Research Article | Open Access
Tzu-Chieh Hung, Wen-Yuan Lee, Kuen-Bao Chen, Yueh-Chiu Chan, Calvin Yu-Chian Chen, "Insight into HIV of IFN-Induced Myxovirus Resistance 2 (MX2) Expressed by Traditional Chinese Medicine", BioMed Research International, vol. 2014, Article ID 871576, 14 pages, 2014. https://doi.org/10.1155/2014/871576
Insight into HIV of IFN-Induced Myxovirus Resistance 2 (MX2) Expressed by Traditional Chinese Medicine
Recently, an important topic of the acquired immunodeficiency syndrome (AIDS) had been published in 2013. In this report, the expression of the IFN-induced myxovirus resistance 2 (MX2) had been defined the function to kill the human immunodeficiency virus (HIV). The screening from the Traditional Chinese Medicine (TCM) database by simulating molecular docking and molecular dynamics could select candidate compounds, which may express MX2 against HIV. Saussureamine C, Crotalaburnine, and Precatorine are selected based on the highest docking score and other TCM compounds. The data from molecular dynamics are helpful in the analysis and detection of protein-ligand interactions. According to the docking poses, hydrophobic interactions, and hydrogen bond with structure variations, this research could assess the interaction between protein and ligand interaction. In addition to the detection of TCM compound efficacy, we suggest that Saussureamine C is better than the others in protein-ligand interaction and the structural variation to express MX2.
The human immunodeficiency virus (HIV) is a retrovirus that causes humans to have the acquired immunodeficiency syndrome disease (AIDS) [1–4]. In this disease, immune system of patient is compromised by the virus, which then allows opportunistic infections, and cancers, to flourish. In the statistics, the main reasons for HIV virus transmission are unprotected sexual intercourse [5, 6], contaminated medical equipment [7, 8], vertical infection (pregnancy, delivery, or breastfeeding) [9, 10], and bodily fluids.
Since the first case in 1981, AIDS has caused nearly thirty-six million deaths and there were still seventy-five million carriers, as reported by UNAIDS (http://www.unaids.org/en/resources/campaigns/globalreport2013/factsheet/). There are still no vaccines or drugs available to kill the virus in clinical treatment; therefore, the highly active antiretroviral therapy (HAART) becomes the standard care for patients with advanced infection . HARRT uses a complex of transcription inhibitors to decrease the patient’s total numbers of HIV virus, but it is expensive and inconvenient medication.
Recent research has pointed out that HIV of IFN-induced myxovirus resistance 2 (MX2) is an important gene against AIDS . The MX is a resistant system to kill virus by overexpression of the IFN-α which imposes an early block to HIV-1 reverse transcription. In human, there are two known kinds of MX system named MX1 and MX2. The MX1 is known as the function against influenza and the MX2 has defined to resist HIV. Thus, a lot IFN be produced from MX2 expressed could be a better treatment for HIV patients .
Computer-Aided Drug Design (CADD), which is an in silico simulation technique to screen for compounds by the structure and the biological activity of drug candidates, has the advantages of both greater speed and lower cost than traditional drug design. There are two major application areas named structure-based drug design and ligand-based drug design of CADD [14–19]. In this research, we used CADD to investigate the ligand efficacy based on structure-based drug design and molecular dynamics.
The personalized medicine and biomedicine  become a well-known knowledge which could analyze the disease associated with the mutation , pathway, and even discuss the cause for special disease . Traditional Chinese medicine (TCM) is a kind of personalized medicine which is an important medical culture in Asia. The TCM Database@Taiwan (http://tcm.cmu.edu.tw/)  is the largest traditional Chinese medicine database in the world built in 2011. This TCM database contains 2D chemical structures, 3D chemical structures, bioactivity, and molecular information for over 61,000 compounds of TCM herbs. From 2011 to 2014, there have been successful discoveries of novel lead compounds from the TCM Database@Taiwan for cancer treatment [24–27], pain relief , and antivirals [28–32]. With the assistance of the application system of the website  and the cloud computing platform , the TCM Database@Taiwan could be valuable for TCM application and drug design.
