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  1. Science & Research (NCTR)

Suguna Devi Sakkiah Ph.D.
Leadership Role

Staff Fellow — Division of Bioinformatics and Biostatistics

Suguna Sakkiah, Ph.D.

Suguna Devi Sakkiah, Ph.D.
(870) 543-7391
NCTRResearch@fda.hhs.gov  

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About  |  Publications


Background

Dr. Suguna Devi Sakkiah received her Ph.D. from Gyeongsang National University (GNU), South Korea in 2012 and her master’s degree from Bharathiar University, India. In 2010, she received a Young Pioneer Research Scientist award while pursuing her Ph.D. After receiving her Ph.D., she  worked as a postdoctoral researcher at the Gwangju Institute of Science and Technology, the University of California, Los Angeles and the Cedars-Sinai Medical Center, Los Angeles from 2012 to 2015. She joined NCTR as an ORISE postdoctoral researcher in 2015 and converted to an FDA staff fellow in 2018. During her research career, Dr. Sakkiah has published more than 60 peer-reviewed research articles and review papers, and two book chapters.

Research Interests

Dr. Sakkiah’s main interests are in cheminformatics, molecular docking, molecular dynamics simulation, QSAR, database, and machine learning. To assist in safety evaluation of FDA-regulated products Dr. Sakkiah has developed multiple models to predict chemicals such as neuronal acetylcholine esterase binding with endocrine receptors (androgen and estrogen receptors). Currently, she is developing an androgenic activity database (AADB) to enhance knowledge about endocrine disruptors and to apply computational chemistry and machine learning to reduce drug cardiotoxicity and opioid addiction.

Professional Societies/National and International Groups

American Association of Pharmaceutical Scientists
Member
2016 – Present

MidSouth Computational Biology and Bioinformatics Society
Member
2015 – Present

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Select Publications

Publication titles are linked to text abstracts on PubMed.

Structural Changes due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations.
Sakkiah S., Kusko R., Pan B., Guo W., Ge W., Tong W., and Hong H.
Front Pharmacol. 2018, 9:492; doi: 10.3389/fphar.2018.00492. eCollection 2018.

Competitive Docking Model for Prediction of the Human Nicotinic Acetylcholine Receptor α7 Binding of Tobacco Constituents.
Ng H.W., Leggett C., Sakkiah S., Pan B., Ye H., Wu L., Selvaraj C., Tong W., and Hong H.
Oncotarget. 2018, 9(24):16899-16916; doi: 10.18632/oncotarget.24458. eCollection 2018 Mar 30.

Development of Estrogen Receptor Beta Binding Prediction Model Using Larger Sets of Chemicals.
Sakkiah S., Selvaraj C., Gong P., Zhang C., Tong W., and Hong H.
Oncotarget. 2017, 8(54):92989-93000; doi: 10.18632/oncotarget.21723. eCollection 2017 Nov 3.

sNebula, a Network-Based Algorithm to Predict Binding Between Human Leukocyte Antigen and Peptides.
Luo H., Ye H., Ng H.W., Sakkiah S., Mendrick D.L., and Hong H.
Sci Rep. 2016, 6:32115; doi: 10.1038/srep32115.

Pathway Analysis Revealed Potential Diverse Health Impacts of Flavonoids that Bind Estrogen Receptors.
Ye H., Ng H.W., Sakkiah S., Ge W., Perkins R., Tong W., and Hong H.
Int J Environ Res Public Health. 2016, 13(4):373; doi: 10.3390/ijerph13040373.

FOXC1 Activates Smoothened-Independent Hedgehog Signalling in Basal-like Breast Cancer.
Han B., Qu Y., Jin Y., Yu Y., Deng N., Wawrowsky K., Zhang X., Li N., Bose S., Wang Q,. Sakkiah S., Abrol R., Jensen T.W., Berman B.P., Tanaka H., Johnson J., Gao B., Hao J., Liu Z., Buttyan R., Ray P.S., Hung M.C., Giuliano A.E., and Cui X.
Cell Rep. 2015, 13(5):1046-58; doi: 10.1016/j.celrep.2015.09.063.

