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Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors

Yıl 2024, Cilt: 28 Sayı: 4, 1099 - 1106, 28.06.2025

Florentinus Dika Octa Riswanto Stephanus Satria Wira Waskitha Michael Resta Surya Yanuar Enade Perdana Istyastono

Öz

Acetylcholinesterase (AChE) inhibitors have been used to delay the dementia progression in Alzheimer’s Disease (AD). In 2017, a structure-based virtual screening (SBVS) protocol was made publicly available and successfully employed to discover chalcone derivatives and short peptides as AChE inhibitors. During the upgrading process of the SBVS protocol, an optimized version of the enhanced directory of useful decoys (DUDE) was released. This optimized DUDE was named DUDE-Z. In this article, the re-optimization of the upgraded SBVS protocol is presented. The optimization process made use of a machine learning package and library called recursive partitioning and regression tree (RPART) in R statistical computing software environment. The optimized SBVS protocol has the F-measure value of 0.322 against the DUDE-Z. The protocol was subsequently analyzed to efficiently screen on a newly released open- accessed natural products database LOTUS (https://lotus.naturalproducts.net/) to discover bioactive natural products as AChE inhibitors. The SBVS campaigns on 276,518 natural products identified 867 compounds as virtual hits, thirty- seven of which were identified as compounds found in the species from Kingdom Plantae.

Anahtar Kelimeler

Structure-based virtual screening machine learning acetylcholinesterase natural products

