Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Author(s): Jing Tang, Yunxia Wang, Yi Li, Yang Zhang, Runyuan Zhang, Ziyu Xiao, Yongchao Luo, Xueying Guo, Lin Tao, Yan Lou, Weiwei Xue, Feng Zhu*

Journal Name: Current Pharmaceutical Design

Volume 25 , Issue 13 , 2019

Become EABM
Become Reviewer

Abstract:

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Keywords: Nanoproteomics, nanomaterials, nanomedicine, protein quantification, mass spectrometry, biomedicine science.

[1]
Nath Roy D, Goswami R, Pal A. Nanomaterial and toxicity: What can proteomics tell us about the nanotoxicology? Xenobiotica 2017; 47(7): 632-43.
[http://dx.doi.org/10.1080/00498254.2016.1205762] [PMID: 27414072]
[2]
Satyavani K, Gurudeeban S, Ramanathan T, Balasubramanian T. Biomedical potential of silver nanoparticles synthesized from calli cells of Citrullus colocynthis (L.) Schrad. J Nanobiotechnology 2011; 9: 43.
[http://dx.doi.org/10.1186/1477-3155-9-43] [PMID: 21943321]
[3]
Fathil MF, Md Arshad MK, Ruslinda AR, et al. Progression in sensing cardiac troponin biomarker charge transductions on semiconducting nanomaterials. Anal Chim Acta 2016; 935: 30-43.
[http://dx.doi.org/10.1016/j.aca.2016.06.012] [PMID: 27543013]
[4]
He W, Wamer W, Xia Q, Yin JJ, Fu PP. Enzyme-like activity of nanomaterials. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 2014; 32(2): 186-211.
[http://dx.doi.org/10.1080/10590501.2014.907462] [PMID: 24875443]
[5]
Zhang XQ, Yuan JN, Selvaraj G, Ji GF, Chen XR, Wei DQ. Towards the low-sensitive and high-energetic co-crystal explosive CL-20/TNT: From intermolecular interactions to structures and properties. Phys Chem Chem Phys 2018; 20(25): 17253-61.
[http://dx.doi.org/10.1039/C8CP01841C] [PMID: 29901061]
[6]
Yang ST, Liu Y, Wang YW, Cao A. Biosafety and bioapplication of nanomaterials by designing protein-nanoparticle interactions. Small 2013; 9(9-10): 1635-53.
[http://dx.doi.org/10.1002/smll.201201492] [PMID: 23341247]
[7]
Chang XL, Yang ST, Xing G. Molecular toxicity of nanomaterials. J Biomed Nanotechnol 2014; 10(10): 2828-51.
[http://dx.doi.org/10.1166/jbn.2014.1936] [PMID: 25992420]
[8]
Hua S, Wu SY. Editorial: Advances and Challenges in Nanomedicine. Front Pharmacol 2018; 9: 1397.
[http://dx.doi.org/10.3389/fphar.2018.01397] [PMID: 30555328]
[9]
Tabassum N, Verma V, Kumar M, Kumar A, Singh B. Nanomedicine in cancer stem cell therapy: From fringe to forefront. Cell Tissue Res 2018; 374(3): 427-38.
[http://dx.doi.org/10.1007/s00441-018-2928-5] [PMID: 30302547]
[10]
Satyavani K, Gurudeeban S, Ramanathan T, Balasubramanian T. Toxicity study of silver nanoparticles synthesized from Suaeda monoica on Hep-2 cell line. Avicenna J Med Biotechnol 2012; 4(1): 35-9.
[PMID: 23407847]
[11]
Kawasaki ES, Player A. Nanotechnology, nanomedicine, and the development of new, effective therapies for cancer. Nanomedicine 2005; 1(2): 101-9.
[http://dx.doi.org/10.1016/j.nano.2005.03.002] [PMID: 17292064]
[12]
Arvizo R, Bhattacharya R, Mukherjee P. Gold nanoparticles: Opportunities and challenges in nanomedicine. Expert Opin Drug Deliv 2010; 7(6): 753-63.
[http://dx.doi.org/10.1517/17425241003777010] [PMID: 20408736]
[13]
Brown SD, Nativo P, Smith JA, et al. Gold nanoparticles for the improved anticancer drug delivery of the active component of oxaliplatin. J Am Chem Soc 2010; 132(13): 4678-84.
[http://dx.doi.org/10.1021/ja908117a] [PMID: 20225865]
[14]
Aminabad NS, Farshbaf M, Akbarzadeh A. Recent advances of gold nanoparticles in biomedical applications: State of the art. Cell Biochem Biophys 2019; 77(2): 123-37.
[http://dx.doi.org/10.1007/s12013-018-0863-4] [PMID: 30570696]
[15]
Krasnoslobodtsev AV, Torres MP, Kaur S, et al. Nano-immunoassay with improved performance for detection of cancer biomarkers. Nanomedicine (Lond) 2015; 11(1): 167-73.
[http://dx.doi.org/10.1016/j.nano.2014.08.012] [PMID: 25200613]
[16]
Proetto MT, Callmann CE, Cliff J, et al. Tumor retention of enzyme-responsive Pt(II) drug-loaded nanoparticles imaged by nanoscale secondary ion mass spectrometry and fluorescence microscopy. ACS Cent Sci 2018; 4(11): 1477-84.
[http://dx.doi.org/10.1021/acscentsci.8b00444] [PMID: 30555899]
[17]
Nicolini C, Bragazzi N, Pechkova E. Nanoproteomics enabling personalized nanomedicine. Adv Drug Deliv Rev 2012; 64(13): 1522-31.
[http://dx.doi.org/10.1016/j.addr.2012.06.015] [PMID: 22820526]
[18]
Agrawal GK, Timperio AM, Zolla L, Bansal V, Shukla R, Rakwal R. Biomarker discovery and applications for foods and beverages: Proteomics to nanoproteomics. J Proteomics 2013; 93: 74-92.
[http://dx.doi.org/10.1016/j.jprot.2013.04.014] [PMID: 23619387]
[19]
Riehemann K, Schneider SW, Luger TA, Godin B, Ferrari M, Fuchs H. Nanomedicine-challenge and perspectives. Angew Chem Int Ed Engl 2009; 48(5): 872-97.
[http://dx.doi.org/10.1002/anie.200802585] [PMID: 19142939]
[20]
Zhang X, Ning Z, Mayne J, et al. In vitro metabolic labeling of intestinal microbiota for quantitative metaproteomics. Anal Chem 2016; 88(12): 6120-5.
[http://dx.doi.org/10.1021/acs.analchem.6b01412] [PMID: 27248155]
[21]
Khan A, Ali A, Junaid M, et al. Identification of novel drug targets for diamond-blackfan anemia based on RPS19 gene mutation using protein-protein interaction network. BMC Syst Biol 2018; 12(Suppl. 4): 39.
[http://dx.doi.org/10.1186/s12918-018-0563-0] [PMID: 29745857]
[22]
Liu H, Webster TJ. Nanomedicine for implants: A review of studies and necessary experimental tools. Biomaterials 2007; 28(2): 354-69.
