Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels

Ion channel trafficking implications in heart failure

Heart failure (HF) is recognized as an epidemic in the contemporary world, impacting around 1%-2% of the adult population and affecting around 6 million Americans. HF remains a major cause of mortality, morbidity, and poor quality of life. Several therapies are used to treat HF and improve the survival of patients; however, despite these substantial improvements in treating HF, the incidence of HF is increasing rapidly, posing a significant burden to human health. The total cost of care for HF is USD 69.8 billion in 2023, warranting a better understanding of the mechanisms involved in HF. Among the most serious manifestations associated with HF is arrhythmia due to the electrophysiological changes within the cardiomyocyte. Among these electrophysiological changes, disruptions in sodium and potassium currents' function and trafficking, as well as calcium handling, all of which impact arrhythmia in HF. The mechanisms responsible for the trafficking, anchoring, organization, and recycling of ion channels at the plasma membrane seem to be significant contributors to ion channels dysfunction in HF. Variants, microtubule alterations, or disturbances of anchoring proteins lead to ion channel trafficking defects and the alteration of the cardiomyocyte's electrophysiology. Understanding the mechanisms of ion channels trafficking could provide new therapeutic approaches for the treatment of HF. This review provides an overview of the recent advances in ion channel trafficking in HF.

Asn57 N-glycosylation promotes the degradation of hemicellulose by β-1,3-1,4-glucanase from Rhizopus homothallicus

N-glycosylation alters the properties of different enzymes in different ways. Rhizopus homothallicus was first described as an environmental isolate from desert soil in Guatemala. A new gene encoding glucanase RhGlu16B was identified in R. homothallicus. It had high specific activity (9673 U/mg) when barley glucan was used as a substrate, and β-glucan is hemicellulose that is abundant in nature. RhGlu16B has only one N-glycosylation site in its Ala55-Gly64 loop. It was found that N-glycosylation increased its T_m value and catalytic efficiency by 5.1 °C and 59%, respectively. Adding N-glycosylation to the same region of GH16 family glucanases TlGlu16A (from Talaromyces leycettanus) increased its thermostability and catalytic efficiency by 6.4 °C and 38%, respectively. In a verification experiment using GH16 family glucanases BisGlu16B (from Bisporus) in which N-glycosylation was removed, N-glycosylation also appeared to promote thermostability and catalytic efficiency. N-glycosylation reduced the overall root mean square deviation of the enzyme structure, creating rigidity and increasing overall thermostability. This study provided a reference for the molecular modification of GH16 family glucanases and guided the utilization of β-glucan in hemicellulose.

Machine learning-based approach for prediction of ion channels and their subclasses

Ion channels are ion-permeable protein pores that are found in all cell lipid membranes. Distinct ion channels play multiple roles in biological processes. Proteomic data is fast accumulating as a result of the fast growth of mass spectrometry and giving us the chance to comprehensively explore ion channel classes along with their subclasses. This paper proposes an eXtreme Gradient Boosting (XGBoost)-based method to estimate the ion channel classes and their subclasses. Here, 12 feature vectors are applied to better characterize protein sequences like amino acid composition, pseudo-amino acid composition, normalized moreau-broto autocorrelation, amphiphilic pseudo-amino acid composition, dipeptide composition, Geary autocorrelation, tripeptide composition, sequence-order-coupling number, composition/transition/distribution, conjoint triad, moran autocorrelation, quasi-sequence-order descriptors. Here, a total of 9920 features are extracted from the protein sequence. The principal component analysis is applied to determine the optimal number of features to optimize the performance. In 10-fold cross-validation the proposed XGBoost based approach with optimal 50 features achieved accuracy of 100%, 98.70%, 98.77%, 97.26%, 87.40%, 97.39%, 98.03%, 96.42%, and F1-Score of 100%, 99%, 99%, 97%, 87%, 97%, 98%, 97%, for prediction of ion channel and nonion channel, voltage-gated and ligand-gated ion channels, subclasses of voltage-gated ion channels (VGICs), subclasses of ligand-gated ion channels (LGICs), subclasses of voltage-gated calcium channels (VGCCs), subclasses of voltage-gated potassium channels (VGKCs), subclasses of voltage-gated sodium channels (VGSCs), and subclasses of voltage-gated chloride channels, respectively. Here the proposed approach also compares with the other approaches such as support vector machine, k-nearest neighbor, Gaussian Naïve Bayes, and random forest and also compares with existing methods such as support vector machine (SVM) with maximum relevance maximum distance with an accuracy of 86.6%, 83.7%, and 85.1%, for ion channels, non-ion channels and overall respectively and SVM with radial basis function kernel-based method with an accuracy of 100%, 97% and 99.9% for ion channels, nonion channels, and overall accuracy, respectively.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

MFPS_CNN: Multi-filter pattern scanning from position-specific scoring matrix with convolutional neural network for efficient prediction of ion transporters

In cellular transportation mechanisms, the movement of ions across the cell membrane and its proper control are important for cells, especially for life processes. Ion transporters/pumps and ion channel proteins work as border guards controlling the incessant traffic of ions across cell membranes. We revisited the study of classification of transporters and ion channels from membrane proteins with a more efficient deep learning approach. Specifically, we applied multi-window scanning filters of convolutional neural networks on almost full-length position-specific scoring matrices for extracting useful information. In this way, we were able to retain important evolutionary information of the proteins. Our experiment results show that a convolutional neural network with a minimum number of convolutional layers can be enough to extract the conserved information of proteins which leads to higher performance. Our best prediction models were obtained after examining different data imbalanced handling techniques, and different protein encoding methods. We also showed that our models were superior to traditional deep learning approaches on the same datasets as well as other machine learning classification algorithms.