In this study, we screen the TCM Database@Taiwan to select a possible lead compound against HIV. We use biocomputational technology as molecular docking screening to select ligands. Finally, we apply molecular dynamics (MD) simulation to discuss the protein-ligand interactions that may contribute to the evaluation of the effect of MX2 expression.
2. Materials and Methods
2.1. Data Set
Accelrys Discovery Studio 2.5 (DS 2.5) was used for the molecular simulations. A total of 61,000 TCM compounds were downloaded from the TCM database (http://tcm.cmu.edu.tw/). The MX2 sequence was generated from UniProt (ID: P20592) and made the prediction of the 3D structure from Ittarsser wed server (http://zhanglab.ccmb.med.umich.edu/I-TASSER/) . Then to detect correction for the residue of prediction MX2 structure, we submit the structure to rampage Ramachandran (http://mordred.bioc.cam.ac.uk/~rapper/rampage.php).
2.2. Disorder Protein Detection
Because the disorder plays an important role in drug design, we submit protein structure and docking site to predict the disorder region by the Database of Protein Disorder (DisProt: http://www.disprot.org/) . As a result of the prediction, we can decide the character of the docking site and the efficacy of the drug [17, 37].
The docking site designed nearby the important amino acids based on UniProt was reported. Based on a comparison of the disorder regions and the docking sites, we could assess the protein-ligand interaction and drug efficacy.
2.3. Molecular Docking
The docking simulation used the LigandFit  module to dock the TCM compounds to MX2 in the force field of CHARMm . LigandFit is a receptor-rigid docking algorithm program in Discovery Studio 2.5 (DS 2.5). The docking site of MX2 was identified by the nucleotide binding region which was recorded as the relation with GTP function. After docking, the top three docking scores of the compounds were selected and then analyzed for hydrophobic interactions by Ligplus [40, 41].
2.4. Molecular Dynamics Simulation
These ligands must be reprepared based on the reference force field  of GROMACS 4.5.5  by using SwissParam (http://swissparam.ch/)  before applying MD simulation. A simulation box is performed for complex of the protein with candidate compound. The cubic box with a minimum distance of 1.2 Å from the complex was solvated based on the TIP3P water model. The TIP3P water model supports sodium and chloride ion for simulation to neutralize complex charges. The complex takes the minimization with the Steepest Descent method for 5,000 steps. The last structure with the minimum energy was transferred to MD simulation. Electrostatic interactions were calculated based on the Particle-Mesh Ewald (PME) method  with each time step being 2 fs and the numbers of steps were 2,500,000 times. The Berendsen weak thermal coupling method for the equilibration was under the 100 ps constant temperature (PER ensemble). The total simulation time of MD was 5,000 ps. MD trajectories, RMSD, energy, H-bond, and eigenvector calculation of the complex were analyzed using a series of protocols in Gromacs.
3. Results and Discussion
3.1. The Detection of Disorder Protein
The disorder protein is defined as unstructured protein. While the docking site consists of a disorder region, the drug docks to protein hardly and the complex will stabilize with difficultly. The cited references [17, 37] denote that the ligand docks into the disorder region may have weaker side effect than the widespread domain. Therefore, the disorder region should be defined as a hard work for drug design and not a bad docking site for selection. The disorder regions of MX2 are defined as having a disposition of over 0.5 (Figure 1). This result presents the most residue of MX2 for docking site design that do not consist of disorder regions; thus the docking simulation is appropriate without disorder infection.
3.2. Prediction Protein Modeling Detection
The prediction model should detect the credibility of the structure. The rampage Ramachandran uses the structure of each residue to evaluate the unreasonable position that will make amino acids collide while the protein twists (Figure 2). In this result, the amino acids Gly37, Gly75, Gly111, Gly302, Gly408, Gly540, and Glu671 are defined as unreasonable position in MX2. Fortunately, these amino acids are not as important as acids and docking site; thus the prediction model is credible for simulation.