[http://dx.doi.org/10.1016/j.biomaterials.2006.08.049] [PMID: 21898921]
[23]
Kaliamurthi S, Selvaraj G, Junaid M, Khan A, Gu K, Wei DQ. Cancer immunoinformatics: A promising era in the development of peptide vaccines for human papillomavirus-induced cervical cancer. Curr Pharm Des 2018; 24(32): 3791-817.
[http://dx.doi.org/10.2174/1381612824666181106094133] [PMID: 30398106]
[24]
Ray S, Reddy PJ, Choudhary S, Raghu D, Srivastava S. Emerging nanoproteomics approaches for disease biomarker detection: A current perspective. J Proteomics 2011; 74(12): 2660-81.
[http://dx.doi.org/10.1016/j.jprot.2011.04.027] [PMID: 21596164]
[25]
Fredolini C, Meani F, Luchini A, et al. Investigation of the ovarian and prostate cancer peptidome for candidate early detection markers using a novel nanoparticle biomarker capture technology. AAPS J 2010; 12(4): 504-18.
[http://dx.doi.org/10.1208/s12248-010-9211-3] [PMID: 20549403]
[26]
Wang L, Jia E. Ovarian cancer targeted hyaluronic acid-based nanoparticle system for paclitaxel delivery to overcome drug resistance. Drug Deliv 2016; 23(5): 1810-7.
[http://dx.doi.org/10.3109/10717544.2015.1101792] [PMID: 26530693]
[27]
Liu W, Yang X, Wang N, et al. Multiple immunosuppressive effects of CpG-c41 on intracellular TLR-mediated inflammation. Mediators Inflamm 2017; 20176541729
[http://dx.doi.org/10.1155/2017/6541729] [PMID: 28539706]
[28]
Mirzajani F, Askari H, Hamzelou S, et al. Proteomics study of silver nanoparticles toxicity on Oryza sativa L. Ecotoxicol Environ Saf 2014; 108: 335-9.
[http://dx.doi.org/10.1016/j.ecoenv.2014.07.013] [PMID: 25124680]
[29]
Mirzajani F, Askari H, Hamzelou S, et al. Proteomics study of silver nanoparticles toxicity on Bacillus thuringiensis. Ecotoxicol Environ Saf 2014; 100: 122-30.
[http://dx.doi.org/10.1016/j.ecoenv.2013.10.009] [PMID: 24290895]
[30]
Djurišić AB, Leung YH, Ng AM, et al. Toxicity of metal oxide nanoparticles: Mechanisms, characterization, and avoiding experimental artefacts. Small 2015; 11(1): 26-44.
[http://dx.doi.org/10.1002/smll.201303947] [PMID: 25303765]
[31]
García-Santamarina S, Boronat S, Domènech A, Ayté J, Molina H, Hidalgo E. Monitoring in vivo reversible cysteine oxidation in proteins using ICAT and mass spectrometry. Nat Protoc 2014; 9(5): 1131-45.
[http://dx.doi.org/10.1038/nprot.2014.065] [PMID: 24743420]
[32]
Yasmeen F, Raja NI, Razzaq A, Komatsu S. Gel-free/label-free proteomic analysis of wheat shoot in stress tolerant varieties under iron nanoparticles exposure. Biochim Biophys Acta 2016; 1864(11): 1586-98.
[http://dx.doi.org/10.1016/j.bbapap.2016.08.009] [PMID: 27530299]
[33]
Verberkmoes NC, Russell AL, Shah M, et al. Shotgun metaproteomics of the human distal gut microbiota. ISME J 2009; 3(2): 179-89.
[http://dx.doi.org/10.1038/ismej.2008.108] [PMID: 18971961]
[34]
Li Z, Adams RM, Chourey K, Hurst GB, Hettich RL, Pan C. Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J Proteome Res 2012; 11(3): 1582-90.
[http://dx.doi.org/10.1021/pr200748h] [PMID: 22188275]
[35]
Edelmann MJ, Shack LA, Naske CD, Walters KB, Nanduri B. SILAC-based quantitative proteomic analysis of human lung cell response to copper oxide nanoparticles. PLoS One 2014; 9(12)E114390
[http://dx.doi.org/10.1371/journal.pone.0114390] [PMID: 25470785]
[36]
Juang YM, Lai BH, Chien HJ, Ho M, Cheng TJ, Lai CC. Changes in protein expression in rat bronchoalveolar lavage fluid after exposure to zinc oxide nanoparticles: An iTRAQ proteomic approach. Rapid Commun Mass Spectrom 2014; 28(8): 974-80.
[http://dx.doi.org/10.1002/rcm.6866] [PMID: 24623703]
[37]
Kumar V, Kleffmann T, Hampton MB, Cannell MB, Winterbourn CC. Redox proteomics of thiol proteins in mouse heart during ischemia/reperfusion using ICAT reagents and mass spectrometry. Free Radic Biol Med 2013; 58: 109-17.
[http://dx.doi.org/10.1016/j.freeradbiomed.2013.01.021] [PMID: 23376233]
[38]
Schmidt F, Dahlmann B, Janek K, et al. Comprehensive quantitative proteome analysis of 20S proteasome subtypes from rat liver by isotope coded affinity tag and 2-D gel-based approaches. Proteomics 2006; 6(16): 4622-32.
[http://dx.doi.org/10.1002/pmic.200500920] [PMID: 16858736]
[39]
Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999; 17(10): 994-9.
[http://dx.doi.org/10.1038/13690] [PMID: 10504701]
[40]
Wasdo SC. Differential binding of serum proteins to nanoparticles. Int J Nanotechnol 2008; 5: 92-115.
[http://dx.doi.org/10.1504/IJNT.2008.016550]
[41]
Thompson A, Schäfer J, Kuhn K, et al. Tandem mass tags: A novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 2003; 75(8): 1895-904.
[http://dx.doi.org/10.1021/ac0262560] [PMID: 12713048]
[42]
Hung CW, Tholey A. Tandem mass tag protein labeling for top-down identification and quantification. Anal Chem 2012; 84(1): 161-70.
[http://dx.doi.org/10.1021/ac202243r] [PMID: 22103715]
[43]
Hahne H, Neubert P, Kuhn K, et al. Carbonyl-reactive tandem mass tags for the proteome-wide quantification of N-linked glycans. Anal Chem 2012; 84(8): 3716-24.
[http://dx.doi.org/10.1021/ac300197c] [PMID: 22455665]
[44]
Liu JM, Sweredoski MJ, Hess S. Improved 6-Plex tandem mass tags quantification throughput using a linear ion trap-high-energy collision induced dissociation MS(3) scan. Anal Chem 2016; 88(15): 7471-5.
[http://dx.doi.org/10.1021/acs.analchem.6b01067] [PMID: 27377715]
[45]
Jia W, Andaya A, Leary JA. Novel mass spectrometric method for phosphorylation quantification using cerium oxide nanoparticles and tandem mass tags. Anal Chem 2012; 84(5): 2466-73.