Dynamic and Multi-Pharmacophore Modeling for Designing Polo-box Domain Inhibitors.
Sakkiah S., Senese S., Yang Q, Lee K.W., and Torres J.Z.
PLoS One. 2014, 9(7): e101405; doi: 10.1371/journal.pone.0101405. eCollection 2014.

Ligand-Based Pharmacophore Modeling and Bayesian Approaches to Identify c-Src Inhibitors.
Sakkiah S., Arullaperumal V., Hwang S., and Lee K.W.
J Enzyme Inhib Med Chem. 2014, 29(1):69-80; doi: 10.3109/14756366.2012.753881. Epub 2013 Feb 25.

Insight the C-Site Pocket Conformational Changes Responsible for Sirtuin 2 Activity Using Molecular Dynamics Simulations.
Sakkiah S., Arooj M., Cao G.P., and Lee K.W.
PLoS One. 2013, 8(3): e59278; doi: 10.1371/journal.pone.0059278. [Epub 2013 Mar 20].

Identification of Inhibitor Binding Site in Human Sirtuin 2 Using Molecular Docking and Dynamics Simulations.
Sakkiah S., Arooj M., Kumar M.R., Eom S.H., and Lee K.W.
PLoS One. 2013, 8(1): e51429; doi: 10.1371/journal.pone.0051429. [Epub 2013 Jan 28].

Molecular Docking and Dynamics Simulations, Receptor-based Hypothesis: Application to Identify Novel Sirtuin 2 Inhibitors.
Sakkiah S., Thangapandian S., Park C., Son M., and Lee K.W.
Chem Biol Drug Des. 2012, 80(2):315-27; doi: 10.1111/j.1747-0285.2012.01406.x. [Epub 2012 Jun 21].

Identification of Important Chemical Features of 11β-Hydroxysteroid Dehydrogenase Type1 Inhibitors: Application of Ligand Based Virtual Screening and Density Functional Theory.
Sakkiah S., Meganathan C., Sohn Y.S., Namadevan S., and Lee K.W.
Int J Mol Sci. 2012,13(4):5138-62; doi: 10.3390/ijms13045138. [Epub 2012 Apr 23].

Molecular Modeling Study: Conformational Changes of Sirtuin 2 Due to Substrate and Inhibitor Binding.
Sakkiah S., Chandrasekaran M., Lee Y., Kim S., and Lee K.W.
J Biomol Struct Dyn. 2012, 30(3):235-54; doi: 10.1080/07391102.2012.680026. [Epub 2012 Jun 12].

Pharmacophore Based Virtual Screening and Density Theory Studies to Identify Novel Butyrylcholinesterase.
Sakkiah S. and Lee K.W.
Acta Pharmacol Sin. 2012, 33(7):964-78; doi: 10.1038/aps.2012.21. [Epub 2012 Jun 11].

Ligand-Based Virtual Screening and Molecular Docking Studies to Identify the Critical Chemical Features of Potent Cathepsin D Inhibitors.
Sakkiah S., Thangapandian S., and Lee K.W.
Chem Biol Drug Des. 2012, 80(1):64-79; doi: 10.1111/j.1747-0285.2012.01339.x. [Epub 2012 May 14].

Pharmacophore Based Virtual Screening, Molecular Docking Studies to Design Potent Heat Shock Protein 90 Inhibitors.
Sakkiah S., Thangapandian S., John S., and Lee K.W.
Eur J Med Chem. 2011, 46(7):2937-47; doi: 10.1016/j.ejmech.2011.04.018. [Epub 2011 Apr 15].

3D QSAR Pharmacophore Based Virtual Screening and Molecular Docking for Identification of Potential HSP90 Inhibitors.
Sakkiah S., Thangapandian S., John S., Kwon Y.J., and Lee K.W.
Eur J Med Chem. 2010, 45(6):2132-40; doi: 10.1016/j.ejmech.2010.01.016. [Epub 2010 Feb 4].

 

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Contact Information
Suguna Devi Sakkiah
870-543-7391
Expertise
Expertise
Approach
Domain
Technology & Discipline
Bioinformatics
Biostatistics
Toxicology
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