Kaynakça

  • [1] Silva T, Reis J, Teixeira J, Borges F. Alzheimer’s disease, enzyme targets and drug discovery struggles: From natural products to drug prototypes. Ageing Res Rev. 2014; 15:116–145. https://doi.org/10.1016/j.arr.2014.03.008
  • [2] Tan C-C, Yu J-T, Wang H-F, Tan M-S, Meng X-F, Wang C, Jiang T, Zhu X-C, Tan L. Efficacy and safety of donepezil, galantamine, rivastigmine, and memantine for the treatment of Alzheimer’s disease: A systematic review and meta-analysis. J Alzheimers Dis. 2014; 41(2):615–631. https://doi.org/10.3233/JAD-132690
  • [3] Babashpour-Asl M, Kaboudi PS, Barez SR. Therapeutic and medicinal effects of snowdrop (Galanthus spp.) in Alzheimer’s disease: A review. J Educ Health Promot. 2023; 12(1):128. https://doi.org/10.4103/jehp.jehp_451_22
  • [4] Sahoo AK, Dandapat J, Dash UC, Kanhar S. Features and outcomes of drugs for combination therapy as multi-targets strategy to combat Alzheimer’s disease. J Ethnopharmacol. 2018; 215:42–73. https://doi.org/10.1016/j.jep.2017.12.015
  • [5] Rutz A, Sorokina M, Galgonek J, Mietchen D, Willighagen E, Gaudry A, Graham JG, Stephan R, Page R, Vondrášek J, Steinbeck C, Pauli GF, Wolfender JL, Bisson J, Allard PM. The LOTUS initiative for open knowledge management in natural products research. Elife. 2022; 11:1–41. https://doi.org/10.7554/eLife.70780
  • [6] Istyastono EP, Riswanto FDO, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS and receptor ensemble docking increase the prediction accuracy of the structure-based virtual screening protocol targeting acetylcholinesterase. Molecules. 2022; 27(17):5661. https://doi.org/10.3390/molecules27175661
  • [7] Mysinger MM, Carchia M, Irwin JJ, Shoichet BK. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J Med Chem. 2012; 55:6582–6594. https://doi.org/10.1021/jm300687e
  • [8] Stein RM, Yang Y, Balius TE, O’Meara MJ, Lyu J, Young J, Tang K, Shoichet BK, Irwin JJ. Property-unmatched decoys in docking benchmarks. J Chem Inf Model. 2021; 61:699–714. https://doi.org/10.1021/acs.jcim.0c00598
  • [9] Therneau T, Atkinson B, Ripley B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-9. 2015. http://CRAN.R-project.org/package=rpart
  • [10] Istyastono EP, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS-assisted prediction of molecular determinants of ligand binding to receptors. Molecules. 2021; 26(9):2452. https://doi.org/10.3390/molecules26092452
  • [11] Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016; 11:905–919. https://doi.org/10.1038/nprot.2016.051
  • [12] Zhao X, Nie X. Status forecasting based on the baseline Information using logistic regression. Entropy(Basel). 2022; 24(10):1481. https://doi.org/10.3390/e24101481
  • [13] Asako Y, Uesawa Y. High-performance prediction of human estrogen receptor agonists based on chemical structures. Molecules. 2017; 22(4):675. https://doi.org/10.3390/molecules22040675
  • [14] Cappel D, Dixon SL, Sherman W, Duan J. Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling. J Comput Aided Mol Des. 2015; 29:165–182. https://doi.org/10.1007/s10822-014-9813-4
  • [15] Smits RA, Adami M, Istyastono EP, Zuiderveld OP, van Dam CME, de Kanter FJJ, Jongejan A, Coruzzi G, Leurs R, de Esch IJP. Synthesis and QSAR of quinazoline sulfonamides as highly potent human histamine H4 receptor inverse agonists. J Med Chem. 2010; 53:2390–2400. https://doi.org/10.1021/jm901379s
  • [16] Istyastono EP, Radifar M, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS: A molecular interaction fingerprinting tool for docking results of AutoDock Vina and PLANTS. J Chem Inf Model. 2020; 60(8):3697–3702. https://doi.org/10.1021/acs.jcim.0c00305
  • [17] Jordaan MA, Ebenezer O, Damoyi N, Shapi M. Virtual screening, molecular docking studies and DFT calculations of FDA approved compounds similar to the non-nucleoside reverse transcriptase inhibitor (NNRTI) efavirenz. Heliyon. 2020; 6(8):e04642. https://doi.org/10.1016/j.heliyon.2020.e04642
  • [18] Van Der Westhuizen CJ, Stander A, Riley DL, Panayides JL. Discovery of novel acetylcholinesterase inhibitors by virtual screening, in vitro screening, and molecular dynamics simulations. J Chem Inf Model. 2022; 62(6):1550–1572. https://doi.org/10.1021/acs.jcim.1c01443
  • [19] Burianek LE, Soderling SH. Under lock and key: Spatiotemporal regulation of WASP family proteins coordinates separate dynamic cellular processes. Semin Cell Dev Biol. 2013; 24(4):258–266. https://doi.org/10.1016/j.semcdb.2012.12.005
  • [20] Parveen M, Ahmad F, Malla AM, Azaz S, Alam M, Basudan OA, Silva MR, Silva PSP. Acetylcholinesterase and cytotoxic activity of chemical constituents of Clutia lanceolata leaves and its molecular docking study. Nat Prod Bioprospect. 2016; 6:267–278. https://doi.org/10.1007/s13659-016-0110-x
  • [21] Ozarowski M, Mikolajczak PL, Bogacz A, Gryszczynska A, Kujawska M, Jodynis-Liebert J, Piasecka A, Napieczynska H, Szulc M, Kujawski R, Bartkowiak-Wieczorek J, Cichocka J, Bobkiewicz-Kozlowska T, Czerny B, Mrozikiewicz PM. Rosmarinus officinalis L. leaf extract improves memory impairment and affects acetylcholinesterase and butyrylcholinesterase activities in rat brain. Fitoterapia. 2013; 91:261–271. https://doi.org/10.1016/j.fitote.2013.09.012
  • [22] Chethana KR, Sasidhar BS, Naika M, Keri RS. Phytochemical composition of Caesalpinia crista extract as potential source for inhibiting cholinesterase and β-amyloid aggregation: Significance to Alzheimer’s disease. Asian Pac J Trop Biomed. 2018; 8(10):500–512. https://doi.org/10.4103/2221-1691.244159
  • [23] Krieger E, Vriend G. New ways to boost molecular dynamics simulations. J Comput Chem. 2015; 36(13):996–1007. https://doi.org/10.1002/jcc.23899
  • [24] Ravindranath PA, Forli S, Goodsell DS, Olson AJ, Sanner MF. AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput Biol. 2015; 11(12): e1004586 https://doi.org/10.1371/journal.pcbi.1004586
  • [25] Cannon EO, Amini A, Bender A, Sternberg MJE, Muggleton SH, Glen RC, Mitchell JBO. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des. 2007; 21:269–280. https://doi.org/1[0.1007/s10822-007-9113-3

Yıl 2024, Cilt: 28 Sayı: 4, 1099 - 1106, 28.06.2025

Florentinus Dika Octa Riswanto Stephanus Satria Wira Waskitha Michael Resta Surya Yanuar Enade Perdana Istyastono

