SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.

The mesoscale organization of syntaxin 1A and SNAP25 is determined by SNARE-SNARE-interactions

SNARE proteins have been described as the effectors of fusion events in the secretory pathway more than two decades ago. The strong interactions between SNARE-domains are clearly important in membrane fusion, but it is unclear whether they are involved in any other cellular processes. Here, we analyzed two classical SNARE proteins, syntaxin 1A and SNAP25. Although they are supposed to be engaged in tight complexes, we surprisingly find them largely segregated in the plasma membrane. Syntaxin 1A only occupies a small fraction of the plasma membrane area. Yet, we find it is able to redistribute the far more abundant SNAP25 on the mesoscale by gathering crowds of SNAP25 molecules onto syntaxin-clusters in a SNARE-domain dependent manner. Our data suggests that SNARE-domain interactions are not only involved in driving membrane fusion on the nanoscale, but also play an important role in controlling the general organization of proteins on the mesoscale. Further, we propose this mechanisms preserves active syntaxin 1A-SNAP25 complexes at the plasma membrane.

A docked mutation phenocopies dumpy oblique alleles via altered vesicle trafficking

The Drosophila extracellular matrix protein Dumpy (Dpy) is one of the largest proteins encoded by any animal. One class of dpy mutations produces a characteristic shortening of the wing blade known as oblique (dpyo), due to altered tension in the developing wing. We describe here the characterization of docked (doc), a gene originally named because of an allele producing a truncated wing. We show that doc corresponds to the gene model CG5484, which encodes a homolog of the yeast protein Yif1 and plays a key role in ER to Golgi vesicle transport. Genetic analysis is consistent with a similar role for Doc in vesicle trafficking: docked alleles interact not only with genes encoding the COPII core proteins sec23 and sec13, but also with the SNARE proteins synaptobrevin and syntaxin. Further, we demonstrate that the strong similarity between the doc1 and dpyo wing phenotypes reflects a functional connection between the two genes; we found that various dpy alleles are sensitive to changes in dosage of genes encoding other vesicle transport components such as sec13 and sar1. Doc’s effects on trafficking are not limited to Dpy; for example, reduced doc dosage disturbed Notch pathway signaling during wing blade and vein development. These results suggest a model in which the oblique wing phenotype in doc1 results from reduced transport of wild-type Dumpy protein; by extension, an additional implication is that the dpyo alleles can themselves be explained as hypomorphs.

Identification of two pathways mediating protein targeting from ER to lipid droplets

Pathways localizing proteins to their sites of action within a cell are essential for eukaryotic cell organization and function. Although mechanisms of protein targeting to many organelles have been defined, little is known about how proteins, such as key metabolic enzymes, target from the ER to cellular lipid droplets (LDs). Here, we identify two distinct pathways for ER-to-LD (ERTOLD) protein targeting: early ERTOLD, occurring during LD formation, and late ERTOLD, targeting mature LDs after their formation. By using systematic, unbiased approaches, we identified specific membrane-fusion machinery, including regulators, a tether, and SNARE proteins, that are required for late ERTOLD targeting. Components of this fusion machinery localize to LD-ER interfaces and appear to be organized at ER exit sites (ERES) to generate ER-LD membrane bridges. We also identified multiple cargoes for early and late ERTOLD. Collectively, our data provide a new model for how proteins target LDs from the ER.

