Computational Identification and Analysis of Ubiquinone-Binding Proteins

Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will provide insights into the pathways associated with ubiquinones. In this work, we were the first to propose a UBPs predictor (UBPs-Pred). The optimal feature subset selected from three categories of sequence-derived features was fed into the extreme gradient boosting (XGBoost) classifier, and the parameters of XGBoost were tuned by multi-objective particle swarm optimization (MOPSO). The experimental results over the independent validation demonstrated considerable prediction performance with a Matthews correlation coefficient (MCC) of 0.517. After that, we analyzed the UBPs using bioinformatics methods, including the statistics of the binding domain motifs and protein distribution, as well as an enrichment analysis of the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.

[1]  Yanling Li,et al.  High-Throughput Identification of Mammalian Secreted Proteins Using Species-Specific Scheme and Application to Human Proteome , 2018, Molecules.

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  H. Krebs.  The citric acid cycle and the Szent-Györgyi cycle in pigeon breast muscle. , 1940, The Biochemical journal.

[4]  Xiao-hong Cheng,et al.  Effects of coenzyme Q10 intervention on diabetic kidney disease , 2019, Medicine.

[5]  Joanne M. Morrisey,et al.  Characterization of a Plasmodium falciparum Orthologue of the Yeast Ubiquinone-Binding Protein, Coq10p , 2016, PloS one.

[6]  Dmitrij Frishman,et al.  Residue co-evolution helps predict interaction sites in α-helical membrane proteins. , 2019, Journal of structural biology.

[7]  Balachandran Manavalan,et al.  iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree , 2018, Computational and structural biotechnology journal.

[8]  C. Hackenbrock,et al.  Lateral diffusion of ubiquinone during electron transfer in phospholipid- and ubiquinone-enriched mitochondrial membranes. , 1982, The Journal of biological chemistry.

[9]  The UniProt Consortium,et al.  UniProt: a worldwide hub of protein knowledge , 2018, Nucleic Acids Res..

[10]  Huan Liu,et al.  Incremental Feature Selection , 1998, Applied Intelligence.

[11]  David D L Minh,et al.  Identification of the Catalytic Ubiquinone-binding Site of Vibrio cholerae Sodium-dependent NADH Dehydrogenase , 2017, The Journal of Biological Chemistry.

[12]  Junchi Yan,et al.  Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks , 2017, BMC Genomics.

[13]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Jijun Tang,et al.  Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information , 2017, Inf. Sci..

[15]  S. Iwata,et al.  Architecture of Succinate Dehydrogenase and Reactive Oxygen Species Generation , 2003, Science.

[16]  Mehdi Jafaria,et al.  Coenzyme Q 10 in the treatment of heart failure : A systematic review of systematic reviews , 2018 .

[17]  Saeed Jalili,et al.  Protein secondary structure prediction using DWKF based on SVR-NSGAII , 2012, Neurocomputing.

[18]  E. Berry,et al.  3-Nitropropionic Acid Is a Suicide Inhibitor of Mitochondrial Respiration That, upon Oxidation by Complex II, Forms a Covalent Adduct with a Catalytic Base Arginine in the Active Site of the Enzyme* , 2005, Journal of Biological Chemistry.

[19]  Satoshi Omura,et al.  Structural and Computational Analysis of the Quinone-binding Site of Complex II (Succinate-Ubiquinone Oxidoreductase) , 2006, Journal of Biological Chemistry.

[20]  Michio Tsuda,et al.  A mutation in succinate dehydrogenase cytochrome b causes oxidative stress and ageing in nematodes , 1998, Nature.

[21]  A. Jainul Fathima,et al.  Pharmacophore Mapping of Ligand Based Virtual Screening, Molecular Docking and Molecular Dynamic Simulation Studies for Finding Potent NS2B/NS3 Protease Inhibitors as Potential Anti-dengue Drug Compounds , 2018, Current Bioinformatics.

[22]  Xue-wen Chen,et al.  On Position-Specific Scoring Matrix for Protein Function Prediction , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[24]  HuangYing,et al.  CD-HIT Suite , 2010 .

