by [University of Cambridge] at 14:50 15 June 2016 ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 2 acceptable quality level and producer Bayesian risks, for values more than a specified value, cause no changes in sampling policy.