[http://dx.doi.org/10.1021/ac203248s] [PMID: 22304650]
[46]
Adav SS, Qian J, Ang YL, et al. iTRAQ quantitative clinical proteomics revealed role of Na(+)K(+)-ATPase and its correlation with deamidation in vascular dementia. J Proteome Res 2014; 13(11): 4635-46.
[http://dx.doi.org/10.1021/pr500754j] [PMID: 25152327]
[47]
Zhang P, Li C, Zhang P, Jin C, Pan D, Bao Y. iTRAQ-based proteomics reveals novel members involved in pathogen challenge in sea cucumber Apostichopus japonicus. PLoS One 2014; 9(6)E100492
[http://dx.doi.org/10.1371/journal.pone.0100492] [PMID: 24949634]
[48]
Ikeda D, Ageta H, Tsuchida K, Yamada H. iTRAQ-based proteomics reveals novel biomarkers of osteoarthritis. Biomarkers 2013; 18(7): 565-72.
[http://dx.doi.org/10.3109/1354750X.2013.810667] [PMID: 23937207]
[49]
An D, Wei X, Li H, et al. Identification of PCSK9 as a novel serum biomarker for the prenatal diagnosis of neural tube defects using iTRAQ quantitative proteomics. Sci Rep 2015; 5: 17559.
[http://dx.doi.org/10.1038/srep17559] [PMID: 26691006]
[50]
Zieske LR. A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J Exp Bot 2006; 57(7): 1501-8.
[http://dx.doi.org/10.1093/jxb/erj168] [PMID: 16574745]
[51]
Pan CH, Chuang KJ, Chen JK, et al. Characterization of pulmonary protein profiles in response to zinc oxide nanoparticles in mice: A 24-hour and 28-day follow-up study. Int J Nanomedicine 2015; 10: 4705-16.
[PMID: 26251593]
[52]
Shadforth IP, Dunkley TP, Lilley KS, Bessant C. i-Tracker: For quantitative proteomics using iTRAQ. BMC Genomics 2005; 6: 145.
[http://dx.doi.org/10.1186/1471-2164-6-145] [PMID: 16242023]
[53]
Ong SE, Blagoev B, Kratchmarova I, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002; 1(5): 376-86.
[http://dx.doi.org/10.1074/mcp.M200025-MCP200] [PMID: 12118079]
[54]
Hoedt E, Zhang G, Neubert TA. Stable isotope labeling by amino acids in cell culture (SILAC) for quantitative proteomics. Adv Exp Med Biol 2014; 806: 93-106.
[http://dx.doi.org/10.1007/978-3-319-06068-2_5] [PMID: 24952180]
[55]
Ahrends R, Pieper S, Kühn A, et al. A metal-coded affinity tag approach to quantitative proteomics. Mol Cell Proteomics 2007; 6(11): 1907-16.
[http://dx.doi.org/10.1074/mcp.M700152-MCP200] [PMID: 17627934]
[56]
Wang P, Fu T, Zhang X, et al. Differentiating physicochemical properties between NDRIs and sNRIs clinically important for the treatment of ADHD. Biochim Biophys Acta, Gen Subj 2017; 1861(11 Pt A): 2766-77.
[http://dx.doi.org/10.1016/j.bbagen.2017.07.022] [PMID: 28757337]
[57]
Bergmann U, Ahrends R, Neumann B, Scheler C, Linscheid MW. Application of metal-coded affinity tags (MeCAT): Absolute protein quantification with top-down and bottom-up workflows by metal-coded tagging. Anal Chem 2012; 84(12): 5268-75.
[http://dx.doi.org/10.1021/ac203460b] [PMID: 22659083]
[58]
El-Khatib AH, He Y, Esteban-Fernández D, Linscheid MW. Application of higher energy collisional dissociation (HCD) to the fragmentation of new DOTA-based labels and N-termini DOTA-labeled peptides. J Mass Spectrom 2017; 52(8): 543-9.
[http://dx.doi.org/10.1002/jms.3954] [PMID: 28577300]
[59]
Messana I, Cabras T, Iavarone F, Vincenzoni F, Urbani A, Castagnola M. Unraveling the different proteomic platforms. J Sep Sci 2013; 36(1): 128-39.
[http://dx.doi.org/10.1002/jssc.201200830] [PMID: 23212829]
[60]
Prudova A, Gocheva V, Auf dem Keller U, et al. TAILS N-terminomics and proteomics show protein degradation dominates over proteolytic processing by cathepsins in pancreatic tumors. Cell Rep 2016; 16(6): 1762-73.
[http://dx.doi.org/10.1016/j.celrep.2016.06.086] [PMID: 27477282]
[61]
Kleifeld O, Doucet A, Prudova A, et al. Identifying and quantifying proteolytic events and the natural N terminome by terminal amine isotopic labeling of substrates. Nat Protoc 2011; 6(10): 1578-611.
[http://dx.doi.org/10.1038/nprot.2011.382] [PMID: 21959240]
[62]
auf dem Keller U, Overall CM. CLIPPER: An add-on to the Trans-Proteomic Pipeline for the automated analysis of TAILS N-terminomics data. Biol Chem 2012; 393(12): 1477-83.
[PMID: 23667905]
[63]
Kleifeld O, Doucet A, auf dem Keller U, et al. Isotopic labeling of terminal amines in complex samples identifies protein N-termini and protease cleavage products. Nat Biotechnol 2010; 28(3): 281-8.
[http://dx.doi.org/10.1038/nbt.1611] [PMID: 20208520]
[64]
Leclercq A, Nonell A, Todolí Torró JL, et al. Introduction of organic/hydro-organic matrices in inductively coupled plasma optical emission spectrometry and mass spectrometry: A tutorial review. Part II. Practical considerations. Anal Chim Acta 2015; 885: 57-91.
[http://dx.doi.org/10.1016/j.aca.2015.04.039] [PMID: 26231892]
[65]
Milton MJT, Wielgosz RI. Uncertainty in SI-traceable measurements of amount of substance by isotope dilution mass spectrometry. Metrologia 2000; 37: 199.
[http://dx.doi.org/10.1088/0026-1394/37/3/3]
[66]
Sötebier CA, Weidner SM, Jakubowski N, Panne U, Bettmer J. Separation and quantification of silver nanoparticles and silver ions using reversed phase high performance liquid chromatography coupled to inductively coupled plasma mass spectrometry in combination with isotope dilution analysis. J Chromatogr A 2016; 1468: 102-8.
[http://dx.doi.org/10.1016/j.chroma.2016.09.028] [PMID: 27663727]
[67]
Jager PL, Vaalburg W, Pruim J, de Vries EG, Langen KJ, Piers DA. Radiolabeled amino acids: Basic aspects and clinical applications in oncology. J Nucl Med 2001; 42(3): 432-45.
[PMID: 11337520]
[68]
Miller PW, Long NJ, Vilar R, Gee AD. Synthesis of 11C, 18F, 15O, and 13N radiolabels for positron emission tomography. Angew Chem Int Ed Engl 2008; 47(47): 8998-9033.