3.3. Molecular Docking
After molecular docking and ranking by docking score, the top three TCM compounds can be selected (Table 1). These TCM compounds are Saussureamine C, Crotalaburnine, and Precatorine extracted from the TCM herbs Saussurea lappa Clarke, Crotalaria laburnifolia (or Crotalaria anagyroides), and Abrus precatorius, respectively. The top compound, Saussureamine C, is found having antiulcer function  and its herb Saussurea lappa Clarke can prevent breast cancer , treat heart disease [48, 49], have antihepatotoxic activity , and express the cytotoxic T lymphocytes . The second ranked compounds with the herb do not have reference about immunity. The third ranked herb Abrus precatorius had been defined the relation about Antiprotozoal [52, 53], antimicrobial [54–56], anti-inflammatory , apoptosis [58, 59], and immunotoxin . Most of the reported literatures indicate that these compounds can have an effect on immunity; therefore we suggest that the selected compounds can have an influence on MX2.
The structure of the candidate compounds screened from TCM database is displayed in Figure 3. The docking poses and the neighbor amino acid in binding site are presented in Figure 4. This result indicates that the residues Gln127, Ser147, Ile149, Gln330, and Ile333 have been interacted with all ligands. Thus, we could be suggested that these amino acids may play important roles for ligand to bind with MX2.
The program Ligplus can analyze the possible H-bond and hydrophobic interaction (Figure 5). In this figure, the amino acids colored deep red are at high frequency while proteins have interactions with the ligands through hydrophobic interactions or hydrogen bonds. Thus these amino acids supply the possibility that these amino acids may have the function on protein-ligand interaction.
3.4. Molecular Dynamics Simulation
The RMSD and total energy of a complex during MD simulation were recorded (Figure 6). The total energy is in the range between −1295~−1285103 kJ/mol and tends to be −1290103 kJ/mol. The amplitude tends to be gentle and the energy is lower which indicates that the complex is more and more stable.
The root mean square derivation (RMSD) is the calculation of the root mean square for each atom in MD to describe the position variation focus on protein, ligand, and complex (Figure 7). In this figure, the RMSD variation of the complexes are lower than of Apo form (unbound protein), which presents that the conformation of MX2 with compounds will be more stable than unbound one.
The RMSF is the average of RMS for each residue position variation in the whole MD (Figure 8). To compare the RMSF between the protein in complex and in Apo form (unbound protein), we could find the effect of each ligand to similar residues in important region of protein. In Figure 8, the similar pick site confirms that the docking site is designed correctly and the top1 compound Saussureamine C has better effect on MX2 than others.
The clustering is a method to classify the MD trajectaries into several groups using RMSD variation (Figure 9). In this figure, the largest group of Apo form (unbound protein) is not the last group which may present this structure of Apo form is unstable in simulation. Thus, the clustering for complex means that the protein with ligand will be stable. Among these compounds, the Saussureamine C could make a balance quickly.
The structure variation could help in discussing the interaction and the function expressed from ligand effect; thus the analysis of structure variation is necessary (Figures 10 to 13). In Figure 10, the compound has made the protein variation after compound dock to protein. In this result, we found that the selected compounds could make strong influence on MX2, besides Precatorine.
In Figure 11(a), the Gly146 and Arg336 have high H-bond occupancy during the MD simulation. We suggest that this situation might make the compound close to docking site; then the compound could have more effect on functional domain. Figure 11(b) presents the large composition variation of MX2 while interacting with Saussureamine C. We suggest Saussureamine C could have strong effect on MX2 in this situation.
In Figure 13(a), the data recorded that Precatorine could become H-bond with Ser132 and Ser147 but the occupancy is less than others (it means that the frequency is less than other selected compounds). The protein variation could be found, especially around the docking site.