[25]  Quan Zou,et al.  Incorporating Distance-based Top-n-gram and Random Forest to Identify Electron Transport Proteins. , 2019, Journal of proteome research.

[26]  Silvio C. E. Tosatto,et al.  The Pfam protein families database in 2019 , 2018, Nucleic Acids Res..

[27]  Han Zhang,et al.  Gene Expression Value Prediction Based on XGBoost Algorithm , 2019, Front. Genet..

[28]  Ying Gao,et al.  Bioinformatics Applications Note Sequence Analysis Cd-hit Suite: a Web Server for Clustering and Comparing Biological Sequences , 2022 .

[29]  Donglin Zeng,et al.  Reinforcement Learning Trees , 2015, Journal of the American Statistical Association.

[30]  Mehdi Jafari,et al.  Coenzyme Q10 in the treatment of heart failure: A systematic review of systematic reviews , 2018, Indian heart journal.

[31]  Shandar Ahmad,et al.  Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence , 2020, Proteins.

[32]  Zhiqiang Ma,et al.  HEMEsPred: Structure-Based Ligand-Specific Heme Binding Residues Prediction by Using Fast-Adaptive Ensemble Learning Scheme , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[33]  Xingpeng Jiang,et al.  Sequence clustering in bioinformatics: an empirical study. , 2018, Briefings in bioinformatics.

[34]  S. Khan,et al.  Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC. , 2017, Journal of theoretical biology.

[35]  Michael Gribskov,et al.  IRESpy: an XGBoost model for prediction of internal ribosome entry sites , 2019, BMC Bioinformatics.

[36]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[37]  Mikael Bodén,et al.  MEME Suite: tools for motif discovery and searching , 2009, Nucleic Acids Res..

[38]  V. Vetvicka,et al.  Combination Therapy with Glucan and Coenzyme Q10 in Murine Experimental Autoimmune Disease and Cancer , 2018, AntiCancer Research.

[39]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[40]  Manfred Auer,et al.  Structure of fumarate reductase from Wolinella succinogenes at 2.2 Å resolution , 1999, Nature.

[41]  Lingling Zhao,et al.  A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease , 2019, Front. Genet..

[42]  A. Tafazoli,et al.  Coenzyme Q10 in breast cancer care. , 2017, Future oncology.

[43]  Okio Hino,et al.  A mutation in the SDHC gene of complex II increases oxidative stress, resulting in apoptosis and tumorigenesis. , 2005, Cancer research.

[44]  G. Dallner,et al.  Biochemical, physiological and medical aspects of ubiquinone function. , 1995, Biochimica et biophysica acta.

[45]  B. Lemire,et al.  The Quaternary Structure of the Saccharomyces cerevisiae Succinate Dehydrogenase , 2004, Journal of Biological Chemistry.

[46]  Hiroyuki Tsutsui,et al.  Effects of coenzyme Q10 supplementation on diastolic function in patients with heart failure with preserved ejection fraction. , 2019, Drug discoveries & therapeutics.

[47]  Zhiqiang Ma,et al.  Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme , 2017, BMC Bioinformatics.

[48]  F. L. Crane,et al.  Biochemical Functions of Coenzyme Q10 , 2001, Journal of the American College of Nutrition.

[49]  Z. Rao,et al.  Crystal Structure of Mitochondrial Respiratory Membrane Protein Complex II , 2005, Cell.

[50]  C. Chothia,et al.  The geometry of domain combination in proteins. , 2002, Journal of molecular biology.

[51]  Yongxing Xu,et al.  Efficacy of coenzyme Q10 in patients with chronic kidney disease: protocol for a systematic review , 2019, BMJ Open.

[52]  Ying Wang,et al.  Understanding Ubiquinone. , 2016, Trends in cell biology.

[53]  Hui Ding,et al.  A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features , 2019, Front. Bioeng. Biotechnol..

[54]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[55]  Quan Zou,et al.  A Review of DNA-binding Proteins Prediction Methods , 2019, Current Bioinformatics.

[56]  G. Dallner,et al.  Distribution and redox state of ubiquinones in rat and human tissues. , 1992, Archives of biochemistry and biophysics.