NESCA, a newly discovered signaling adapter protein in the NGF-pathway, contains a RUN domain at its N-terminus. Here we report the crystal structure of the NESCA RUN domain determined at 2.0-Å resolution. The overall fold of the NESCA RUN domain comprises nine helices, resembling the RUN domain of RPIPx and the RUN1 domain of Rab6IP1. However, compared to the other RUN domains, the RUN domain of NESCA has significantly different surface electrostatic distributions at the putative GTPase-interacting interface. We demonstrate that the RUN domain of NESCA can bind H-Ras, a downstream signaling molecule of TrkA, with high affinity. Moreover, NESCA RUN can directly interact with TrkA. These results provide new insights into how NESCA participates in the NGF-TrkA signaling pathway.
Adaptor Proteins, Signal Transducing ; chemistry ; genetics ; metabolism ; Amino Acid Sequence ; Binding Sites ; Crystallography, X-Ray ; Gene Expression ; Humans ; Models, Molecular ; Molecular Sequence Data ; Nerve Growth Factor ; chemistry ; genetics ; metabolism ; Oncogene Protein p21(ras) ; chemistry ; genetics ; metabolism ; Protein Binding ; Protein Structure, Tertiary ; Receptor, trkA ; chemistry ; genetics ; metabolism ; Recombinant Proteins ; chemistry ; genetics ; metabolism ; Sequence Homology, Amino Acid ; Signal Transduction ; rab GTP-Binding Proteins ; chemistry

Motor dysfunction is the main clinical symptom and diagnosis basis of patients with Parkinson's disease (PD). A total of 30 subjects were recruited in this study, including 15 PD patients (PD group) and 15 healthy subjects (control group). Then 5 wearable inertial sensor nodes were worn on the bilateral upper limbs, lower limbs and waist of subjects. When completing the 6 paradigm tasks, the acceleration and angular velocity signals from different parts of the body were acquired and analyzed to obtain 20 quantitative parameters which contain information about the amplitude, frequency, and fatigue degree of movements to assess the motor function. The clinical data of the two groups were statistically analyzed and compared, and then Back Propagation (BP) Neural Network was used to classify the two groups and predict the clinical score. The final results showed that most of the parameters had significant difference between the two groups, ten times of 5-fold cross validation showed that the classification accuracy of the BP Neural Network for the two groups was 90%, and the predictive accuracy of Hoehn-Yahr (H-Y) staging and unified PD rating scale (UPDRS) Ⅲ score of the patients were 72.80% and 68.64%, respectively. This study shows the feasibility of quantitative assessment of motor function in PD patients using wearable sensors, and the quantitative parameters obtained in this paper may have reference value for future related research.