Atomic resolution map of the solvent interactions driving SOD1 unfolding in CAPRIN1 condensates.

Biomolecules can be sequestered into membrane-less compartments, referred to as biomolecular condensates. Experimental and computational methods have helped define the physical-chemical properties of condensates. Less is known about how the high macromolecule concentrations in condensed phases contribute "solvent" interactions that can remodel the free-energy landscape of other condensate-resident proteins, altering thermally accessible conformations and, in turn, modulating function. Here, we use solution NMR spectroscopy to obtain atomic resolution insights into the interactions between the immature form of superoxide dismutase 1 (SOD1), which can mislocalize and aggregate in stress granules, and the RNA-binding protein CAPRIN1, a component of stress granules. NMR studies of CAPRIN1:SOD1 interactions, focused on both unfolded and folded SOD1 states in mixed phase and demixed CAPRIN1-based condensates, establish that CAPRIN1 shifts the SOD1 folding equilibrium toward the unfolded state through preferential interactions with the unfolded ensemble, with little change to the structure of the folded conformation. Key contacts between CAPRIN1 and the H80-H120 region of unfolded SOD1 are identified, as well as SOD1 interaction sites near both the arginine-rich and aromatic-rich regions of CAPRIN1. Unfolding of immature SOD1 in the CAPRIN1 condensed phase is shown to be coupled to aggregation, while a more stable zinc-bound, dimeric form of SOD1 is less susceptible to unfolding when solvated by CAPRIN1. Our work underscores the impact of the condensate solvent environment on the conformational states of resident proteins and supports the hypothesis that ALS mutations that decrease metal binding or dimerization function as drivers of aggregation in condensates.

PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

The protease associated (PA) domain in ScpA from Streptococcus pyogenes plays a role in substrate recruitment.

Annually, over 18 million disease cases and half a million deaths worldwide are estimated to be caused by Group A Streptococcus. ScpA (or C5a peptidase) is a well characterised member of the cell enveleope protease family, which possess a S8 subtilisin-like catalytic domain and a shared multi-domain architecture. ScpA cleaves complement factors C5a and C3a, impairing the function of these critical anaphylatoxins and disrupts complement-mediated innate immunity. Although the high resolution structure of ScpA is known, the details of how it recognises its substrate are only just emerging. Previous studies have identified a distant exosite on the 2nd fibronectin domain that plays an important role in recruitment via an interaction with the substrate core. Here, using a combination of solution NMR spectroscopy, mutagenesis with functional assays and computational approaches we identify a second exosite within the protease-associated (PA) domain. We propose a model in which the PA domain assists optimal delivery of the substrate's C terminus to the active site for cleavage.