Special Protein Molecules Computational Identification

Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.

[1]  Long Zhang,et al.  Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences , 2017, International journal of molecular sciences.

[2]  Lei Chen,et al.  Application of the Shortest Path Algorithm for the Discovery of Breast Cancer-Related Genes , 2016 .

[3]  Pufeng Du,et al.  PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo-Amino Acid Composition for Large-Scale Protein Datasets , 2014, International journal of molecular sciences.

[4]  Xiangxiang Zeng,et al.  nDNA-prot: identification of DNA-binding proteins based on unbalanced classification , 2014, BMC Bioinformatics.

[5]  Bo Chen,et al.  Biochemical and Computational Insights on a Novel Acid-Resistant and Thermal-Stable Glucose 1-Dehydrogenase , 2017, International journal of molecular sciences.

[6]  B. Snel,et al.  Predicting disease genes using protein–protein interactions , 2006, Journal of Medical Genetics.

[7]  Bo Li,et al.  Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations , 2017, International journal of molecular sciences.

[8]  Kamila Korzekwa,et al.  Relationship of Triamine-Biocide Tolerance of Salmonella enterica Serovar Senftenberg to Antimicrobial Susceptibility, Serum Resistance and Outer Membrane Proteins , 2017, International journal of molecular sciences.

[9]  Jijun Tang,et al.  Identification of drug-target interactions via multiple information integration , 2017, Inf. Sci..

[10]  Jianxin Wang,et al.  CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks , 2017, International journal of molecular sciences.

[11]  Yi Pan,et al.  ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  Hui Ding,et al.  Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. , 2011, Journal of theoretical biology.

[13]  Dong Xu,et al.  Transmembrane Protein Alignment and Fold Recognition Based on Predicted Topology , 2013, PloS one.

[14]  Shunfang Wang,et al.  Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA , 2015, International journal of molecular sciences.

[15]  Jian Song,et al.  Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information , 2017, Molecules.

[16]  B. Liu,et al.  DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation , 2015, Scientific Reports.

[17]  B. Liu,et al.  PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation , 2017, International journal of molecular sciences.

[18]  Xing Chen,et al.  PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences , 2017, International journal of molecular sciences.

[19]  Jijun Tang,et al.  PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only , 2017, IEEE Transactions on NanoBioscience.

[20]  Jian Gao,et al.  Identification of Direct Activator of Adenosine Monophosphate-Activated Protein Kinase (AMPK) by Structure-Based Virtual Screening and Molecular Docking Approach , 2017, International journal of molecular sciences.

[21]  Xuan Liu,et al.  Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning , 2016, IEEE Transactions on NanoBioscience.

[22]  Xinying Xu,et al.  An Ameliorated Prediction of Drug–Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features , 2017, International journal of molecular sciences.

[23]  Hua Tang,et al.  IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types , 2017, International journal of molecular sciences.

[24]  Jinyan Li,et al.  Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information , 2010, BMC Bioinformatics.

[25]  Jingpu Zhang,et al.  Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  Bo Liu,et al.  A Topology Structure Based Outer Membrane Proteins Segment Alignment Method , 2013 .

[27]  Junjie Chen,et al.  Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences , 2015, Nucleic Acids Res..

[28]  B. Liu,et al.  PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation , 2015, Molecular informatics.

[29]  Jinyan Li,et al.  Protein binding hot spots prediction from sequence only by a new ensemble learning method , 2017, Amino Acids.

[30]  Wei Chen,et al.  Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines , 2017, Scientific Reports.

[31]  Wei Ding,et al.  3D-QSAR and Molecular Docking Studies on the TcPMCA1-Mediated Detoxification of Scopoletin and Coumarin Derivatives , 2017, International journal of molecular sciences.

[32]  Wei Zhao,et al.  UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences , 2017, International journal of molecular sciences.

[33]  B. Liu,et al.  iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition , 2014, PloS one.

[34]  Shunfang Wang,et al.  Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection , 2017, International journal of molecular sciences.

[35]  Xiangrong Liu,et al.  An Empirical Study of Features Fusion Techniques for Protein-Protein Interaction Prediction , 2016 .

[36]  Tingting Fu,et al.  Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics , 2017, Nucleic Acids Res..

[37]  Pu-Feng Du,et al.  Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties. , 2016, Journal of theoretical biology.

[38]  Yuhang Zhang,et al.  Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network , 2017, International journal of molecular sciences.

[39]  Jinyan Li,et al.  Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences , 2013, Proteins.

[40]  Yi Pan,et al.  Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[41]  Jinyan Li,et al.  A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[42]  Bo Li,et al.  NOREVA: normalization and evaluation of MS-based metabolomics data , 2017, Nucleic Acids Res..

[43]  Yi Pan,et al.  DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization , 2018, Bioinform..

[44]  Jijun Tang,et al.  Predicting protein-protein interactions via multivariate mutual information of protein sequences , 2016, BMC Bioinformatics.

[45]  Guohua Wang,et al.  Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods , 2017, Molecules.

[46]  Feng Zhu,et al.  Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate , 2018, International journal of molecular sciences.

[47]  Lei Chen,et al.  An integrated method for the identification of novel genes related to oral cancer , 2017, PloS one.

[48]  Xin Wang,et al.  PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. , 2012, Analytical biochemistry.

[49]  Jijun Tang,et al.  Identification of Protein–Protein Interactions via a Novel Matrix-Based Sequence Representation Model with Amino Acid Contact Information , 2016, International journal of molecular sciences.

[50]  Zixiang Wang,et al.  Computational identification of binding energy hot spots in protein–RNA complexes using an ensemble approach , 2018, Bioinform..

[51]  Bin Liu,et al.  BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches , 2019, Briefings Bioinform..

[52]  Jian Zhang,et al.  Identification of novel proliferative diabetic retinopathy related genes on protein-protein interaction network , 2016, Neurocomputing.

[53]  Q. Zou,et al.  Network-based method for mining novel HPV infection related genes using random walk with restart algorithm. , 2017, Biochimica et biophysica acta. Molecular basis of disease.

[54]  Lin Wu,et al.  CytoCtrlAnalyser: a Cytoscape app for biomolecular network controllability analysis , 2018, Bioinform..

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

[56]  Ricardo L. Mancera,et al.  Understanding Insulin Endocrinology in Decapod Crustacea: Molecular Modelling Characterization of an Insulin-Binding Protein and Insulin-Like Peptides in the Eastern Spiny Lobster, Sagmariasus verreauxi , 2017, International journal of molecular sciences.

[57]  Yi Pan,et al.  CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks , 2015, Biosyst..

[58]  Bing Wang,et al.  Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System , 2017, International journal of molecular sciences.

[59]  Dong Xu,et al.  OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method , 2017, J. Comput. Biol..

[60]  Anton A Nizhnikov,et al.  Predicting Amyloidogenic Proteins in the Proteomes of Plants , 2017, International journal of molecular sciences.

[61]  Shuang Li,et al.  SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity , 2016, PloS one.

[62]  Wei Chen,et al.  Identifying the Subfamilies of Voltage-Gated Potassium Channels Using Feature Selection Technique , 2014, International journal of molecular sciences.

[63]  Wei Chen,et al.  Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine , 2012, Comput. Biol. Medicine.

[64]  Shunfang Wang,et al.  A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition , 2015, Entropy.

[65]  Lei Deng,et al.  A computational interactome and functional annotation for the human proteome , 2016, eLife.