Open Access Open Access  Restricted Access Subscription or Fee Access

Pf Phosphatidylserine decarboxylase: Molecular modeling and inhibithors prediction

Mamadou Sangare, Cheickna Cisse, Phillip Cruz, Oudou Diabate, Jeffrey G Shaffer, Jian Li, Seydou Doumbia, Mamadou WELE


Plasmodium falciparum (P.f) is a protozoan parasite responsible for the most severe and deadly form of malaria. The resistance of Pf to last resort antimalarial drugs has been reported, there is an urgent need to identify new therapeutic candidates for drug development. Advancements in bioinformatics technologies provide potential cost and time-effective solutions for predicting therapeutic candidates. Phosphatidylserine decarboxylase (PSD) is a member of the lyase family (more specifically, the carboxy-lyases), which cut carbon-to-carbon bonds. PSD catalyzes the decarboxylation of phosphatidylserine to generate phosphatidylethanolamine, which is a critical step in phospholipid metabolism in prokaryotes and eukaryotes. The model of PSD has not been previously characterized, but it is recognized as a structural pathway for the design of new potential inhibitors for developing future antimalarial drugs. Here we investigate and propose PSD as a promising new target for Pf and build his model to identify new potential inhibitors of this new therapeutic target. PSD was extracted from the Tropical Disease Research (TDR) Targets database, which facilitates the identification and prioritization of drugs and drug targets of neglected pathogens. The 3D structure of the target protein was predicted using the AlphaFold2 server and the ligands were extracted from the Zinc Database Chemical Library. Molecular docking was performed using Autodock-Vina. Ten PSD inhibitors were identified according to affinity docking score, which ranged from -8.5 to -8.3 kcal/mol and were consistent with the Lipinski rule of five. This study provides a promising building block for experimental studies in establishing novel antimalarial drugs.

Full Text:



« World malaria report 2022 ». Consulté le: 11 mars 2023. [En ligne]. Disponible sur:

Worl Health Organization, « World malaria report 2021 ». Consulté le: 3 août 2022. [En ligne]. Disponible sur:

J. D. Smith, J. A. Rowe, M. K. Higgins, et T. Lavstsen, « Malaria’s Deadly Grip: Cytoadhesion of Plasmodium falciparum Infected Erythrocytes », Cell. Microbiol., vol. 15, no 12, p. 10.1111/cmi.12183, déc. 2013, doi: 10.1111/cmi.12183.

S. Alonso et al., « The economic burden of malaria on households and the health system in a high transmission district of Mozambique », Malar. J., vol. 18, p. 360, nov. 2019, doi: 10.1186/s12936-019- 2995-4.

M. T. White, L. Conteh, R. Cibulskis, et A. C. Ghani, « Costs and cost-effectiveness of malaria control interventions - a systematic review », Malar. J., vol. 10, no 1, p. 337, nov. 2011, doi: 10.1186/1475-2875- 10-337.

B. Blasco, D. Leroy, et D. A. Fidock, « Antimalarial drug resistance: linking Plasmodium falciparum parasite biology to the clinic », Nat. Med., vol. 23, no 8, Art. no 8, août 2017, doi: 10.1038/nm.4381.

P. Jagannathan et A. Kakuru, « Malaria in 2022: Increasing challenges, cautious optimism », Nat. Commun., vol. 13, p. 2678, mai 2022, doi: 10.1038/s41467-022-30133-w.

J. Hughes, S. Rees, S. Kalindjian, et K. Philpott, « Principles of early drug discovery », Br. J. Pharmacol., vol. 162, no 6, p. 1239‑1249, mars 2011, doi: 10.1111/j.1476-5381.2010.01127.x.

M. Kontoyianni, « Docking and Virtual Screening in Drug Discovery », in Proteomics for Drug Discovery, vol. 1647, I. M. Lazar, M. Kontoyianni, et A. C. Lazar, Éd., in Methods in Molecular Biology, vol. 1647., New York, NY: Springer New York, 2017, p. 255‑266. doi: 10.1007/978-1-4939-7201-2_18.

B. Rasulev, « Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures », Handb. Comput. Chem., p. 2133‑2161, déc. 2016, doi: 10.1007/978-3-319-27282-5_54.

J. D. Durrant et J. A. McCammon, « Molecular dynamics simulations and drug discovery », BMC Biol., vol. 9, no 1, p. 71, oct. 2011, doi: 10.1186/1741-7007-9-71.

J. Lin, D. Sahakian, S. De Morais, J. Xu, R. Polzer, et S. Winter, « The Role of Absorption, Distribution, Metabolism, Excretion and Toxicity in Drug Discovery », Curr. Top. Med. Chem., vol. 3, no 10, p. 1125‑1154, mai 2003, doi: 10.2174/1568026033452096.

