iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

Oncotarget

Volumn 7, Issue 32, 2016, Pages 51270-51283

  Qiu, Wang Ren ^a,b   Xiao, Xuan ^a,c   Xu, Zhao Chun ^a   Chou, Kuo Chen ^c,d,e  

^a JINGDEZHEN CERAMIC INSTITUTE   (China)

^b University of Missouri   (United States)

^c GORDON LIFE SCIENCE INSTITUTE   (United States)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^e UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Ensemble classifier; Protein phosphorylation; Pseudo components; Random forests

Indexed keywords

SERINE; THREONINE; TYROSINE; LYSINE; PROTEIN; PROTEIN KINASE;

ACCESS TO INFORMATION; AMINO ACID SEQUENCE; ARTICLE; CLASSIFIER; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; ONLINE SYSTEM; PROTEIN ANALYSIS; PROTEIN PHOSPHORYLATION; PROTEIN STRUCTURE; VALIDATION PROCESS; ALGORITHM; BINDING SITE; BIOLOGY; CHEMISTRY; HUMAN; METABOLISM; PHOSPHORYLATION; PROCEDURES; PROTEIN DOMAIN; PROTEIN PROCESSING; SOFTWARE;

ALGORITHMS; AMINO ACID SEQUENCE; BINDING SITES; COMPUTATIONAL BIOLOGY; HUMANS; LYSINE; PHOSPHORYLATION; PROTEIN INTERACTION DOMAINS AND MOTIFS; PROTEIN KINASES; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEINS; SOFTWARE;

EID: 84978664062     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.9987     Document Type: Article

References (115)