[http://dx.doi.org/10.1002/anie.200800222] [PMID: 18988199]
[69]
Schirrmacher E, Wängler B, Cypryk M, et al. Synthesis of p-(di-tert-butyl[(18)F]fluorosilyl)benzaldehyde ([(18)F]SiFA-A) with high specific activity by isotopic exchange: A convenient labeling synthon for the (18)F-labeling of N-amino-oxy derivatized peptides. Bioconjug Chem 2007; 18(6): 2085-9.
[http://dx.doi.org/10.1021/bc700195y] [PMID: 18030993]
[70]
Peracchia MT, Fattal E, Desmaële D, et al. Stealth PEGylated polycyanoacrylate nanoparticles for intravenous administration and splenic targeting. J Control Release 1999; 60(1): 121-8.
[http://dx.doi.org/10.1016/S0168-3659(99)00063-2] [PMID: 10370176]
[71]
Cagney G, Emili A. De novo peptide sequencing and quantitative profiling of complex protein mixtures using mass-coded abundance tagging. Nat Biotechnol 2002; 20(2): 163-70.
[http://dx.doi.org/10.1038/nbt0202-163] [PMID: 11821862]
[72]
Tao WA, Aebersold R. Advances in quantitative proteomics via stable isotope tagging and mass spectrometry. Curr Opin Biotechnol 2003; 14(1): 110-8.
[http://dx.doi.org/10.1016/S0958-1669(02)00018-6] [PMID: 12566010]
[73]
Wang W, Zhou H, Lin H, et al. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 2003; 75(18): 4818-26.
[http://dx.doi.org/10.1021/ac026468x] [PMID: 14674459]
[74]
Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res 2006; 5(9): 2339-47.
[http://dx.doi.org/10.1021/pr060161n] [PMID: 16944946]
[75]
Tang J, Zhang Y, Fu J, et al. Computational advances in the label-free quantification of cancer proteomics data. Curr Pharm Des 2018; 24(32): 3842-58.
[http://dx.doi.org/10.2174/1381612824666181102125638] [PMID: 30387388]
[76]
Tang J, Fu J, Wang Y, et al. ANPELA: Analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform 2019.
[http://dx.doi.org/10.1093/bib/bby127] [PMID: 30649171]
[77]
Neilson KA, Ali NA, Muralidharan S, et al. Less label, more free: Approaches in label-free quantitative mass spectrometry. Proteomics 2011; 11(4): 535-53.
[http://dx.doi.org/10.1002/pmic.201000553] [PMID: 21243637]
[78]
Podwojski K, Eisenacher M, Kohl M, et al. Peek a peak: A glance at statistics for quantitative label-free proteomics. Expert Rev Proteomics 2010; 7(2): 249-61.
[http://dx.doi.org/10.1586/epr.09.107] [PMID: 20377391]
[79]
Blackburn K, Cheng FY, Williamson JD, Goshe MB. Data-independent liquid chromatography/mass spectrometry (LC/MS(E)) detection and quantification of the secreted Apium graveolens pathogen defense protein mannitol dehydrogenase. Rapid Commun Mass Spectrom 2010; 24(7): 1009-16.
[http://dx.doi.org/10.1002/rcm.4476] [PMID: 20213632]
[80]
Li B, Tang J, Yang Q, et al. NOREVA: Normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res 2017; 45(W1)W162-70
[http://dx.doi.org/10.1093/nar/gkx449] [PMID: 28525573]
[81]
Liu H, Sadygov RG, Yates JR III. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 2004; 76(14): 4193-201.
[http://dx.doi.org/10.1021/ac0498563] [PMID: 15253663]
[82]
Gioria S, Urbán P, Hajduch M, et al. Proteomics study of silver nanoparticles on Caco-2 cells. Toxicol In Vitro 2018; 50: 347-72.
[http://dx.doi.org/10.1016/j.tiv.2018.03.015] [PMID: 29626626]
[83]
Xu LJ, Zong C, Zheng XS, Hu P, Feng JM, Ren B. Label-free detection of native proteins by surface-enhanced Raman spectroscopy using iodide-modified nanoparticles. Anal Chem 2014; 86(4): 2238-45.
[http://dx.doi.org/10.1021/ac403974n] [PMID: 24460183]
[84]
Mustafa G, Sakata K, Komatsu S. Proteomic analysis of flooded soybean root exposed to aluminum oxide nanoparticles. J Proteomics 2015; 128: 280-97.
[http://dx.doi.org/10.1016/j.jprot.2015.08.010] [PMID: 26306862]
[85]
Vogt C, Pernemalm M, Kohonen P, et al. Proteomics analysis reveals distinct corona composition on magnetic nanoparticles with different surface coatings: Implications for interactions with primary human macrophages. PLoS One 2015; 10(10)E0129008
[http://dx.doi.org/10.1371/journal.pone.0129008] [PMID: 26444829]
[86]
Chawade A, Alexandersson E, Levander F. Normalyzer: A tool for rapid evaluation of normalization methods for omics data sets. J Proteome Res 2014; 13(6): 3114-20.
[http://dx.doi.org/10.1021/pr401264n] [PMID: 24766612]
[87]
Karpievitch YV, Dabney AR, Smith RD. Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinformatics 2012; 13(Suppl. 16): S5.
[http://dx.doi.org/10.1186/1471-2105-13-S16-S5] [PMID: 23176322]
[88]
Listgarten J, Emili A. Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 2005; 4(4): 419-34.
[http://dx.doi.org/10.1074/mcp.R500005-MCP200] [PMID: 15741312]
[89]
Xue W, Wang P, Tu G, et al. Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder. Phys Chem Chem Phys 2018; 20(9): 6606-16.
[http://dx.doi.org/10.1039/C7CP07869B] [PMID: 29451287]
[90]
Webb-Robertson BJ, Matzke MM, Jacobs JM, Pounds JG, Waters KM. A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors. Proteomics 2011; 11(24): 4736-41.
[http://dx.doi.org/10.1002/pmic.201100078] [PMID: 22038874]
[91]
De Livera AM, Sysi-Aho M, Jacob L, et al. Statistical methods for handling unwanted variation in metabolomics data. Anal Chem 2015; 87(7): 3606-15.
[http://dx.doi.org/10.1021/ac502439y] [PMID: 25692814]
[92]
Callister SJ, Barry RC, Adkins JN, et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res 2006; 5(2): 277-86.
[http://dx.doi.org/10.1021/pr050300l] [PMID: 16457593]
[93]
Ting L, Cowley MJ, Hoon SL, Guilhaus M, Raftery MJ, Cavicchioli R. Normalization and statistical analysis of quantitative proteomics data generated by metabolic labeling. Mol Cell Proteomics 2009; 8(10): 2227-42.
[http://dx.doi.org/10.1074/mcp.M800462-MCP200] [PMID: 19605365]
[94]
Selvaraj G, Kaliamurthi S, Kaushik AC, et al. Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms. J Biomed Inform 2018; 86: 120-34.
[http://dx.doi.org/10.1016/j.jbi.2018.09.004] [PMID: 30195659]
[95]
Selvaraj G, Kaliamurthi S, Lin S, Gu K, Wei DQ. Prognostic impact of tissue inhibitor of metalloproteinase-1 in non-small cell lung cancer: Systematic review and meta-analysis. Curr Med Chem 2018.