Effect of metformin on the expression of SNAER proteins in the skeletal muscle of rats with type 2 diabetes

Background and Aims: SNARE proteins are composed of a combination of SNAP-23, Stx-4, and VAMP-2 isoforms that are significantly expressed in skeletal muscle. These proteins control the transport of GLUT4 to the cell membranes. The modifications in the expression of SNARE proteins can cause Type 2 diabetes. The present study aimed to assess the effect of metformin on the expression of these proteins in rats. Materials and Methods: For the purpose of the study, 40 male Wistar rats were randomly selected. Streptozotocin and Nicotinamide were used for the induction of type 2 diabetes. The animals were assigned to five groups (n=8), including healthy and diabetic groups as control, as well as three experimental groups which were treated with different doses of metformin (100, 150, and 200 mg/kg body weight) for 30 days. The quantitative reverse transcription PCR (RT-qPCR) method was applied to evaluate the expression of SNARE complex proteins.. Results: Based on the results, metformin (100, 150, and 200 mg/kg body weight) decreased serum glucose levels and increased serum insulin levels. This difference in dose of 200 mg/kg body weight was statistically significant (P<0.05). Moreover, all three doses of metformin increased the expression of SNAP- 23, syntaxin-4, and VAMP-2 proteins in skeletal muscle tissue. Metformin at a dose of 200 mg/kg body weight demonstrated the most significant effects (P<0.05). Conclusion: As evidenced by the results of the current study, another anti-diabetic mechanism of metformin is to increase the expression of SNARE proteins, which effectively improves insulin resistance and lowers blood glucose.

SNARE-dependent mitochondria-lysosome contacts regulate glial mitochondrial dynamics and dopaminergic neuron survival

Mitochondria are dynamic organelles that undergo fission and fusion, enabling swift structural modification to adapt cellular needs. Disturbances in mitochondrial dynamics, frequently defects ascribed to neurons, have been associated with pathological contexts such as neurodegeneration in Parkinson's disease. Nonetheless, the mechanism of glial mitochondrial dynamics contributing to neurodegeneration remains unclear. Here we present evidence that the Drosophila R-SNARE VAMP7 regulates glial mitochondrial dynamics and dopaminergic neuron survival via modulating the dynamic of mitochondria-lysosome contact, which determines the mitochondrial fission site. Independent of its characterized role in autophagosome-lysosome fusion, glial VAMP7 depletion causes mitochondrial elongation and dysfunction, increased ROS levels, and production of lipid droplets. These conferred changes in glia in turn affects nearby dopaminergic neuron survival. Glial VAMP7 genetically interacts with the mitochondrial fission/fusion factors Drp1 and Marf1 and controls glial mitochondrial dynamics via regulating the frequency and duration of mitochondria-lysosome contact. Our findings indicate that SNARE proteins, although not direct mediators on mitochondrial fusion, provide spatial cues to modulate glial mitochondrial fission via organelle contacts, impacting on neuron survival in a non-cell-autonomous manner.

SNARER: New Molecular Descriptors for SNARE Proteins Classification

Background: SNARE proteins play an important role in different biological functions. This study aims to investigate the contribution of a new class of molecular descriptors (called SNARER) related to the chemical-physical properties of proteins in order to evaluate the performance of binary classifiers for SNARE proteins. Results: We constructed a SNARE proteins balanced dataset, D128, and an unbalanced one, DUNI, on which we tested and compared the performance of the new descriptors presented here in combination with the feature sets (GAAC, CTDT, CKSAAP and 188D) already present in the literature. The machine learning algorithms used were Random Forest, k-Nearest Neighbors and AdaBoost and oversampling and subsampling techniques were applied to the unbalanced dataset. The addition of the SNARER descriptors increases the precision for all considered ML algorithms. In particular, on the unbalanced DUNI dataset the accuracy increases in parallel with the increase in sensitivity while on the balanced dataset D128 the accuracy increases compared to the counterpart without the addition of SNARER descriptors, with a strong improvement in specificity. Our best result is the combination of our descriptors SNARER with CKSAAP feature on the dataset D128 with 92.3% of accuracy, 90.1% for sensitivity and 95% for specificity with the RF algorithm. Conclusions: The performed analysis has shown how the introduction of molecular descriptors linked to the chemical-physical and structural characteristics of the proteins can improve the classification performance. Additionally, it was pointed out that performance can change based on using a balanced or unbalanced dataset. The balanced nature of training can significantly improve forecast accuracy.