Öz

Kaynakça

  • [1] Silva T, Reis J, Teixeira J, Borges F. Alzheimer’s disease, enzyme targets and drug discovery struggles: From natural products to drug prototypes. Ageing Res Rev. 2014; 15:116–145. https://doi.org/10.1016/j.arr.2014.03.008
  • [2] Tan C-C, Yu J-T, Wang H-F, Tan M-S, Meng X-F, Wang C, Jiang T, Zhu X-C, Tan L. Efficacy and safety of donepezil, galantamine, rivastigmine, and memantine for the treatment of Alzheimer’s disease: A systematic review and meta-analysis. J Alzheimers Dis. 2014; 41(2):615–631. https://doi.org/10.3233/JAD-132690
  • [3] Babashpour-Asl M, Kaboudi PS, Barez SR. Therapeutic and medicinal effects of snowdrop (Galanthus spp.) in Alzheimer’s disease: A review. J Educ Health Promot. 2023; 12(1):128. https://doi.org/10.4103/jehp.jehp_451_22
  • [4] Sahoo AK, Dandapat J, Dash UC, Kanhar S. Features and outcomes of drugs for combination therapy as multi-targets strategy to combat Alzheimer’s disease. J Ethnopharmacol. 2018; 215:42–73. https://doi.org/10.1016/j.jep.2017.12.015
  • [5] Rutz A, Sorokina M, Galgonek J, Mietchen D, Willighagen E, Gaudry A, Graham JG, Stephan R, Page R, Vondrášek J, Steinbeck C, Pauli GF, Wolfender JL, Bisson J, Allard PM. The LOTUS initiative for open knowledge management in natural products research. Elife. 2022; 11:1–41. https://doi.org/10.7554/eLife.70780
  • [6] Istyastono EP, Riswanto FDO, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS and receptor ensemble docking increase the prediction accuracy of the structure-based virtual screening protocol targeting acetylcholinesterase. Molecules. 2022; 27(17):5661. https://doi.org/10.3390/molecules27175661
  • [7] Mysinger MM, Carchia M, Irwin JJ, Shoichet BK. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J Med Chem. 2012; 55:6582–6594. https://doi.org/10.1021/jm300687e
  • [8] Stein RM, Yang Y, Balius TE, O’Meara MJ, Lyu J, Young J, Tang K, Shoichet BK, Irwin JJ. Property-unmatched decoys in docking benchmarks. J Chem Inf Model. 2021; 61:699–714. https://doi.org/10.1021/acs.jcim.0c00598
  • [9] Therneau T, Atkinson B, Ripley B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-9. 2015. http://CRAN.R-project.org/package=rpart
  • [10] Istyastono EP, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS-assisted prediction of molecular determinants of ligand binding to receptors. Molecules. 2021; 26(9):2452. https://doi.org/10.3390/molecules26092452
  • [11] Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016; 11:905–919. https://doi.org/10.1038/nprot.2016.051
  • [12] Zhao X, Nie X. Status forecasting based on the baseline Information using logistic regression. Entropy(Basel). 2022; 24(10):1481. https://doi.org/10.3390/e24101481
  • [13] Asako Y, Uesawa Y. High-performance prediction of human estrogen receptor agonists based on chemical structures. Molecules. 2017; 22(4):675. https://doi.org/10.3390/molecules22040675
  • [14] Cappel D, Dixon SL, Sherman W, Duan J. Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling. J Comput Aided Mol Des. 2015; 29:165–182. https://doi.org/10.1007/s10822-014-9813-4
  • [15] Smits RA, Adami M, Istyastono EP, Zuiderveld OP, van Dam CME, de Kanter FJJ, Jongejan A, Coruzzi G, Leurs R, de Esch IJP. Synthesis and QSAR of quinazoline sulfonamides as highly potent human histamine H4 receptor inverse agonists. J Med Chem. 2010; 53:2390–2400. https://doi.org/10.1021/jm901379s
  • [16] Istyastono EP, Radifar M, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS: A molecular interaction fingerprinting tool for docking results of AutoDock Vina and PLANTS. J Chem Inf Model. 2020; 60(8):3697–3702. https://doi.org/10.1021/acs.jcim.0c00305
  • [17] Jordaan MA, Ebenezer O, Damoyi N, Shapi M. Virtual screening, molecular docking studies and DFT calculations of FDA approved compounds similar to the non-nucleoside reverse transcriptase inhibitor (NNRTI) efavirenz. Heliyon. 2020; 6(8):e04642. https://doi.org/10.1016/j.heliyon.2020.e04642
  • [18] Van Der Westhuizen CJ, Stander A, Riley DL, Panayides JL. Discovery of novel acetylcholinesterase inhibitors by virtual screening, in vitro screening, and molecular dynamics simulations. J Chem Inf Model. 2022; 62(6):1550–1572. https://doi.org/10.1021/acs.jcim.1c01443
  • [19] Burianek LE, Soderling SH. Under lock and key: Spatiotemporal regulation of WASP family proteins coordinates separate dynamic cellular processes. Semin Cell Dev Biol. 2013; 24(4):258–266. https://doi.org/10.1016/j.semcdb.2012.12.005
  • [20] Parveen M, Ahmad F, Malla AM, Azaz S, Alam M, Basudan OA, Silva MR, Silva PSP. Acetylcholinesterase and cytotoxic activity of chemical constituents of Clutia lanceolata leaves and its molecular docking study. Nat Prod Bioprospect. 2016; 6:267–278. https://doi.org/10.1007/s13659-016-0110-x
  • [21] Ozarowski M, Mikolajczak PL, Bogacz A, Gryszczynska A, Kujawska M, Jodynis-Liebert J, Piasecka A, Napieczynska H, Szulc M, Kujawski R, Bartkowiak-Wieczorek J, Cichocka J, Bobkiewicz-Kozlowska T, Czerny B, Mrozikiewicz PM. Rosmarinus officinalis L. leaf extract improves memory impairment and affects acetylcholinesterase and butyrylcholinesterase activities in rat brain. Fitoterapia. 2013; 91:261–271. https://doi.org/10.1016/j.fitote.2013.09.012
  • [22] Chethana KR, Sasidhar BS, Naika M, Keri RS. Phytochemical composition of Caesalpinia crista extract as potential source for inhibiting cholinesterase and β-amyloid aggregation: Significance to Alzheimer’s disease. Asian Pac J Trop Biomed. 2018; 8(10):500–512. https://doi.org/10.4103/2221-1691.244159
  • [23] Krieger E, Vriend G. New ways to boost molecular dynamics simulations. J Comput Chem. 2015; 36(13):996–1007. https://doi.org/10.1002/jcc.23899
  • [24] Ravindranath PA, Forli S, Goodsell DS, Olson AJ, Sanner MF. AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput Biol. 2015; 11(12): e1004586 https://doi.org/10.1371/journal.pcbi.1004586
  • [25] Cannon EO, Amini A, Bender A, Sternberg MJE, Muggleton SH, Glen RC, Mitchell JBO. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des. 2007; 21:269–280. https://doi.org/1[0.1007/s10822-007-9113-3
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Farmasotik Kimya
Bölüm Articles
Yazarlar