1. Foster MW, Hess DT, Stamler JS. Protein S-nitrosylation in health and disease: a current perspective. Trends Mol Med. 2009; 15:391-404.
2. Uehara T, Nakamura T, Yao D, Shi ZQ, Gu Z, Ma Y, Masliah E, Nomura Y, Lipton SA. S-nitrosylated protein-disulphide isomerase links protein misfolding to neurodegeneration. Nature. 2006; 441:513-517.
3. Kobayashi Y, Absher DM, Gulzar ZG, Young SR, McKenney JK, Peehl DM, Brooks JD, Myers RM, Sherlock G. DNA methylation profiling reveals novel biomarkers and important roles for DNA methyltransferases in prostate cancer. Genome Res. 2011; 21:1017-1027.
4. Cantara WA, Crain PF, Rozenski J, McCloskey JA, Harris KA, Zhang X, Vendeix FA, Fabris D, Agris PF. The RNA Modification Database, RNAMDB: 2011 update. Nucleic Acids Res. 2011; 39:D195-201.
5. Xu Y, Ding J, Wu LY. iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS ONE. 2013; 8:e55844.
6. Xu Y, Shao XJ, Wu LY, Deng NY. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ. 2013; 1:e171.
7. Qiu WR, Xiao X, Lin WZ. iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach. Biomed Res Int (BMRI). 2014; 2014:947416.
8. Xu Y, Wen X, Shao XJ. iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int J Mol Sci. 2014; 15:7594-7610.
9. Xu Y, Wen X, Wen LS, Wu LY. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE. 2014; 9:e105018.
10. Qiu WR, Xiao X, Lin WZ. iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model. J Biomol Struct Dyn. 2015; 33:1731-1742.
11. Jia J, Liu Z, Xiao X, Liu B. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequencecoupling effects into pseudo components and optimizing imbalanced training dataset. Anal Biochem. 2016; 497:48-56.
12. Jia J, Liu Z, Xiao X, Liu B. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol. 2016; 394:223-230.
13. Xu Y. Recent progress in predicting posttranslational modification sites in proteins. Curr Top Med Chem. 2016; 16:591-603.
14. Jia J, Liu Z, Xiao X, Liu B, Chou KC. iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget. 2016; doi:10.18632/ oncotarget.9148.
15. Liu Z, Xiao X, Qiu WR. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem. (also, Data in Brief, 2015, 4: 87-89). 2015; 474:69-77.
16. Chou KC. Impacts of bioinformatics to medicinal chemistry. Med Chem. 2015; 11:218-234.
17. Chen W, Feng P, Ding H. iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition. Anal Biochem. (also, Data in Brief, 2015; 5: 376-378). 2015; 490:26-33.
18. Liu Z, Xiao X, Yu DJ, Jia J, Qiu WR. pRNAm-PC: Predicting N-methyladenosine sites in RNA sequences via physicalchemical properties. Anal Biochem. 2016; 497:60-67.
19. Herren AW, Weber DM, Rigor RR, Margulies KB, Phinney BS, Bers DM. CaMKII Phosphorylation of Na(V)1.5: Novel in Vitro Sites Identified by Mass Spectrometry and Reduced S516 Phosphorylation in Human Heart Failure. J Proteome Res. 2015; 14:2298-2311.
20. Tanaka K, Soeda M, Hashimoto Y, Takenaka S, Komori M. Identification of phosphorylation sites in Hansenula polymorpha Pex14p by mass spectrometry. FEBS Open Bio. 2013; 3:6-10.
21. Kaufmann H, Bailey JE, Fussenegger M. Use of antibodies for detection of phosphorylated proteins separated by twodimensional gel electrophoresis. Proteomics. 2001; 1:194-199.
22. Gao J, Thelen JJ, Dunker AK, Xu D. Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol Cell Proteomics. 2010; 9:2586-2600.
23. Yao Q, Gao J, Bollinger C, Thelen JJ, Xu D. Predicting and analyzing protein phosphorylation sites in plants using musite. Frontiers in plant science. 2012; 3:186.
24. Huang SY, Shi SP, Qiu JD, Liu MC. Using support vector machines to identify protein phosphorylation sites in viruses. J Mol Graphics Modell. 2015; 56:84-90.
25. Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J Theor Biol. 2011; 273:236-247.
26. Liu B, Fang L, Long R, Lan X. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition Bioinformatics. 2016; 32:362-389.
27. Jia J, Liu Z, Xiao X, Liu B. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules. 2016; 21:95.
28. Liu B, Long R. iDHS-EL: Identifying DNase I hypersensi-tivesites by fusing three different modes of pseudo nucleotide composition into an en-semble learning framework. Bioinformatics. 2016; doi:10.1093/ bioinformatics/btw186.
29. Xiao X, Ye HX, Liu Z, Jia JH, Chou KC. iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget. 2016; doi:10.18632/oncotarget.9057.
30. Qiu WR, Sun BQ, Xiao X. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory Molecular Informatics. 2016; doi:10.1002/ minf.201600010.
31. Chou KC. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem. 1993; 268:16938-16948.
32. Liu B, Wang S, Long R. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics. 2016; in press.
33. Chou KC, Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem J. 1980; 187:829-835.
34. Zhou GP, Deng MH. An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J. 1984; 222:169-176.
35. Chou KC. Graphic rules in steady and non-steady enzyme kinetics. J Biol Chem. 1989; 264:12074-12079.
36. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Kezdy FJ, Romero DL, Tarpley WG, Reusser F. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry. 1993; 32:6548-6554.
37. Althaus IW, Gonzales AJ, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem. 1993; 268:14875-14880.
38. Wu ZC, Xiao X. 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J Theor Biol. 2010; 267:29-34.
39. Chou KC, Lin WZ, Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams. Natural Science. 2011; 3:862-865
40. Zhou GP. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol. 2011; 284:142-148.
41. Fawcett JA. An Introduction to ROC Analysis. Pattern Recognition Letters. 2005; 27:861-874.
42. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning: ACM), pp. 2006; 233-240.
43. Chou KC. Using subsite coupling to predict signal peptides. Protein Eng. 2001; 14:75-79.
44. Chou KC. A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Sci. 1995; 4:1365-1383.
45. Jia J, Liu Z, Xiao X. iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol. 2015; 377:47-56.
46. Jia J, Liu Z, Xiao X, Liu B. Identification of proteinprotein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC). J Biomol Struct Dyn. 2015; doi:10.1080/07391102.2015.1095116.
47. Chou KC, Shen HB. Review: Recent progresses in protein subcellular location prediction. Anal Biochem. 2007; 370:1-16.
48. Chou KC. Prediction of signal peptides using scaled window. Peptides. 2001; 22:1973-1979.
49. Chou KC. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins Struct Funct Genet. (Erratum: ibid, 2001, Vol44, 60). 2001; 43:246-255.
50. Chou KC. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2005; 21:10-19.
51. Du P, Wang X, Xu C, Gao Y. PseAAC-Builder: A crossplatform stand-alone program for generating various special Chou's pseudo-amino acid compositions. Anal Biochem. 2012; 425:117-119.
52. Cao DS, Xu QS, Liang YZ. propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics. 2013; 29:960-962.
53. Lin SX, Lapointe J. Theoretical and experimental biology in one-A symposium in honour of Professor Kuo-Chen Chou's 50th anniversary and Professor Richard Giegé's 40th anniversary of their scientific careers. J Biomedical Science and Engineering (JBiSE). 2013; 6:435-442.
54. Zhong WZ, Zhou SF. Molecular science for drug development and biomedicine. Int J Mol Sci. 2014; 15:20072-20078.
55. Chou KC. An unprecedented revolution in medicinal science (doi:10.3390/MOL2NET-1-b040). Proceedings of the MOL2NET (International Conference on Multidisciplinary Sciences) 2015; 1:1-10
56. Zhou GP, Zhong WZ. Perspectives in Medicinal Chemistry. Curr Top Med Chem. 2016; 16:381-382.
57. Khan ZU, Hayat M, Khan MA. Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model. J Theor Biol. 2015; 365:197-203.
58. Dehzangi A, Heffernan R, Sharma A, Lyons J, Paliwal K, Sattar A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC. J Theor Biol. 2015; 364: 284-294.
59. Kumar R, Srivastava A, Kumari B, Kumar M. Prediction of beta-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J Theor Biol. 2015; 365:96-103.
60. Mondal S, Pai PP. Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol. 2014; 356:30-35.
61. Wang X, Zhang W, Zhang Q, Li GZ. MultiP-SChlo: multilabel protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multilabel classifier. Bioinformatics. 2015; 31:2639-2645.
62. Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples. Curr Mol Genet Genomics. MGG. 2016; 291:285-296.
63. Ahmad K, Waris M, Hayat M. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. J Membr Biol. 2016:10.1007/s00232-00015-09868-00238.
64. Du P, Gu S, Jiao Y. PseAAC-General: Fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci. 2014; 15:3495-3506.
65. Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst. 2015; 11:2620-2634.
66. Chen W, Lei TY, Jin DC, Lin H. PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Anal Biochem. 2014; 456:53-60.
67. Chen W, Zhang X, Brooker J, Lin H. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics. 2015; 31:119-120.
68. Liu B, Liu F, Fang L, Wang X. repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics. 2015; 31:1307-1309.
69. Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 2015; 43:W65-W71.
70. Wright PE, Dyson HJ. Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol. 1999; 293:321-331.
71. Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z. Intrinsic disorder and protein function. Biochemistry. 2002; 41:6573-6582.
72. Yoon MK, Venkatachalam V, Huang A, Choi BS, Stultz CM, Chou JJ. Residual structure within the disordered C-terminal segment of p21(Waf1/Cip1/Sdi1) and its implications for molecular recognition. Protein Sci. 2009; 18:337-347.
73. Huang T, He ZS, Cui WR, Cai YD, Shi XH. A Sequencebased Approach for Predicting Protein Disordered Regions. Protein and Peptide Letters. 2013; 20:243-248.
74. Iakoucheva LM, Radivojac P, Brown CJ, O'Connor TR, Sikes JG, Obradovic Z, Dunker AK. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 2004; 32:1037-1049.
75. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z. Length-dependent prediction of protein intrinsic disorder. BMC bioinformatics. 2006; 7:208.