[http://dx.doi.org/10.2174/0929867325666180904114455] [PMID: 30182835]
[96]
Yang Q, Wang Y, Zhang S, et al. Biomarker discovery for immunotherapy of pituitary adenomas: Enhanced robustness and prediction ability by modern computational tools. Int J Mol Sci 2019; 20(1): 20.
[http://dx.doi.org/10.3390/ijms20010151] [PMID: 30609812]
[97]
Jha SK, Yoon TH, Pan Z. Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials. Comput Biol Med 2018; 99: 161-72.
[http://dx.doi.org/10.1016/j.compbiomed.2018.06.012] [PMID: 29933127]
[98]
Marie-Desvergne C, Dubosson M, Mossuz VC. Evaluation of a new method for the collection and measurement of 8-isoprostane in exhaled breath for future application in nanoparticle exposure biomonitoring. J Breath Res 2018; 12(3)031001
[http://dx.doi.org/10.1088/1752-7163/aabdf2] [PMID: 29651988]
[99]
Patel T, Telesca D, George S, Nel AE. Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling. Ann Appl Stat 2012; 6(4): 1707-29.
[http://dx.doi.org/10.1214/12-AOAS563] [PMID: 25191531]
[100]
Štefanić PP, Cvjetko P, Biba R, et al. Physiological, ultrastructural and proteomic responses of tobacco seedlings exposed to silver nanoparticles and silver nitrate. Chemosphere 2018; 209: 640-53.
[http://dx.doi.org/10.1016/j.chemosphere.2018.06.128] [PMID: 29958162]
[101]
Selvaraj G, Kaliamurthi S, Cakmak ZE, Cakmak T. In silico validation of microalgal metabolites against Diabetes mellitus. Diabetes Mel 2017; 20: 301-7.
[http://dx.doi.org/10.14341/DM8212]
[102]
Reig CS, Lopez AD, Ramos MH. Nanomaterials: A Map for Their Selection in Food Packaging Applications. Packag Technol Sci 2015; 27: 839-66.
[http://dx.doi.org/10.1002/pts.2076]
[103]
Farhadi Ghalati P, Keshavarzian E, Abouali O, Faramarzi A, Tu J, Shakibafard A. Numerical analysis of micro- and nano-particle deposition in a realistic human upper airway. Comput Biol Med 2012; 42(1): 39-49.
[http://dx.doi.org/10.1016/j.compbiomed.2011.10.005] [PMID: 22061046]
[104]
Luan F, Kleandrova VV, González-Díaz H, et al. Computer-aided nanotoxicology: Assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale 2014; 6(18): 10623-30.
[http://dx.doi.org/10.1039/C4NR01285B] [PMID: 25083742]
[105]
Liu R, Zhang HY, Ji ZX, et al. Development of structure-activity relationship for metal oxide nanoparticles. Nanoscale 2013; 5(12): 5644-53.
[http://dx.doi.org/10.1039/c3nr01533e] [PMID: 23689214]
[106]
Vizcaíno JA, Csordas A, del-Toro N, et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res 2016; 44(D1): D447-56.
[http://dx.doi.org/10.1093/nar/gkv1145] [PMID: 26527722]
[107]
Schmidt T, Samaras P, Frejno M, et al. ProteomicsDB. Nucleic Acids Res 2018; 46(D1): D1271-81.
[http://dx.doi.org/10.1093/nar/gkx1029] [PMID: 29106664]
[108]
Deutsch EW, Lam H, Aebersold R. PeptideAtlas: A resource for target selection for emerging targeted proteomics workflows. EMBO Rep 2008; 9(5): 429-34.
[http://dx.doi.org/10.1038/embor.2008.56] [PMID: 18451766]
[109]
Craig R, Cortens JP, Beavis RC. Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 2004; 3(6): 1234-42.
[http://dx.doi.org/10.1021/pr049882h] [PMID: 15595733]
[110]
Whiteaker JR, Halusa GN, Hoofnagle AN, et al. CPTAC Assay Portal: A repository of targeted proteomic assays. Nat Methods 2014; 11(7): 703-4.
[http://dx.doi.org/10.1038/nmeth.3002] [PMID: 24972168]
[111]
Bhowmick P, Mohammed Y, Borchers CH. MRMAssayDB: An integrated resource for validated targeted proteomics assays. Bioinformatics 2018; 34(20): 3566-71.
[http://dx.doi.org/10.1093/bioinformatics/bty385] [PMID: 29762640]
[112]
Nanjappa V, Thomas JK, Marimuthu A, et al. Plasma Proteome Database as a resource for proteomics research: 2014 update. Nucleic Acids Res 2014; 42(Database issue): D959-65.
[http://dx.doi.org/10.1093/nar/gkt1251] [PMID: 24304897]
[113]
Farrah T, Deutsch EW, Kreisberg R, et al. PASSEL: The PeptideAtlas SRMexperiment library. Proteomics 2012; 12(8): 1170-5.
[http://dx.doi.org/10.1002/pmic.201100515] [PMID: 22318887]
[114]
Mohammed Y, Bhowmick P, Smith DS, et al. PeptideTracker: A knowledge base for collecting and storing information on protein concentrations in biological tissues. Proteomics 2017; 17(7): 17.
[http://dx.doi.org/10.1002/pmic.201600210] [PMID: 27683069]
[115]
Kusebauch U, Campbell DS, Deutsch EW, et al. Human srmatlas: A resource of targeted assays to quantify the complete human proteome. Cell 2016; 166(3): 766-78.
[http://dx.doi.org/10.1016/j.cell.2016.06.041] [PMID: 27453469]
[116]
Whiteaker JR, Halusa GN, Hoofnagle AN, et al. Using the CPTAC Assay Portal to Identify and Implement Highly Characterized Targeted Proteomics Assays. Methods Mol Biol 2016; 1410: 223-36.
[http://dx.doi.org/10.1007/978-1-4939-3524-6_13] [PMID: 26867747]
[117]
Zhu F, Han B, Kumar P, et al. Update of TTD: Therapeutic Target Database. Nucleic Acids Res 2010; 38(Database issue): D787-91.
[http://dx.doi.org/10.1093/nar/gkp1014] [PMID: 19933260]
[118]
Zhang CC, Rogalski JC, Evans DM, Klockenbusch C, Beavis RC, Kast J. In silico protein interaction analysis using the global proteome machine database. J Proteome Res 2011; 10(2): 656-68.
[http://dx.doi.org/10.1021/pr1008652] [PMID: 21067242]
[119]
Jia J, Zhu F, Ma X, et al. Mechanisms of drug combinations: Interaction and network perspectives. Nat Rev Drug Discov 2009; 8(2): 111-28.
[http://dx.doi.org/10.1038/nrd2683] [PMID: 19180105]
[120]
Rao HB, Zhu F, Yang GB, Li ZR, Chen YZ. Update of PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 2011; 39(Web Server issue): W385-90.
[http://dx.doi.org/10.1093/nar/gkr284] [PMID: 21609959]
[121]
Li YH, Yu CY, Li XX, et al. Therapeutic target database update 2018: Enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 2018; 46(D1): D1121-7.
