Schloegl, A. | bioRxiv Science

bioRxiv2023

Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade layer in poxvirus cores

Poxviruses are among the largest double-stranded DNA viruses with members such as Variola virus, Monkeypox virus and the famous vaccination strain Vaccinia virus (VACV). Knowledge about the structural proteins that form the viral core, found in all infectious poxvirus forms, has remained sparse. While major core proteins have been annotated via indirect experimental evidence, their structures have remained elusive and they could not be assigned to the individual architectural features of the core. Hence, which proteins constitute which layers of the core, such as the so-called palisade layer and the inner core wall has remained enigmatic. Here, we have performed a multi-modal cryo-electron microscopy (cryo-EM) approach to elucidate the structural determinants of the VACV core. In combination with molecular modeling using AlphaFold, we unambiguously identify trimers formed by the cleavage product of A10 as the key component of the palisade layer. This allows us to place previously-obtained descriptions of protein interactions within the core wall into perspective and to provide a substantially revised model of poxvirus core architecture. Importantly, we show that interactions within A10 trimers are likely identical among Poxviridae, implying that our structural observations should be generalizable over most, if not all members of this important virus family. One sentence summarySingle-particle cryo-EM, cryo-electron tomography, and AlphaFold modeling reveal the structural architecture of the poxvirus core and identify trimers of protein A10 as the key component of the palisade layer.

molecular biology↗

bioRxiv2020

MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo

To understand the mechanisms of information coding in single neurons, it is necessary to analyze subthreshold synaptic events, action potentials (APs), and the interrelation between these two forms of activity in different behavioral states. However, detecting excitatory postsynaptic potentials (EPSPs) or currents (EPSCs) in awake, behaving animals remains challenging, because of unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and variable time course of synaptic events. Here, we developed a new method for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure), which combines concepts of supervised machine learning and optimal Wiener filtering. First, experts were asked to manually score short epochs of data. Second, the algorithm was trained to obtain the optimal filter coefficients of a Wiener filter and the optimal detection threshold. Third, scored and unscored data were processed with the optimal filter, and events were detected as peaks above threshold. Finally, the area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to quantify accuracy and efficiency of detection. Additionally, cross-validation was performed to exclude overfitting of the scored data, a potential concern with machine-learning approaches. We then challenged the new detection method with EPSP traces in vivo in mice during spatial navigation and EPSC traces in vitro in slices under conditions of enhanced transmitter release. When benchmarked using a (1-AUC)-1 metric, MOD outperformed previous methods (template-fit and deconvolution) by a factor of up to 3. Thus, MOD may become an important tool for large-scale analysis of synaptic activity in vivo and in vitro. HighlightsO_LIA new method for detection of synaptic events, termed MOD, is described C_LIO_LIThe method combines the concepts of supervised machine learning and optimal filtering C_LIO_LIThe method is useful for analysis of both in vitro and in vivo data sets C_LIO_LIMOD outperforms previously published methods for synaptic event detection by a factor of up to 3 C_LI

neuroscience↗