Based on the above discussion, we found that there is highly H-bond occupancy in protein-ligand interaction from different residues nearby docking site. In this situation, we suggest that the H-bond play an important role for the compounds to bind with MX2. It might be presented the binding affinity of ligands to MX2. For this reason, strong force interaction of H-bond make protein and ligand to be more stable. Then, the situation of high H-bond occupancy in MD simulation cause the complex with Saussureamine C tend to stable quickly. Finally, this research indicates that the interaction of compound with the residue in the binding site might active MX2 and simulate the immunity function.
Based on above discussion, we found that the top three TCM compounds Saussureamine C, Crotalaburnine, and Precatorine can have an effect on MX2 against the HIV alive. The residue around the docking site might use H-bond interaction to make antigen or compound simulate the immunity function through MX2 expressed. The structural variations indicate that all compounds can have an effect on immunity function, but Saussureamine C has the best effect on the activation of MX2.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Tzu-Chieh Hung, Wen-Yuan Lee and Kuen-Bao Chen equally contributed to this paper.
The research was supported by Grants from the National Science Council of Taiwan (NSC102-2325-B039-001, NSC102-2221-E-468-027-), Asia University (ASIA100-CMU-2, ASIA101-CMU-2, and 102-Asia-07), and China Medical University Hospital (DMR-103-058, DMR-103-001, and DMR-103-096). This study is also supported in part by Taiwan Department of Health Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004) and Taiwan Department of Health Cancer Research Center of Excellence (MOHW103-TD-B-111-03). and CMU under the Aim for Top University Plan of the Ministry of Education, Taiwan. The authors are grateful to Weng Leong Tou and Yi-Teh Liu for their help in this research.
- F. Barre-Sinoussi, J. C. Chermann, F. Rey et al., “Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (AIDS),” Science, vol. 220, no. 4599, pp. 868–871, 1983.
- M. W. Cloyd, J. J. Y. Chen, and L. Q. Wang, “How does HIV cause AIDS? The homing theory,” Molecular Medicine Today, vol. 6, no. 3, pp. 108–111, 2000.
- R. A. Weiss, “How does HIV cause AIDS?” Science, vol. 260, no. 5112, pp. 1273–1279, 1993.
- D. C. Douek, M. Roederer, and R. A. Koup, “Emerging concepts in the immunopathogenesis of AIDS,” Annual Review of Medicine, vol. 60, pp. 471–484, 2009.
- S. Weller and K. Davis, “Condom effectiveness in reducing heterosexual HIV transmission,” The Cochrane Database of Systematic Reviews, no. 1, Article ID CD003255, 2002.
- B. Leynaert, A. M. Downs, and I. de Vincenzi, “Heterosexual transmission of human immunodeficiency virus: variability of infectivity throughout the course of infection,” The American Journal of Epidemiology, vol. 148, no. 1, pp. 88–96, 1998.
- D. M. Bell, “Occupational risk of human immunodeficiency virus infection in healthcare workers: an overview,” The American Journal of Medicine, vol. 102, no. 5, pp. 9–15, 1997.
- J. L. Cleveland, L. Barker, B. F. Gooch, E. M. Beltrami, and D. Cardo, “Use of HIV postexposure prophylaxis by dental health care personnel: an overview and updated recommendations,” Journal of the American Dental Association, vol. 133, no. 12, pp. 1619–1626, 2002.
- J. C. Forbes, A. M. Alimenti, J. Singer et al., “A national review of vertical HIV transmission,” AIDS, vol. 26, no. 6, pp. 757–763, 2012.
- K. M. Little, P. H. Kilmarx, A. W. Taylor, C. E. Rose, E. D. Rivadeneira, and S. R. Nesheim, “A review of evidence for transmission of HIV from children to breastfeeding women and implications for prevention,” The Pediatric Infectious Disease Journal, vol. 31, no. 9, pp. 938–942, 2012.
- D. Finzi, M. Hermankova, T. Pierson et al., “Identification of a reservoir for HIV-1 in patients on highly active antiretroviral therapy,” Science, vol. 278, no. 5341, pp. 1295–1300, 1997.
- C. Goujon, O. Moncorge, H. Bauby et al., “Human MX2 is an interferon-induced post-entry inhibitor of HIV-1 infection,” Nature, vol. 502, no. 7472, pp. 559–562, 2013.
