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.

Download Full-text

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

Frontiers in Genetics ◽

10.3389/fgene.2020.00214 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xiuzhen Hu ◽

Zhenxing Feng ◽

Xiaojin Zhang ◽

Liu Liu ◽

Shan Wang

Keyword(s):

Ligand Binding ◽

Metal Ion ◽

Solvent Accessibility ◽

Relative Solvent Accessibility ◽

Binding Residues

Download Full-text

Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information

Advances in Bioinformatics ◽

10.1155/2015/843030 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

M. Xavier Suresh ◽

M. Michael Gromiha ◽

Makiko Suwa

Keyword(s):

Membrane Protein ◽

Ligand Binding ◽

Binding Sites ◽

Transmembrane Domain ◽

Three Dimensional ◽

Sequence Information ◽

Bayes Classifier ◽

Binding Residue ◽

Ligand Binding Sites ◽

Binding Residues

Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.

Download Full-text

Computational Biology Tools for Identifying Specific Ligand Binding Residues for Novel Agrochemical and Drug Design

Current Protein and Peptide Science ◽

10.2174/1389203716666150505234923 ◽

2015 ◽

Vol 16 (8) ◽

pp. 701-717 ◽

Cited By ~ 4

Author(s):

Izabella Pena Neshich ◽

Leticia Nishimura ◽

Fabio de Moraes ◽

Jose Salim ◽

Fabian Villalta-Romero ◽

...

Keyword(s):

Drug Design ◽

Computational Biology ◽

Ligand Binding ◽

Binding Residues ◽

Specific Ligand

Download Full-text

The Trans-Membrane Cytochrome b561 Proteins: Structural Information and Biological Function

Current Protein and Peptide Science ◽

10.2174/1389203715666140828100351 ◽

2014 ◽

Vol 15 (8) ◽

pp. 745-760 ◽

Cited By ~ 4

Author(s):

Alajos Berczi ◽

Laszlo Zimanyi

Keyword(s):

Biological Function ◽

Structural Information ◽

Cytochrome B561

Download Full-text

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

Nucleic Acids Research ◽

10.1093/nar/gkab044 ◽

2021 ◽

Author(s):

Ying Xia ◽

Chun-Qiu Xia ◽

Xiaoyong Pan ◽

Hong-Bin Shen

Keyword(s):

Neural Networks ◽

Nucleic Acid ◽

Biological Activities ◽

New Drugs ◽

Superior Performance ◽

Nucleic Acid Binding ◽

Hierarchical Graph ◽

Binding Residue ◽

Binding Residues ◽

Graph Neural Networks

Abstract Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.

Download Full-text

Labeling of integrin alpha v beta 3 with 58Co(III). Evidence of metal ion coordination sphere involvement in ligand binding

Journal of Biological Chemistry ◽

10.1016/s0021-9258(18)98975-1 ◽

1991 ◽

Vol 266 (18) ◽

pp. 11429-11432 ◽

Cited By ~ 2

Author(s):

J.W. Smith ◽

D.A. Cheresh

Keyword(s):

Ligand Binding ◽

Coordination Sphere ◽

Metal Ion ◽

Alpha V Beta 3

Download Full-text

Bridging the gap between structure and kinetics of human SGLT1

AJP Cell Physiology ◽

10.1152/ajpcell.00397.2011 ◽

2012 ◽

Vol 302 (9) ◽

pp. C1293-C1305 ◽

Cited By ~ 37

Author(s):

Monica Sala-Rabanal ◽

Bruce A. Hirayama ◽

Donald D. F. Loo ◽

Vincent Chaptal ◽

Jeff Abramson ◽

...

