Targeting Beta-Blocker Drug-Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study

Are fibrinaloid microclots a cause of autoimmunity in Long Covid and other post-infection diseases?

It is now well established that the blood-clotting protein fibrinogen can polymerise into an anomalous form of fibrin that is amyloid in character; the resultant clots and microclots entrap many other molecules, stain with fluorogenic amyloid stains, are rather resistant to fibrinolysis, can block up microcapillaries, are implicated in a variety of diseases including Long COVID, and have been referred to as fibrinaloids. A necessary corollary of this anomalous polymerisation is the generation of novel epitopes in proteins that would normally be seen as 'self', and otherwise immunologically silent. The precise conformation of the resulting fibrinaloid clots (that, as with prions and classical amyloid proteins, can adopt multiple, stable conformations) must depend on the existing small molecules and metal ions that the fibrinogen may (and is some cases is known to) have bound before polymerisation. Any such novel epitopes, however, are likely to lead to the generation of autoantibodies. A convergent phenomenology, including distinct conformations and seeding of the anomalous form for initiation and propagation, is emerging to link knowledge in prions, prionoids, amyloids and now fibrinaloids. We here summarise the evidence for the above reasoning, which has substantial implications for our understanding of the genesis of autoimmunity (and the possible prevention thereof) based on the primary process of fibrinaloid formation.

A novel insight into mechanism of derangement of coagulation balance: interactions of quantum dots with coagulation-related proteins

Background

Quantum dots (QDs) have gained increased attention for their extensive biomedical and electronic products applications. Due to the high priority of QDs in contacting the circulatory system, understanding the hemocompatibility of QDs is one of the most important aspects for their biosafety evaluation. Thus far, the effect of QDs on coagulation balance haven’t been fully understood, and limited studies also have yet elucidated the potential mechanism from the perspective of interaction of QDs with coagulation-related proteins.

Results

QDs induced the derangement of coagulation balance by prolonging the activated partial thromboplastin time and prothrombin time as well as changing the expression levels of coagulation and fibrinolytic factors. The contact of QDs with PTM (prothrombin), PLG (plasminogen) and FIB (fibrinogen) which are primary coagulation-related proteins in the coagulation and fibrinolysis systems formed QDs-protein conjugates through hydrogen-bonding and hydrophobic interaction. The affinity of proteins with QDs followed the order of PTM > PLG > FIB, and was larger with CdTe/ZnS QDs than CdTe QDs. Binding with QDs not only induced static fluorescence quenching of PTM, PLG and FIB, but also altered their conformational structures. The binding of QDs to the active sites of PTM, PLG and FIB may promote the activation of proteins, thus interfering the hemostasis and fibrinolysis processes.

Conclusions

The interactions of QDs with PTM, PLG and FIB may be key contributors for interference of coagulation balance, that is helpful to achieve a reliable and comprehensive evaluation on the potential biological influence of QDs from the molecular level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00458-x.

Elevated Fibrinogen Level Reduces Therapeutic Efficiency of AD Drugs: Biophysical Insights into the Interaction of FDA-Approved Cholinesterase Inhibitors with Human Fibrinogen

Despite being the second most abundant protein in blood plasma, reports on the interaction of drugs with fibrinogen (FIB) are relatively scarce. The effect of FIB on the therapeutic potency of four FDA-approved Alzheimer's disease drugs, namely, tacrine (TAC), donepezil (DON), eserine (ESE), and huperzine (HUP), was investigated through a combination of different in vitro and in silico experiments. The efficiency of the drugs in inhibiting the activity of acetylcholinesterase (AChE) was significantly reduced in the presence of FIB. This effect was even found to be more substantial than that for the most abundant plasma protein, human serum albumin (HSA). For example, the relative change in IC₅₀ for TAC was found to be 65% in 10 μM FIB as opposed to 43% in the presence of 250 μM HSA. The relative trend of modulation in AChE activity showed consistency with the binding efficiency of the drugs and FIB. The sequestration of drugs in FIB, therefore reducing the availability of free drugs in solution, was identified to be the primary cause for the decrease in the AChE inhibition potency. This study aims to establish FIB as a vital component, while considering the therapeutic effectiveness of different newly developed AChE inhibitors.

COVID-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning

The life-threatening disease COVID-19 has inspired significant efforts to discover novel therapeutic agents through repurposing of existing drugs. Although multi-targeted (polypharmacological) therapies are recognized as the most efficient approach to system diseases such as COVID-19, computational multi-targeted compound screening has been limited by the scarcity of high-quality experimental data and difficulties in extracting information from molecules. This study introduces MolGNN, a new deep learning model for molecular property prediction. MolGNN applies a graph neural network to computational learning of chemical molecule embedding. Comparing to state-of-the-art approaches heavily relying on labeled experimental data, our method achieves equivalent or superior prediction performance without manual labels in the pretraining stage, and excellent performance on data with only a few labels. Our results indicate that MolGNN is robust to scarce training data, and hence a powerful few-shot learning tool. MolGNN predicted several multi-targeted molecules against both human Janus kinases and the SARS-CoV-2 main protease, which are preferential targets for drugs aiming, respectively, at alleviating cytokine storm COVID-19 symptoms and suppressing viral replication. We also predicted molecules potentially inhibiting cell death induced by SARS-CoV-2. Several of MolGNN top predictions are supported by existing experimental and clinical evidence, demonstrating the potential value of our method.