Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm.

BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. RESULTS: In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. CONCLUSIONS: An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.

Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm.

Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+ and Mg2+ metal ion ligands from the BioLip database as the research objects. Based on the amino acids, the physicochemical properties and predicted structural information, we introduced the disorder value as the feature parameter. In addition, based on the component information, position weight matrix and information entropy, we introduced the propensity factor as prediction parameters. Then, we used the deep neural network algorithm for the prediction. Furtherly, we made an optimization for the hyper-parameters of the deep learning algorithm and obtained improved results than the previous IonSeq method.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors.

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility.

Many proteins realize their special functions by binding with specific metal ion ligands during a cell's life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Ca2+, Mg2+, Mn2+, Na+, and K+) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.

An In Situ Reversible Heterodimeric Nanoswitch Controlled by Metal-Ion-Ligand Coordination Regulates the Mechanosensing and Differentiation of Stem Cells.

In situ and cytocompatible nanoswitching by external stimuli is highly appealing for reversibly regulating cellular adhesion and functions in vivo. Here, a heterodimeric nanoswitch is designed to facilitate in situ switchable and combinatorial presentation of integrin-binding cell-adhesive moieties, such as Mg2+ and Arg-Gly-Asp (RGD) ligand in nanostructures. In situ reversible nanoswitching is controlled by convertible coordination between bioactive Mg2+ and bisphosphonate (BP) ligand. A BP-coated gold-nanoparticle monomer (BP-AuNP) on a substrate is prepared to allow in situ assembly of cell-adhesive Mg2+ -active Mg-BP nanoparticles (NPs) on a BP-AuNP surface via Mg2+ -BP coordination, yielding heterodimeric nanostructures (switching "ON"). Ethylenediaminetetraacetic acid (EDTA)-based Mg2+ chelation allows in situ disassembly of Mg2+ -BP NP, reverting to Mg2+ -free monomer (switching "OFF"). This in situ reversible nanoswitching on and off of cell-adhesive Mg2+ presentation allows reversible cell adhesion and release in vivo, respectively, and spatiotemporally controls cyclic cell adhesion. In situ heterodimeric assembly of dual RGD ligand- and Mg2+ -active RGD-BP-Mg2+ NP (switching "Dual ON") further tunes and promotes focal adhesion, spreading, and differentiation of stem cells. The modular nature of this in situ nanoswitch can accommodate various bioactive nanostructures via metal-ion-ligand coordination to regulate diverse cellular functions in vivo in reversible and compatible manner.