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Sugar Transport ◽

Gene Families ◽

Structural Studies ◽

Sugar Binding ◽

Substituted Cysteine Accessibility ◽

Biochemical Assays ◽

Functional Consequences ◽

Binding Residues

The Na+-glucose cotransporter hSGLT1 is a member of a class of membrane proteins that harness Na+ electrochemical gradients to drive uphill solute transport. Although hSGLT1 belongs to one gene family (SLC5), recent structural studies of bacterial Na+ cotransporters have shown that Na+ transporters in different gene families have the same structural fold. We have constructed homology models of hSGLT1 in two conformations, the inward-facing occluded (based on vSGLT) and the outward open conformations (based on Mhp1), mutated in turn each of the conserved gates and ligand binding residues, expressed the SGLT1 mutants in Xenopus oocytes, and determined the functional consequences using biophysical and biochemical assays. The results establish that mutating the ligand binding residues produces profound changes in the ligand affinity (the half-saturation concentration, K0.5); e.g., mutating sugar binding residues increases the glucose K0.5 by up to three orders of magnitude. Mutation of the external gate residues increases the Na+ to sugar transport stoichiometry, demonstrating that these residues are critical for efficient cotransport. The changes in phlorizin inhibition constant ( Ki) are proportional to the changes in sugar K0.5, except in the case of F101C, where phlorizin Ki increases by orders of magnitude without a change in glucose K0.5. We conclude that glucose and phlorizin occupy the same binding site and that F101 is involved in binding to the phloretin group of the inhibitor. Substituted-cysteine accessibility methods show that the cysteine residues at the position of the gates and sugar binding site are largely accessible only to external hydrophilic methanethiosulfonate reagents in the presence of external Na+, demonstrating that the external sugar (and phlorizin) binding vestibule is opened by the presence of external Na+ and closes after the binding of sugar and phlorizin. Overall, the present results provide a bridge between kinetics and structural studies of cotransporters.

Download Full-text

Oxidative Modification of Fibrinogen Affects Its Binding Activity to Glycoprotein (GP) IIb/IIIa

Clinical and Applied Thrombosis/Hemostasis ◽

10.1177/1076029609339749 ◽

2009 ◽

Vol 16 (1) ◽

pp. 51-59 ◽

Cited By ~ 10

Author(s):

Sermin Tetik ◽

Kurtulus Kaya ◽

M. Demir ◽

Emel Eksioglu-Demiralp ◽

Turay Yardimci

Keyword(s):

Metal Ion ◽

Degradation Products ◽

Binding Capacity ◽

Protein Structures ◽

Human Platelets ◽

Binding Activity ◽

Oxidative Modification ◽

Oxidized Fibrinogen ◽

Protein Functions

Aim: Proteins are sensitive biomarkers of human diease condition associated with oxidative stress. Alteration of protein structures by oxidants may result in partial or complete loss of protein functions. We have investigated the effect of structural modifications induced by metal ion catalyzed oxidation of fibrinogen on its binding capacity to glycoprotein IIb/IIIa (GpIIb/IIIa) and human platelets. Methods: We identified and quantified of binding capacity of native and oxidized fibrinogen to its receptor in vitro by flow cytometer. Dityrosine formation on oxidized fibrinogen were detected spectrophotometrically. Elevated degradation products of fibrinogen after oxidation were revealed in the HPLC analysis. The native and oxidized fibrinogen were analyzed on mass spectrum upon digestion with tyripsin. Results: Oxidatively modified fibrinogen showed less binding activity than native fibrinogen to GpIIb/IIIa coated micro beads and human platelets whereas slightly higher binding capaticity to ADP induced stimulated platelets. Formation of dityrosines in the amino acid side chains of fibrinogen were observed upon oxidation. Decreased binding capacity of oxidized fibrinogen correlated with intensities of dityrosine formation. Oxidized fibrinogen had more ion-mass intensities at higher than native fibrinogen. Clinical implications: Important point is decreased of binding capacity of the oxidized fibrinogen to own receptor. The decreased rate of binding, leading to effect in the diseases of clot formation may acount for the association between oxidation of fibrinogen and the incidence of effect in human diseases.

Download Full-text

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

Current Medicinal Chemistry ◽

10.2174/0929867328666210910125802 ◽

2021 ◽

Vol 28 ◽

Author(s):

Yu-He Yang ◽

Jia-Shu Wang ◽

Shi-Shi Yuan ◽

Meng-Lu Liu ◽

Wei Su ◽

...

Keyword(s):

Machine Learning ◽

Protein Function ◽

Vital Role ◽

Atp Binding ◽

Learning Methods ◽

Machine Learning Methods ◽

Protein Ligand Interactions ◽

Protein Functions ◽

Ligand Interactions ◽

Binding Residues

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Download Full-text