SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

Abstract

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.

Identifying SNARE Proteins Using an Alignment-Free Method Based on Multiscan Convolutional Neural Network and PSSM Profiles

“…Since the publication of the benchmark dataset, 22 the identification of SNARE proteins has gained much interest from researchers. In addition to the deep learning framework by Le and Nguyen, 22 current method proposed by SNAREs-SAP 25 architected on machine learning algorithms also achieved high performances on SNAREs data. In this section, we focused on comparing the predictive efficiency made by our model with aforementioned ones since we used the same dataset.…”

“…SNAREs-SAP, which was developed by Zhang et al, 25 assembles from SVM-RFE-CBR and PSSM profiles. Similarly, the architecture of CKSAAP-Manhattan 23 was constructed on a kNN classifier, and its feature extraction was based on the CKSAAP method.…”

“…For SNARE proteins, Le and Nguyen, as pioneers in this field, have ensembled a model and web server termed SNARE-CNN based on convolutional neural network (CNN), with a newly proposed benchmark dataset of SNARE sequences. To date, various studies have been conducted on the aforementioned dataset to improve the predictive performance using different methods such as Manhattan distance and k-nearest neighbors (kNN), hybrid model, or support vector machine–recursive feature elimination–correlation bias reduction (SVM-RFE-CBR) …”

“…21 For SNARE proteins, Le and Nguyen, 22 predictive performance using different methods such as Manhattan distance and k-nearest neighbors (kNN), 23 hybrid model, 24 or support vector machine−recursive feature elimination−correlation bias reduction (SVM-RFE-CBR). 25 However, all current methods approaching the prediction problem face two independent issues. First, most previous studies used conventional machine learning (ML) algorithms, which could not retrieve the hidden information from sequence information compared to deep learning (i.e., CNN).…”

“…To date, various studies have been conducted on the aforementioned dataset to improve the predictive performance using different methods such as Manhattan distance and k-nearest neighbors (kNN), 23 hybrid model, 24 or support vector machine–recursive feature elimination–correlation bias reduction (SVM-RFE-CBR). 25 …”