ABSTRACT
The phenomenon of protein aggregation is associated with a wide range of human diseases. Our knowledge of the aggregation behaviour of viral proteins, however, is still rather limited. Here, we investigated this behaviour in the SARS-CoV and SARS-CoV-2 proteomes. An initial analysis using a panel of sequence-based predictors suggested the presence of multiple aggregation-prone regions (APRs) in these proteomes and revealed a strong aggregation propensity in some SARS-CoV-2 proteins. We then studied the in vitro aggregation of predicted aggregation-prone SARS-CoV and SARS-CoV-2 proteins and protein regions, including the signal sequence peptide and fusion peptides 1 and 2 of the spike protein, a peptide from the NSP6 protein, and the ORF10 and NSP11 proteins. Our results show that these peptides and proteins can form amyloid aggregates. We used circular dichroism spectroscopy to reveal the presence of ß-sheet rich cores in aggregates and X-ray diffraction and Raman spectroscopy to confirm the formation of amyloid structures. Furthermore, we demonstrated that SARS-CoV-2 NSP11 aggregates are toxic to mammalian cell cultures. These results motivate further studies about the possible role of aggregation of SARS proteins in protein misfolding diseases and other human conditions.
Subject(s)
COVID-19 , Severe acute respiratory syndrome-related coronavirus , Humans , Animals , Amyloidogenic Proteins , Proteome , SARS-CoV-2 , MammalsABSTRACT
Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties given an ergodic sampling and an accurate force-field. The force-field parameters are calibrated to reproduce properties measured by experiments or simulations. The main contribution of this paper is an approximate Bayesian framework for the calibration and uncertainty quantification of the force-field parameters, without assuming parameter uncertainty to be Gaussian. To this aim, since the likelihood function of the MD simulation models is intractable in the absence of Gaussianity assumption, we use a likelihood-free inference scheme known as approximate Bayesian computation (ABC) and propose an adaptive population Monte Carlo ABC algorithm, which is illustrated to converge faster and scales better than the previously used ABCsubsim algorithm for the calibration of the force-field of a helium system. The second contribution is the adaptation of ABC algorithms for High Performance Computing to MD simulations within the Python ecosystem ABCpy. This adaptation includes a novel use of a dynamic allocation scheme for Message Passing Interface (MPI). We illustrate the performance of the developed methodology to learn posterior distribution and Bayesian estimates of Lennard-Jones force-field parameters of helium and the TIP4P system of water implemented for both simulated and experimental datasets collected using neutron and X-ray diffraction. For simulated data, the Bayesian estimate is in close agreement with the true parameter value used to generate the dataset. For experimental as well as for simulated data, the Bayesian posterior distribution shows a strong correlation pattern between the force-field parameters. Providing an estimate of the entire posterior distribution, our methodology also allows us to perform the uncertainty quantification of model prediction. This research opens up the possibility to rigorously calibrate force-fields from available experimental datasets of any structural and dynamic property.
ABSTRACT
Dysfunction of D-amino acid oxidase (DAO) and DAO activator (DAOA)/G72 genes have been linked to neuropsychiatric disorders. The glutamate hypothesis of schizophrenia has proposed that increased DAO activity leads to decreased D-serine, which subsequently may lead to N-methyl-D-aspartate (NMDA) receptor hypofunction. It has been shown that DAOA binds to DAO and increases its activity. However, there are also studies showing DAOA decreases DAO activity. Thus, the effect of DAOA on DAO is controversial. We aimed to understand the effect of DAOA on DAO activity in neuron-like (SH-SY5Y), astrocyte-like (1321N1) and kidney-like (HEK293) human cell lines. DAO activity was measured based on the release of hydrogen peroxide and its interaction with Amplex Red reagent. We found that DAOA increases DAO activity only in HEK293 cells, but has no effect on DAO activity in SH-SY5Y and 1321N1 cells. This might be because of different signaling pathways, or due to lower DAO and DAOA expression in SH-SY5Y and 1321N1 cells compared to HEK293 cells, but also due to different compartmentalization of the proteins. The lower DAO and DAOA expression in neuron-like SH-SY5Y and astrocyte-like 1321N1 cells might be due to tightly regulated expression, as previously reported in the human post-mortem brain. Our simulation experiments to demonstrate the interaction between DAOA and human DAO (hDAO) showed that hDAO holoenzyme [hDAO with flavine adenine dinucleotide (FAD)] becomes more flexible and misfolded in the presence of DAOA, whereas DAOA had no effect on hDAO apoprotein (hDAO without FAD), which indicate that DAOA inactivates hDAO holoenzyme. Furthermore, patch-clamp analysis demonstrated no effect of DAOA on NMDA receptor activity in NR1/NR2A HEK293 cells. In summary, the interaction between DAO and DAOA seems to be cell type and its biochemical characteristics dependent which still needs to be elucidated.