- J. W. Schoggins, S. J. Wilson, M. Panis et al., “A diverse range of gene products are effectors of the type I interferon antiviral response,” Nature, vol. 472, no. 7344, pp. 481–485, 2011.
- H.-J. Huang, H. W. Yu, C.-Y. Chen et al., “Current developments of computer-aided drug design,” Journal of the Taiwan Institute of Chemical Engineers, vol. 41, no. 6, pp. 623–635, 2010.
- W. I. Tou, S. S. Chang, C. C. Lee, and C. Y. Chen, “Drug design for neuropathic pain regulation from traditional Chinese medicine,” Scientific Reports, vol. 3, p. 844, 2013.
- C. Y. Chen, “A novel integrated framework and improved methodology of computer-aided drug design,” Current Topics in Medicinal Chemistry, vol. 13, no. 9, pp. 965–988, 2013.
- C. Y.-C. Chen and W. I. Tou, “How to design a drug for the disordered proteins?” Drug Discovery Today, vol. 18, no. 19-20, pp. 910–915, 2013.
- S. C. Basak, “Recent developments and future directions at current computer aided drug design,” Current Computer-Aided Drug Design, vol. 9, no. 1, p. 1, 2013.
- C. Y. Chen, “Weighted equation and rules—a novel concept for evaluating protein-ligand interaction,” Journal of Biomolecular Structure and Dynamics, vol. 27, no. 3, pp. 271–282, 2009.
- W.-L. Liao and F.-J. Tsai, “Personalized medicine: a paradigm shift in healthcare,” BioMedicine, vol. 3, no. 2, pp. 66–72, 2013.
- C. Lee -, C. Tsai -H, L. Wan et al., “Increased incidence of Parkinsonism among Chinese with β-glucosidase mutation in central Taiwan,” BioMedicine, vol. 3, no. 2, pp. 92–94, 2013.
- I. C. Chou, W. Lin -D, C. Wang -H et al., “Möbius syndrome in a male with XX/XY mosaicism,” BioMedicine, vol. 3, no. 2, pp. 102–104, 2013.
- C. Y. Chen, “TCM Database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico,” PLoS ONE, vol. 6, no. 1, Article ID e15939, 2011.
- H. J. Huang, K. J. Lee, H. W. Yu et al., “Structure-based and ligand-based drug design for HER 2 receptor,” Journal of Biomolecular Structure and Dynamics, vol. 28, no. 1, pp. 23–37, 2010.
- W. I. Tou and C. Y. Chen, “Traditional Chinese medicine as dual guardians against hypertension and cancer?” Journal of Biomolecular Structure and Dynamics, vol. 30, no. 3, pp. 299–317, 2012.
- S. C. Yang, S. S. Chang, and C. Y. Chen, “Identifying HER2 inhibitors from natural products database,” PLoS ONE, vol. 6, no. 12, Article ID e28793, 2011.
- C. Y. Chen and C. Y. Chen, “Insights into designing the dual-targeted HER2/HSP90 inhibitors,” Journal of Molecular Graphics and Modelling, vol. 29, no. 1, pp. 21–31, 2010.
- S. S. Chang, H. J. Huang, and C. Y. Chen, “High performance screening, structural and molecular dynamics analysis to identify H1 inhibitors from TCM Database@Taiwan,” Molecular BioSystems, vol. 7, no. 12, pp. 3366–3374, 2011.
- C. H. Lin, T. T. Chang, M. F. Sun et al., “Potent inhibitor design against H1N1 swine influenza: structure-based and molecular dynamics analysis for M2 inhibitors from traditional Chinese medicine database,” Journal of Biomolecular Structure and Dynamics, vol. 28, no. 4, pp. 471–482, 2011.
- S. S. Chang, H. J. Huang, and C. Y. Chen, “Two birds with one stone? Possible dual-targeting H1N1 inhibitors from traditional Chinese medicine,” PLoS Computational Biology, vol. 7, no. 12, Article ID e1002315, 2011.