[PMID: 29140520]
[122]
Zhu F, Qin C, Tao L, et al. Clustered patterns of species origins of nature-derived drugs and clues for future bioprospecting. Proc Natl Acad Sci USA 2011; 108(31): 12943-8.
[http://dx.doi.org/10.1073/pnas.1107336108] [PMID: 21768386]
[123]
Zheng G, Xue W, Yang F, et al. Revealing vilazodone’s binding mechanism underlying its partial agonism to the 5-HT1A receptor in the treatment of major depressive disorder. Phys Chem Chem Phys 2017; 19(42): 28885-96.
[http://dx.doi.org/10.1039/C7CP05688E] [PMID: 29057413]
[124]
MacLean B, Tomazela DM, Shulman N, et al. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010; 26(7): 966-8.
[http://dx.doi.org/10.1093/bioinformatics/btq054] [PMID: 20147306]
[125]
Zhu F, Shi Z, Qin C, et al. Therapeutic target database update 2012: A resource for facilitating target-oriented drug discovery. Nucleic Acids Res 2012; 40(Database issue): D1128-36.
[http://dx.doi.org/10.1093/nar/gkr797] [PMID: 21948793]
[126]
Desiere F, Deutsch EW, King NL, et al. The PeptideAtlas project. Nucleic Acids Res 2006; 34(Database issue): D655-8.
[http://dx.doi.org/10.1093/nar/gkj040] [PMID: 16381952]
[127]
Zhu F, Ma XH, Qin C, et al. Drug discovery prospect from untapped species: Indications from approved natural product drugs. PLoS One 2012; 7(7)E39782
[http://dx.doi.org/10.1371/journal.pone.0039782] [PMID: 22808057]
[128]
Zhu F, Han LY, Chen X, et al. Homology-free prediction of functional class of proteins and peptides by support vector machines. Curr Protein Pept Sci 2008; 9(1): 70-95.
[http://dx.doi.org/10.2174/138920308783565697] [PMID: 18336324]
[129]
Tao L, Zhu F, Qin C, et al. Nature’s contribution to today’s pharmacopeia. Nat Biotechnol 2014; 32(10): 979-80.
[http://dx.doi.org/10.1038/nbt.3034] [PMID: 25299914]
[130]
Yang F, Zheng G, Fu T, et al. Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H. Phys Chem Chem Phys 2018; 20(37): 23873-84.
[http://dx.doi.org/10.1039/C8CP01843J] [PMID: 29947629]
[131]
Reisinger F, del-Toro N, Ternent T, Hermjakob H, Vizcaíno JA. Introducing the PRIDE Archive RESTful web services. Nucleic Acids Res 2015; 43(W1)W599-604
[http://dx.doi.org/10.1093/nar/gkv382] [PMID: 25904633]
[132]
Fu J, Tang J, Wang Y, et al. Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification. Front Pharmacol 2018; 9: 681.
[http://dx.doi.org/10.3389/fphar.2018.00681] [PMID: 29997509]
[133]
Tyanova S, Temu T, Sinitcyn P, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016; 13(9): 731-40.
[http://dx.doi.org/10.1038/nmeth.3901] [PMID: 27348712]
[134]
Lee DY, Saha R, Yusufi FN, Park W, Karimi IA. Web-based applications for building, managing and analysing kinetic models of biological systems. Brief Bioinform 2009; 10(1): 65-74.
[http://dx.doi.org/10.1093/bib/bbn039] [PMID: 18805901]
[135]
Rosenberger G, Ludwig C, Röst HL, Aebersold R, Malmström L. aLFQ: An R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinformatics 2014; 30(17): 2511-3.
[http://dx.doi.org/10.1093/bioinformatics/btu200] [PMID: 24753486]
[136]
Øverbye A, Skotland T, Koehler CJ, et al. Identification of prostate cancer biomarkers in urinary exosomes. Oncotarget 2015; 6(30): 30357-76.
[http://dx.doi.org/10.18632/oncotarget.4851] [PMID: 26196085]
[137]
Gluck F, Hoogland C, Antinori P, et al. EasyProt-an easy-to-use graphical platform for proteomics data analysis. J Proteomics 2013; 79: 146-60.
[http://dx.doi.org/10.1016/j.jprot.2012.12.012] [PMID: 23277275]
[138]
Wang P, Yang F, Yang H, et al. Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods. Biomed Mater Eng 2015; 26(Suppl. 1): S2233-9.
[http://dx.doi.org/10.3233/BME-151529] [PMID: 26406003]
[139]
Zhu F, Zheng CJ, Han LY, et al. Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting. Curr Mol Pharmacol 2008; 1(3): 213-32.
[http://dx.doi.org/10.2174/1874467210801030213] [PMID: 20021435]
[140]
Fu T, Zheng G, Tu G, et al. Exploring the binding mechanism of metabotropic glutamate receptor 5 negative allosteric modulators in clinical trials by molecular dynamics simulations. ACS Chem Neurosci 2018; 9(6): 1492-502.
[http://dx.doi.org/10.1021/acschemneuro.8b00059] [PMID: 29522307]
[141]
Wijetunge CD, Saeed I, Boughton BA, et al. EXIMS: An improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data. Bioinformatics 2015; 31(19): 3198-206.
[http://dx.doi.org/10.1093/bioinformatics/btv356] [PMID: 26063840]
[142]
Tao L, Zhu F, Xu F, Chen Z, Jiang YY, Chen YZ. Co-targeting cancer drug escape pathways confers clinical advantage for multi-target anticancer drugs. Pharmacol Res 2015; 102: 123-31.
[http://dx.doi.org/10.1016/j.phrs.2015.09.019] [PMID: 26438971]
[143]
Choi M, Chang CY, Clough T, et al. MSstats: An R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 2014; 30(17): 2524-6.
[http://dx.doi.org/10.1093/bioinformatics/btu305] [PMID: 24794931]
[144]
Surinova S, Choi M, Tao S, et al. Prediction of colorectal cancer diagnosis based on circulating plasma proteins. EMBO Mol Med 2015; 7(9): 1166-78.
[http://dx.doi.org/10.15252/emmm.201404873] [PMID: 26253081]
[145]
Li YH, Xu JY, Tao L, et al. SVM-Prot 2016: A web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS One 2016; 11(8)E0155290
[http://dx.doi.org/10.1371/journal.pone.0155290] [PMID: 27525735]
[146]
Kuzniar A, Kanaar R. PIQMIe: A web server for semi-quantitative proteomics data management and analysis. Nucleic Acids Res 2014; 42(Web Server issue): W100-6.
[http://dx.doi.org/10.1093/nar/gku478] [PMID: 24861615]
[147]
Taverner T, Karpievitch YV, Polpitiya AD, et al. DanteR: An extensible R-based tool for quantitative analysis of -omics data. Bioinformatics 2012; 28(18): 2404-6.
[http://dx.doi.org/10.1093/bioinformatics/bts449] [PMID: 22815360]
[148]
Xu Z, Wu C, Xie F, et al. Comprehensive quantitative analysis of ovarian and breast cancer tumor peptidomes. J Proteome Res 2015; 14(1): 422-33.