76. Henikoff S, Henikoff JG. Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences of the United States of America. 1992; 89:10915-10919.
77. Shi SP, Qiu JD, Sun XY, Suo SB, Huang SY, Liang RP. PMeS: prediction of methylation sites based on enhanced feature encoding scheme. PloS one. 2012; 7:e38772.
78. Kandaswamy KK, Moller S, Suganthan PN, Sridharan S, Pugalenthi G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol. 2011; 270:56-62.
79. Lin WZ, Fang JA, Xiao X. iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE. 2011; 6:e24756.
80. Pugalenthi G, Kandaswamy KK, Kolatkar P. RSARF: Prediction of Residue Solvent Accessibility from Protein Sequence Using Random Forest Method. Protein Pept Lett. 2012; 19:50-56.
81. Breiman L. Random forests. Machine learning. 2001; 45:5-32.
82. Chou KC, Shen HB. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Comm (BBRC). 2007; 357: 633-640.
83. Shen HB. Signal-3L: a 3-layer approach for predicting signal peptide. Biochem Biophys Res Comm (BBRC). 2007; 363:297-303.
84. Shen HB. Using ensemble classifier to identify membrane protein types. Amino Acids. 2007; 32:483-488.
85. Chou KC, Shen HB. MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem Biophys Res Comm (BBRC). 2007; 360:339-345.
86. Chou KC, Shen HB. Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun (BBRC). 2006; 347:150-157.
87. Shen HB. Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins. Protein Eng Des Sel. 2007; 20:39-46.
88. Chou KC, Shen HB. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res. 2007; 6:1728-1734.
89. Shen HB, Chou KC. Ensemble classifier for protein fold pattern recognition. Bioinformatics. 2006; 22:1717-1722.
90. Shen HB. EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochem Biophys Res Comm (BBRC). 2007; 364:53-59.
91. Chen J, Liu H, Yang J. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids. 2007; 33: 423-428.
92. Chou KC. Prediction of protein signal sequences and their cleavage sites. Proteins: Struct, Funct, Genet. 2001; 42:136-139.
93. Chen W, Feng PM, Lin H, Chou KC. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013; 41:e68.
94. Chen W, Feng PM, Deng EZ. iTIS-PseTNC: a sequencebased predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem. 2014; 462:76-83.
95. Chen W, Feng PM, Lin H. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Research International (BMRI). 2014; 2014:623149.
96. Ding H, Deng EZ, Yuan LF, Liu L. iCTX-Type: A sequencebased predictor for identifying the types of conotoxins in targeting ion channels. BioMed Research International (BMRI). 2014; 2014:286419.
97. Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 2014; 42:12961-12972.
98. Liu B, Fang L, Liu F, Wang X. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS ONE. 2015; 10:e0121501.
99. Xiao X, Min JL, Lin WZ, Liu Z. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach. J Biomol Struct Dyn. 2015; 33:2221-2233.
100. Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget. 2016; 7:16895-16909.
101. Chen W, Feng P, Ding H. Using deformation energy to analyze nucleosome positioning in genomes. Genomics. 2016; 107:69-75.
102. Liu B, Fang L, Liu F, Wang X. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distancepair composition approach. J Biomol Struct Dyn. 2016; 34:223-235.
103. Chou KC, Wu ZC, Xiao X. iLoc-Hum: Using accumulationlabel scale to predict subcellular locations of human proteins with both single and multiple sites. Mol BioSyst. 2012; 8:629-641.
104. Lin WZ, Fang JA, Xiao X. iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins. Mol BioSyst. 2013; 9:634-644.
105. Xiao X, Wu ZC. iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol. 2011; 284:42-51.
106. Xiao X, Wang P, Lin WZ. iAMP-2L: A two-level multilabel classifier for identifying antimicrobial peptides and their functional types. Anal Biochem. 2013; 436:168-177.
107. Chou KC. Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Mol BioSyst. 2013; 9:1092-1100.
108. Chou KC, Zhang CT. Review: Prediction of protein structural classes. Crit Rev Biochem Mol Biol. 1995; 30:275-349.
109. Zhou GP. An intriguing controversy over protein structural class prediction. J Protein Chem. 1998; 17:729-738.
110. Zhou GP, Doctor K. Subcellular location prediction of apoptosis proteins. Proteins: Struct, Funct, Genet. 2003; 50:44-48.
111. Chou KC, Cai YD. Prediction of membrane protein types by incorporating amphipathic effects. J Chem Inf Model. 2005; 45:407-413.
112. Shen HB. Virus-PLoc: A fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers. 2007; 85:233-240.
113. Nanni L, Brahnam S, Lumini A. Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition. J Theor Biol. 2014; 360:109-116.
114. Ahmad S, Kabir M, Hayat M. Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC. Computer methods and programs in biomedicine. 2015; 122:165-174.
115. Liu B, Xu J, Fan S, Xu R, Jiyun Zhou J, Wang X. PseDNA-Pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance transformation. Molecular Informatics. 2015; 34:8-17.