The Novel Application of Geometric Morphometrics with Principal Component Analysis to Existing G Protein-Coupled Receptor (GPCR) Structures

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.

Special Issue “GPCRs: Ligands and beyond 2022”

The human genome encodes more than 800 different G protein-coupled receptors (GPCRs), uncovering their importance in human physiology [...]

Tilianin: A Potential Natural Lead Molecule for New Drug Design and Development for the Treatment of Cardiovascular Disorders

Cardiovascular disorders (CVDs) are the leading risk factor for death worldwide, and research into the processes and treatment regimens has received a lot of attention. Tilianin is a flavonoid glycoside that can be found in a wide range of medicinal plants and is most commonly obtained from Dracocephalum moldavica. Due to its extensive range of biological actions, it has become a well-known molecule in recent years. In particular, numerous studies have shown that tilianin has cardioprotective properties against CVDs. Hence, this review summarises tilianin’s preclinical research in CVDs, as well as its mechanism of action and opportunities in future drug development. The physicochemical and drug-likeness properties, as well as the toxicity profile, were also highlighted. Tilianin can be a natural lead molecule in the therapy of CVDs such as coronary heart disease, angina pectoris, hypertension, and myocardial ischemia, according to scientific evidence. Free radical scavenging, inflammation control, mitochondrial function regulation, and related signalling pathways are all thought to play a role in tilianin’s cardioprotective actions. Finally, we discuss tilianin-derived compounds, as well as the limitations and opportunities of using tilianin as a lead molecule in drug development for CVDs. Overall, the scientific evidence presented in this review supports that tilianin and its derivatives could be used as a lead molecule in CVD drug development initiatives.