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.

[1]  Xiaoyong Cao,et al.  Identification of metal ion binding sites based on amino acid sequences , 2017, PloS one.

[2]  E. Brǎiloiu,et al.  Mechanisms of modulation of brain microvascular endothelial cells function by thrombin , 2017, Brain Research.

[3]  Xikun Wang,et al.  Predicting protein-ligand binding residues with deep convolutional neural networks , 2019, BMC Bioinformatics.

[4]  Chin-Teng Lin,et al.  Protein Metal Binding Residue Prediction Based on Neural Networks , 2004, ICONIP.

[5]  Alexander E. Kel,et al.  MATCHTM: a tool for searching transcription factor binding sites in DNA sequences , 2003, Nucleic Acids Res..

[6]  Sung-Il Yang,et al.  Codon and amino-acid distribution in DNA , 2005 .

[7]  Z Jiang,et al.  Identification of Ca2+-binding residues of a protein from its primary sequence , 2016 .

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Zhenxing Feng,et al.  Recognizing Ion Ligand–Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle , 2020, Frontiers in Bioengineering and Biotechnology.

[10]  Xiuzhen Hu,et al.  Recognizing five molecular ligand‐binding sites with similar chemical structure , 2020, J. Comput. Chem..

[11]  Zhenxing Feng,et al.  Recognizing ion ligand binding sites by SMO algorithm , 2019, BMC Molecular and Cell Biology.

[12]  W. Taylor,et al.  The classification of amino acid conservation. , 1986, Journal of theoretical biology.

[13]  Alexios Koutsoukas,et al.  Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data , 2017, Journal of Cheminformatics.

[14]  Yang Zhang,et al.  Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals , 2016, Bioinform..

[15]  Josef Pánek,et al.  A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins , 2005, Proteins.

[16]  Chin-Sheng Yu,et al.  Prediction of Metal Ion–Binding Sites in Proteins Using the Fragment Transformation Method , 2012, PloS one.

[17]  Lianyi Han,et al.  Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach , 2006, BMC Bioinformatics.

[18]  Ram Samudrala,et al.  A protein sequence meta-functional signature for calcium binding residue prediction , 2010, Pattern Recognit. Lett..

[19]  Leonardo Nogueira Matos,et al.  Deep Neural Networks for Acoustic Modeling in the Presence of Noise , 2018, IEEE Latin America Transactions.

[20]  Keunho Choi,et al.  Development of a Natural Language Processing based Deep Learning Model for Automated HS Code Classification of the Imported Goods , 2021 .

[21]  E. Schiffrin,et al.  Signal transduction mechanisms mediating the physiological and pathophysiological actions of angiotensin II in vascular smooth muscle cells. , 2000, Pharmacological reviews.

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Juan Wang,et al.  The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method , 2018, BMC Bioinformatics.

[24]  Y. Zhang,et al.  Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals , 2016, Bioinform..

[25]  Sitao Wu,et al.  ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction , 2008, PloS one.

[26]  Franck Dernoncourt,et al.  Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives , 2018, PloS one.

[27]  J. S. Sodhi,et al.  Predicting metal-binding site residues in low-resolution structural models. , 2004, Journal of molecular biology.

[28]  T G Dewey,et al.  The Shannon information entropy of protein sequences. , 1996, Biophysical journal.

[29]  Raffaella Folli,et al.  Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG , 2020, Sensors.

[30]  Yang Zhang,et al.  BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions , 2012, Nucleic Acids Res..