[http://dx.doi.org/10.1021/pr500840w] [PMID: 25350482]
[149]
Bohnenberger H, Ströbel P, Mohr S, et al. Quantitative mass spectrometric profiling of cancer-cell proteomes derived from liquid and solid tumors. J Vis Exp 2015; (96): E52435
[http://dx.doi.org/10.3791/52435] [PMID: 25867170]
[150]
Kuzmanov U, Guo H, Buchsbaum D, et al. Global phosphoproteomic profiling reveals perturbed signaling in a mouse model of dilated cardiomyopathy. Proc Natl Acad Sci USA 2016; 113(44): 12592-7.
[http://dx.doi.org/10.1073/pnas.1606444113] [PMID: 27742792]
[151]
Saraei S, Suomi T, Kauko O, Elo LL, Stegle O. Phosphonormalizer: An R package for normalization of MS-based label-free phosphoproteomics. Bioinformatics 2018; 34(4): 693-4.
[http://dx.doi.org/10.1093/bioinformatics/btx573] [PMID: 28968644]
[152]
Yang H, Qin C, Li YH, et al. Therapeutic target database update 2016: Enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res 2016; 44(D1): D1069-74.
[http://dx.doi.org/10.1093/nar/gkv1230] [PMID: 26578601]
[153]
Li B, Tang J, Yang Q, et al. Performance evaluation and online realization of data-driven normalization methods used in LC/MS based untargeted metabolomics analysis. Sci Rep 2016; 6: 38881.
[http://dx.doi.org/10.1038/srep38881] [PMID: 27958387]
[154]
Van Riper SK, Higgins L, Carlis JV, Griffin TJ. RIPPER: A framework for MS1 only metabolomics and proteomics label-free relative quantification. Bioinformatics 2016; 32(13): 2035-7.
[http://dx.doi.org/10.1093/bioinformatics/btw091] [PMID: 27153682]
[155]
Wieczorek S, Combes F, Lazar C, et al. DAPAR & ProStaR: Software to perform statistical analyses in quantitative discovery proteomics. Bioinformatics 2017; 33(1): 135-6.
[http://dx.doi.org/10.1093/bioinformatics/btw580] [PMID: 27605098]
[156]
Yang FY, Fu TT, Zhang XY, et al. Comparison of computational model and X-ray crystal structure of human serotonin transporter: Potential application for the pharmacology of human monoamine transporters. Mol Simul 2017; 43: 1089-98.
[http://dx.doi.org/10.1080/08927022.2017.1309653]
[157]
Weiner AK, Sidoli S, Diskin SJ, Garcia BA. Graphical interpretation and analysis of proteins and their ontologies (GiaPronto): A one-click graph visualization software for proteomics data sets. Mol Cell Proteomics 2018; 17(7): 1426-31.
[http://dx.doi.org/10.1074/mcp.TIR117.000438] [PMID: 29118029]
[158]
Chang C, Xu K, Guo C, et al. PANDA-view: An easy-to-use tool for statistical analysis and visualization of quantitative proteomics data. Bioinformatics 2018; 34(20): 3594-6.
[http://dx.doi.org/10.1093/bioinformatics/bty408] [PMID: 29790911]
[159]
Proietti C, Zakrzewski M, Watkins TS, et al. Mining, visualizing and comparing multidimensional biomolecular data using the Genomics Data Miner (GMine) Web-Server. Sci Rep 2016; 6: 38178.
[http://dx.doi.org/10.1038/srep38178] [PMID: 27922118]
[160]
Yu CY, Li XX, Yang H, et al. Assessing the performances of protein function prediction algorithms from the perspectives of identification accuracy and false discovery rate. Int J Mol Sci 2018; 19(1)E183
[http://dx.doi.org/10.3390/ijms19010183] [PMID: 29316706]
[161]
Teo G, Kim S, Tsou CC, et al. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics 2015; 129: 108-20.
[http://dx.doi.org/10.1016/j.jprot.2015.09.013] [PMID: 26381204]
[162]
Ebhardt HA, Degen S, Tadini V, et al. Comprehensive proteome analysis of human skeletal muscle in cachexia and sarcopenia: A pilot study. J Cachexia Sarcopenia Muscle 2017; 8(4): 567-82.
[http://dx.doi.org/10.1002/jcsm.12188] [PMID: 28296247]
[163]
Xue W, Wang P, Li B, et al. Identification of the inhibitory mechanism of FDA approved selective serotonin reuptake inhibitors: An insight from molecular dynamics simulation study. Phys Chem Chem Phys 2016; 18(4): 3260-71.
[http://dx.doi.org/10.1039/C5CP05771J] [PMID: 26745505]
[164]
Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL. ROTS: An R package for reproducibility-optimized statistical testing. PLOS Comput Biol 2017; 13(5)E1005562
[http://dx.doi.org/10.1371/journal.pcbi.1005562] [PMID: 28542205]
[165]
Bhosale SD, Moulder R, Venäläinen MS, et al. Serum proteomic profiling to identify biomarkers of premature carotid atherosclerosis. Sci Rep 2018; 8(1): 9209.
[http://dx.doi.org/10.1038/s41598-018-27265-9] [PMID: 29907817]
[166]
Wang P, Zhang X, Fu T, et al. Differentiating physicochemical properties between addictive and nonaddictive ADHD drugs revealed by molecular dynamics simulation studies. ACS Chem Neurosci 2017; 8(6): 1416-28.
[http://dx.doi.org/10.1021/acschemneuro.7b00173] [PMID: 28557437]
[167]
Efstathiou G, Antonakis AN, Pavlopoulos GA, et al. ProteoSign: An end-user online differential proteomics statistical analysis platform. Nucleic Acids Res 2017; 45(W1)W300-6
[http://dx.doi.org/10.1093/nar/gkx444] [PMID: 28520987]
[168]
Li YH, Wang PP, Li XX, et al. The Human kinome targeted by FDA approved multi-target drugs and combination products: A comparative study from the drug-target interaction network perspective. PLoS One 2016; 11(11)E0165737
[http://dx.doi.org/10.1371/journal.pone.0165737] [PMID: 27828998]
[169]
Navarro P, Kuharev J, Gillet LC, et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol 2016; 34(11): 1130-6.
[http://dx.doi.org/10.1038/nbt.3685] [PMID: 27701404]
[170]
Hoekman B, Breitling R, Suits F, Bischoff R, Horvatovich P. msCompare: A framework for quantitative analysis of label-free LC-MS data for comparative candidate biomarker studies. Mol Cell Proteomics 2012 11(6): M111 015974.
[171]
Pavlou M. Developing a Proteomic Prognostic Signature for Breast Cancer Patients. Doctoral 2014.
[172]
Strbenac D, Zhong L, Raftery MJ, et al. Quantitative performance evaluator for proteomics (QPEP): Web-based application for reproducible evaluation of proteomics preprocessing methods. J Proteome Res 2017; 16(7): 2359-69.