Florentinus Dika Octa Riswanto 0000-0002-7174-6382

Stephanus Satria Wira Waskitha 0009-0002-8885-7422

Michael Resta Surya Yanuar 0009-0007-7000-1120

Enade Perdana Istyastono 0000-0002-8344-5587

Yayımlanma Tarihi 28 Haziran 2025
Yayımlandığı Sayı Yıl 2024 Cilt: 28 Sayı: 4

Kaynak Göster

APA Riswanto, F. D. O., Waskitha, S. S. W., Yanuar, M. R. S., Istyastono, E. P. (2025). Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors. Journal of Research in Pharmacy, 28(4), 1099-1106.
AMA Riswanto FDO, Waskitha SSW, Yanuar MRS, Istyastono EP. Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors. J. Res. Pharm. Temmuz 2025;28(4):1099-1106.
Chicago Riswanto, Florentinus Dika Octa, Stephanus Satria Wira Waskitha, Michael Resta Surya Yanuar, ve Enade Perdana Istyastono. “Structure-Based Virtual Screening on a New Open-Source Natural Products Database LOTUS to Discover Acetylcholinesterase ınhibitors”. Journal of Research in Pharmacy 28, sy. 4 (Temmuz 2025): 1099-1106.
EndNote Riswanto FDO, Waskitha SSW, Yanuar MRS, Istyastono EP (01 Temmuz 2025) Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors. Journal of Research in Pharmacy 28 4 1099–1106.
IEEE F. D. O. Riswanto, S. S. W. Waskitha, M. R. S. Yanuar, ve E. P. Istyastono, “Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors”, J. Res. Pharm., c. 28, sy. 4, ss. 1099–1106, 2025.
ISNAD Riswanto, Florentinus Dika Octa vd. “Structure-Based Virtual Screening on a New Open-Source Natural Products Database LOTUS to Discover Acetylcholinesterase ınhibitors”. Journal of Research in Pharmacy 28/4 (Temmuz 2025), 1099-1106.
JAMA Riswanto FDO, Waskitha SSW, Yanuar MRS, Istyastono EP. Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors. J. Res. Pharm. 2025;28:1099–1106.
MLA Riswanto, Florentinus Dika Octa vd. “Structure-Based Virtual Screening on a New Open-Source Natural Products Database LOTUS to Discover Acetylcholinesterase ınhibitors”. Journal of Research in Pharmacy, c. 28, sy. 4, 2025, ss. 1099-06.
Vancouver Riswanto FDO, Waskitha SSW, Yanuar MRS, Istyastono EP. Structure-based virtual screening on a new open-source natural products database LOTUS to discover acetylcholinesterase ınhibitors. J. Res. Pharm. 2025;28(4):1099-106.