- C. Y. Chen, Y. H. Chang, D. T. Bau et al., “Ligand-based dual target drug design for H1N1: swine flu—a preliminary first study,” Journal of Biomolecular Structure and Dynamics, vol. 27, no. 2, pp. 171–178, 2009.
- H. J. Huang, Y. R. Jian, and C. Y. Chen, “Traditional Chinese medicine application in HIV: an in silico study,” Journal of Biomolecular Structure and Dynamics, vol. 32, no. 1, pp. 1–12, 2014.
- T. Y. Tsai, K. W. Chang, and C. Y. Chen, “IScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan,” Journal of Computer-Aided Molecular Design, vol. 25, no. 6, pp. 525–531, 2011.
- K. W. Chang, T. Y. Tsai, K. C. Chen et al., “iSMART: an integrated cloud computing web server for traditional Chinese medicine for online virtual screening, de novo evolution and drug design,” Journal of Biomolecular Structure and Dynamics, vol. 29, no. 1, pp. 243–250, 2011.
- Y. Zhang, “I-TASSER server for protein 3D structure prediction,” BMC Bioinformatics, vol. 9, article 40, 2008.
- B. Xue, R. L. Dunbrack, R. W. Williams, A. K. Dunker, and V. N. Uversky, “PONDR-FIT: a meta-predictor of intrinsically disordered amino acids,” Biochimica et Biophysica Acta, vol. 1804, no. 4, pp. 996–1010, 2010.
- W. I. Tou and C. Y. Chen, “May disordered protein cause serious drug side effect?” Drug Discovery Today, vol. 19, no. 4, pp. 367–372, 2013.
- C. M. Venkatachalam, X. Jiang, T. Oldfield, and M. Waldman, “LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites,” Journal of Molecular Graphics and Modelling, vol. 21, no. 4, pp. 289–307, 2003.
- B. R. Brooks, C. L. Brooks III, A. D. Mackerell Jr. et al., “CHARMM: the biomolecular simulation program,” Journal of Computational Chemistry, vol. 30, no. 10, pp. 1545–1614, 2009.
- R. A. Laskowski and M. B. Swindells, “LigPlot+: multiple ligand-protein interaction diagrams for drug discovery,” Journal of Chemical Information and Modeling, vol. 51, no. 10, pp. 2778–2786, 2011.
- A. C. Wallace, R. A. Laskowski, and J. M. Thornton, “LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions,” Protein Engineering, vol. 8, no. 2, pp. 127–134, 1995.
- U. D. Priyakumar and A. D. MacKerell, “Comparison of the CHARMM27, AMBER4.1 and BMS nucleic acid force fields via free energy calculations of base flipping,” Abstracts of Papers of the American Chemical Society, vol. 230, pp. U1391–U1392, 2005.
- B. Hess, C. Kutzner, D. van der Spoel, and E. Lindahl, “GRGMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation,” Journal of Chemical Theory and Computation, vol. 4, no. 3, pp. 435–447, 2008.
- V. Zoete, M. A. Cuendet, A. Grosdidier, and O. Michielin, “SwissParam: a fast force field generation tool for small organic molecules,” Journal of Computational Chemistry, vol. 32, no. 11, pp. 2359–2368, 2011.
- T. A. Darden and L. G. Pedersen, “Molecular modeling: an experimental tool,” Environmental Health Perspectives, vol. 101, no. 5, pp. 410–412, 1993.
- M. Yoshikawa, S. Hatakeyama, Y. Inoue, and J. Yamahara, “Saussureamines A, B, C, D, and E, new anti-ulcer principles from Chinese Saussureae radix,” Chemical and Pharmaceutical Bulletin, vol. 41, no. 1, pp. 214–216, 1993.
- Y. K. Choi, S. G. Cho, S. M. Woo et al., “Saussurea lappa clarke-derived costunolide prevents TNF alpha -induced breast cancer cell migration and invasion by inhibiting NF- kappa B activity,” Evidence-Based Complementary and Alternative Medicine, vol. 2013, Article ID 936257, 10 pages, 2013.