[http://dx.doi.org/10.1021/acs.jproteome.6b00882] [PMID: 28580786]
[173]
Xu J, Wang P, Yang H, et al. Comparison of FDA approved kinase targets to clinical trial ones: Insights from their system profiles and drug-target interaction networks. BioMed Res Int 2016; 20162509385
[http://dx.doi.org/10.1155/2016/2509385] [PMID: 27547755]
[174]
McDermott JE, Wang J, Mitchell H, et al. Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data. Expert Opin Med Diagn 2013; 7(1): 37-51.
[http://dx.doi.org/10.1517/17530059.2012.718329] [PMID: 23335946]
[175]
Latosinska A, Vougas K, Makridakis M, et al. Comparative Analysis of Label-Free and 8-Plex iTRAQ Approach for Quantitative Tissue Proteomic Analysis. PLoS One 2015; 10(9)e0137048
[176]
Collier TS, Sarkar P, Franck WL, Rao BM, Dean RA, Muddiman DC. Direct comparison of stable isotope labeling by amino acids in cell culture and spectral counting for quantitative proteomics. Anal Chem 2010; 82(20): 8696-702.
[http://dx.doi.org/10.1021/ac101978b] [PMID: 20845935]
[177]
Fenselau C, Yao X. 18O2-labeling in quantitative proteomic strategies: A status report. J Proteome Res 2009; 8(5): 2140-3.
[http://dx.doi.org/10.1021/pr8009879] [PMID: 19338309]
[178]
Megger DA, Bracht T, Meyer HE, Sitek B. Label-free quantification in clinical proteomics. Biochim Biophys Acta 2013; 1834(8): 1581-90.
[http://dx.doi.org/10.1016/j.bbapap.2013.04.001] [PMID: 23567906]
[179]
Stare SM, Jozefowicz JJ. The effects of environmental factors on cancer prevalence rates and specific cancer mortality rates in a sample of OECD developed countries. IJABE 2008; 5: 24.
[180]
Lynn KS, Chen CC, Lih TM, et al. MAGIC: An automated N-linked glycoprotein identification tool using a Y1-ion pattern matching algorithm and in silico MS2 approach. Anal Chem 2015; 87(4): 2466-73.
[http://dx.doi.org/10.1021/ac5044829] [PMID: 25629585]
[181]
Park KS, Tae J, Choi B, et al. Characterization, in vitro cytotoxicity assessment, and in vivo visualization of multimodal, RITC-labeled, silica-coated magnetic nanoparticles for labeling human cord blood-derived mesenchymal stem cells. Nanomedicine (Lond) 2010; 6(2): 263-76.
[http://dx.doi.org/10.1016/j.nano.2009.07.005] [PMID: 19699324]
[182]
Cheng PC, Chang HK, Chen SH. Quantitative nanoproteomics for protein complexes (QNanoPX) related to estrogen transcriptional action. Mol Cell Proteomics 2010; 9(2): 209-24.
[http://dx.doi.org/10.1074/mcp.M900183-MCP200] [PMID: 19805454]
[183]
Hanke S, Besir H, Oesterhelt D, Mann M. Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the attomole level. J Proteome Res 2008; 7(3): 1118-30.
[http://dx.doi.org/10.1021/pr7007175] [PMID: 18271523]
[184]
Roe MR, McGowan TF, Thompson LV, Griffin TJ. Targeted 18O-labeling for improved proteomic analysis of carbonylated peptides by mass spectrometry. J Am Soc Mass Spectrom 2010; 21(7): 1190-203.
[http://dx.doi.org/10.1016/j.jasms.2010.03.029] [PMID: 20434358]
[185]
Li XX, Yin J, Tang J, et al. Determining the balance between drug efficacy and safety by the network and biological system profile of its therapeutic target. Front Pharmacol 2018; 9: 1245.
[http://dx.doi.org/10.3389/fphar.2018.01245] [PMID: 30429792]
[186]
Zhu F, Li XX, Yang SY, Chen YZ. Clinical success of drug targets prospectively predicted by in silico study. Trends Pharmacol Sci 2018; 39(3): 229-31.
[http://dx.doi.org/10.1016/j.tips.2017.12.002] [PMID: 29295742]
[187]
Han ZJ, Xue WW, Tao L, Zhu F. Identification of novel immune-relevant drug target genes for Alzheimer’s Disease by combining ontology inference with network analysis. CNS Neurosci Ther 2018; 24(12): 1253-63.
[http://dx.doi.org/10.1111/cns.13051] [PMID: 30106219]
[188]
Zhu F, Han L, Zheng C, et al. What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets. J Pharmacol Exp Ther 2009; 330(1): 304-15.
[http://dx.doi.org/10.1124/jpet.108.149955] [PMID: 19357322]
[189]
Li YH, Li XX, Hong JJ, et al. Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Brief Bioinform 2019.
[http://dx.doi.org/10.1093/bib/bby130] [PMID: 30689717]
[190]
Li X, Li X, Li Y, et al. What makes species productive of anti-cancer drugs? Clues from drugs’ species origin, druglikeness, target and pathway. Anticancer Agents Med Chem 2018.
[http://dx.doi.org/10.2174/1871520618666181029132017] [PMID: 30370862]
[191]
Tu G, Fu T, Yang F, Yao L, Xue W, Zhu F. Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein−Peptide Docking and Molecular Dynamics Simulation. Molecules 2018; 23(11)E3018
[http://dx.doi.org/10.3390/molecules23113018] [PMID: 30463177]
[192]
Zheng G, Yang F, Fu T, et al. Computational characterization of the selective inhibition of human norepinephrine and serotonin transporters by an escitalopram scaffold. Phys Chem Chem Phys 2018; 20(46): 29513-27.
[http://dx.doi.org/10.1039/C8CP06232C] [PMID: 30457616]
[193]
Yang F, Zheng G, Fu T, et al. Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H. Phys Chem Chem Phys 2018; 20(37): 23873-84.
[http://dx.doi.org/10.1039/C8CP01843J] [PMID: 29947629]
[194]
Xue W, Yang F, Wang P, et al. What contributes to serotonin-norepinephrine reuptake inhibitors’ dual-targeting mechanism? The key role of transmembrane domain 6 in human serotonin and norepinephrine transporters revealed by molecular dynamics simulation. ACS Chem Neurosci 2018; 9(5): 1128-40.
[http://dx.doi.org/10.1021/acschemneuro.7b00490] [PMID: 29300091]
[195]
Xue W, Fu T, Zheng G, et al. Recent advances and challenges of the drugs acting on monoamine transporters. Curr Med Chem 2018.
[http://dx.doi.org/10.2174/0929867325666181009123218] [PMID: 30306851]
[196]
Zheng G, Xue W, Wang P, et al. Exploring the inhibitory mechanism of approved selective norepinephrine reuptake inhibitors and reboxetine enantiomers by molecular dynamics study. Sci Rep 2016; 6: 26883.
[http://dx.doi.org/10.1038/srep26883] [PMID: 27230580]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 25
ISSUE: 13
Year: 2019
Page: [1536 - 1553]
Pages: 18
DOI: 10.2174/1381612825666190618123306
Price: $65

Article Metrics

PDF: 46
HTML: 6