Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors.

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions. RESULTS: We developed BioNet, a deep biological networkmodel with a graph encoder-decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.

Discovery of Small Anti-ACE2 Peptides to Inhibit SARS-CoV-2 Infectivity (Adv. Therap. 7/2021)

SARS-CoV-2 infects human cells by binding its spike protein to the human ACE2 receptor. Using a peptide biopanning strategy, the authors have discovered small anti-ACE2 peptides that can effectively block the SARS-CoV-2/ACE2 interaction. The anti-ACE2 peptides can be potentially used as prophylactic or therapeutic agents for SARS-CoV-2 and other ACE2-mediated viruses. This is reported by Kun Cheng and co-workers in article number 2100087.

The Importance of Apparent pKa in the Development of Nanoparticles Encapsulating siRNA and mRNA.

Polymer and lipid nanoparticles have been extensively used as carriers to address the biological barriers encountered in siRNA and mRNA delivery. We summarize the crucial role of nanoparticle charge and ionizability in complexing RNAs, binding to biological components, escaping from the endosome, and releasing RNAs into the cytoplasm. We highlight the significant impact of the apparent pKa of nanoparticles on their efficacy and toxicity, and the importance of optimizing pKa in the development of lead formulations for RNAs. We also discuss the feasibility of fine-tuning the pKa in nanoparticles and the applications of this approach in the optimization of delivery systems for RNAs.

Discovery of Small Anti-ACE2 Peptides to Inhibit SARS-CoV-2 Infectivity.

COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which infects host cells by binding its viral spike protein receptor-binding domain (RBD) to the angiotensin converting enzyme 2 (ACE2) on host cells. Blocking the SARS-CoV-2-RBD/ACE2 interaction is, therefore, a potential strategy to inhibit viral infections. Using a novel biopanning strategy, a small anti-ACE2 peptide is discovered, which shows high affinity and specificity to human ACE2. It blocks not only the SARS-CoV-2-RBD/ACE2 interaction but also the SARS-CoV-1-RBD/ACE2 interaction. Moreover, it inhibits SARS-CoV-2 infection in Vero-E6 cells. The peptide shows negligible cytotoxicity in Vero-E6 cells and Huh7 cells. In vivo short-term lung toxicity study also demonstrates a good safety of the peptide after intratracheal administration. The anti-ACE2 peptide can be potentially used as a prophylactic or therapeutic agent for SARS-CoV-2 or other ACE2-mediated viruses. The strategy used in this study also provides a fast-track platform to discover other antiviral peptides, which will prepare the world for future pandemics.