Unraveling the impact of the p.R107L mutation on the structure and function of human αB-Crystallin: Implications for cataract formation.

To date, several pathogenic mutations have been identified in the primary structure of human α-Crystallin, frequently involving the substitution of arginine with a different amino acid. These mutations can lead to the incidence of cataracts and myopathy. Recently, an important cataract-associated mutation has been reported in the functional α-Crystallin domain (ACD) of human αB-Crystallin protein, where arginine 107 (R107) is replaced by a leucine. In this study, we investigated the structure, chaperone function, stability, oligomerization, and amyloidogenic properties of the p.R107L human αB-Crystallin using a number of different techniques. Our results suggest that the p.R107L mutation can cause significant changes in the secondary, tertiary, and quaternary structures of αB-Crystallin. This cataractogenic mutation led to the formation of protein oligomers with larger sizes than the wild-type protein and reduced the chemical and thermal stability of the mutant chaperone. Both fluorescence and microscopic assessments indicated that this mutation significantly altered the amyloidogenic properties of human αB-Crystallin. Furthermore, the mutant protein indicated an attenuated in vitro chaperone activity. The molecular dynamics (MD) simulation confirmed the experimental results and indicated that p.R107L mutation could alter the proper conformation of human αB-Crystallin dimers. In summary, our results indicated that the p.R107L mutation could promote the formation of larger oligomers, diminish the stability and chaperone activity of human αB-Crystallin, and these changes, in turn, can play a crucial role in the development of cataract disorder.

CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework.

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).

Probing the interaction between 7-geranyloxycoumarin and bovine serum albumin: Spectroscopic analyzing and molecular docking study.

7-Geranyloxycoumarin (auraptene; AUR), as a potent phytochemical, is the naturally abundant prenyloxycoumarin found in many genera of the Rutaceae family. As the interaction with serum albumins may play a crucial role in identifying their pharmacological properties, we investigated AUR binding profile with bovine serum albumin (BSA) by experimental and computational methods. Binding constant, binding site, mode of binding, and the BSA structural change upon AUR addition, were studied. UV-vis spectroscopy results and fluorescence quenching analysis proposed that AUR can form the ground state complex with BSA. Meantime, thermodynamic parameters (negative ΔH and ΔS values) revealed that hydrogen bonds and van der Waals interactions play major role, as intermolecular forces, in the AUR-BSA complex formation. Synchronous fluorescence spectra and circular dichroism (CD) data showed that the secondary structure of BSA did not change significantly in the presence of AUR. Moreover, molecular docking results showed that AUR binds to the subdomain IIIB of BSA.