- O. P. Upadhyay, J. K. Ojha, H. S. Bajpai, and A. K. Hathwal, “Study of kustha (Saussurea lappa, Clarke) in ischaemic heart disease,” Ancient Science of Life, vol. 13, no. 1-2, pp. 11–18, 1993.
- T. S. Saleem, N. Lokanath, A. Prasanthi, M. Madhavi, G. Mallika, and M. N. Vishnu, “Aqueous extract of Saussurea lappa root ameliorate oxidative myocardial injury induced by isoproterenol in rats,” Journal of Advanced Pharmaceutical Technology and Research, vol. 4, no. 2, pp. 94–100, 2013.
- S. Yaeesh, Q. Jamal, A. J. Shah, and A. H. Gilani, “Antihepatotoxic activity of Saussurea lappa extract on D-galactosamine and lipopolysaccharide-induced hepatitis in mice,” Phytotherapy Research, vol. 24, supplement 2, pp. S229–S232, 2010.
- M. Taniguchi, T. Kattaoka, H. Suzuki et al., “Costunolide and dehydrocostus lactone as inhibitors of killing function of cytotoxic T lymphocytes,” Bioscience, Biotechnology and Biochemistry, vol. 59, no. 11, pp. 2064–2067, 1995.
- Y. Hata, S. N. Ebrahimi, and M. De Mieri, “Antitrypanosomal isoflavan quinones from Abrus precatorius,” Fitoterapia, vol. 93, pp. 81–87, 2014.
- Y. Hata, M. Raith, S. N. Ebrahimi et al., “Antiprotozoal isoflavan quinones from Abrus precatorius ssp. africanus,” Planta Medica, vol. 79, no. 6, pp. 492–498, 2013.
- O. Adelowotan, I. Aibinu, E. Adenipekun, and T. Odugbemi, “The in-vitro antimicrobial activity of Abrus precatorius (L) fabaceae extract on some clinical pathogens,” The Nigerian Postgraduate Medical Journal, vol. 15, no. 1, pp. 32–37, 2008.
- A. A. Rahman, V. Samoylenko, S. K. Jain et al., “Antiparasitic and antimicrobial isoflavanquinones from Abrus schimperi,” Natural Product Communications, vol. 6, no. 11, pp. 1645–1650, 2011.
- V. Jayakumar, R. Bhaskaran, and S. Tsushima, “Potential of plant extracts in combination with bacterial antagonist treatment as biocontrol agent of red rot of sugarcane,” Canadian Journal of Microbiology, vol. 53, no. 2, pp. 196–206, 2007.
- S. C. Kuo, S. C. Chen, L. H. Chen, J. B. Wu, J. P. Wang, and C. M. Teng, “Potent antiplatelet, anti-inflammatory and antiallergic isoflavanquinones from the roots of Abrus precatorius,” Planta Medica, vol. 61, no. 4, pp. 307–312, 1995.
- N. Saxena, P. Yadav, and O. Kumar, “The Fas/Fas ligand apoptotic pathway is involved in abrin-induced apoptosis,” Toxicological Sciences, vol. 135, no. 1, pp. 103–118, 2013.
- M. Shafi Sofi, M. K. Sateesh, M. Bashir et al., “Cytotoxic and pro-apoptotic effects of Abrus precatorius L. on human metastatic breast cancer cell line, MDA-MB-231,” Cytotechnology, vol. 65, no. 3, pp. 407–417, 2013.
- S. Gadadhar and A. A. Karande, “Abrin immunotoxin: targeted cytotoxicity and intracellular trafficking pathway,” PLoS ONE, vol. 8, no. 3, Article ID e58304, 2013.
- S.C. Lovell, L. W. Davis, W. B. Arendall III et al., “Structure validation by C geometry: , and C deviation,” Proteins: Structure, Function, and Bioinformatics, vol. 50, no. 3, pp. 437–450, 2003.
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