C. Acharya, A. Coop, J. E. Polli, et A. D. MacKerell, « Recent Advances in Ligand-Based Drug Design: Relevance and Utility of the Conformationally Sampled Pharmacophore Approach », Curr. Comput. Aided Drug Des., vol. 7, no 1, p. 10‑22, mars 2011.

M. Batool, B. Ahmad, et S. Choi, « A Structure-Based Drug Discovery Paradigm », Int. J. Mol. Sci., vol. 20, no 11, p. 2783, juin 2019, doi: 10.3390/ijms20112783.

J.-Y. Choi et al., « Characterization of Plasmodium phosphatidylserine decarboxylase expressed in yeast and application for inhibitor screening », Mol. Microbiol., vol. 99, no 6, p. 999‑1014, mars 2016, doi: 10.1111/mmi.13280.

A. R. Jensen, Y. Adams, et L. Hviid, « Cerebral Plasmodium falciparum malaria: The role of PfEMP1 in its pathogenesis and immunity, and PfEMP1‐based vaccines to prevent it », Immunol. Rev., vol. 293, no 1, p. 230‑252, janv. 2020, doi: 10.1111/imr.12807.

L. Urán Landaburu et al., « TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration », Nucleic Acids Res., p. gkz999, nov. 2019, doi: 10.1093/nar/gkz999.

The UniProt Consortium, « UniProt: the Universal Protein Knowledgebase in 2023 », Nucleic Acids Res., vol. 51, no D1, p. D523‑D531, janv. 2023, doi: 10.1093/nar/gkac1052.

E. M. Zdobnov, D. Kuznetsov, F. Tegenfeldt, M. Manni, M. Berkeley, et E. V. Kriventseva, « OrthoDB in 2020: evolutionary and functional annotations of orthologs », Nucleic Acids Res., vol. 49, no D1, p. D389‑D393, janv. 2021, doi: 10.1093/nar/gkaa1009.

A. Hernández-Plaza et al., « eggNOG 6.0: enabling comparative genomics across 12 535 organisms », Nucleic Acids Res., vol. 51, no D1, p. D389‑D394, janv. 2023, doi: 10.1093/nar/gkac1022.

M. Kanehisa, M. Furumichi, Y. Sato, M. Kawashima, et M. Ishiguro-Watanabe, « KEGG for taxonomy- based analysis of pathways and genomes », Nucleic Acids Res., vol. 51, no D1, p. D587‑D592, oct. 2022, doi: 10.1093/nar/gkac963.

K.-B. Li, « ClustalW-MPI: ClustalW analysis using distributed and parallel computing », Bioinformatics, vol. 19, no 12, p. 1585‑1586, août 2003, doi: 10.1093/bioinformatics/btg192.

L.-H. Hung et R. Samudrala, « Accelerated protein structure comparison using TM-score-GPU », Bioinformatics, vol. 28, no 16, p. 2191‑2192, août 2012, doi: 10.1093/bioinformatics/bts345.

M. Bhasin et G. P. S. Raghava, « ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST », Nucleic Acids Res., vol. 32, no Web Server issue, p. W414-419, juill. 2004, doi: 10.1093/nar/gkh350.

T. Hirokawa, S. Boon-Chieng, et S. Mitaku, « SOSUI: classification and secondary structure prediction system for membrane proteins », Bioinforma. Oxf. Engl., vol. 14, no 4, p. 378‑379, 1998, doi: 10.1093/bioinformatics/14.4.378.

N. Bordin et al., « AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms », Commun. Biol., vol. 6, no 1, Art. no 1, févr. 2023, doi: 10.1038/s42003-023-04488-9.

G. S. Couch, D. K. Hendrix, et T. E. Ferrin, « Nucleic acid visualization with UCSF Chimera », Nucleic Acids Res., vol. 34, no 4, p. e29, 2006, doi: 10.1093/nar/gnj031.

A. Warnecke, T. Sandalova, A. Achour, et R. A. Harris, « PyTMs: a useful PyMOL plugin for modeling common post-translational modifications », BMC Bioinformatics, vol. 15, no 1, p. 370, nov. 2014, doi: 10.1186/s12859-014-0370-6.

H. Rashid et al., Homology Modeling of Alpha-Glucosidase from Candida albicans: Sequence Analysis and Structural Validation Studies in silico. 2023. doi: 10.21577/0103-5053.20230123.

R. A. Laskowski, M. W. MacArthur, D. S. Moss, et J. M. Thornton, « PROCHECK: a program to check the stereochemical quality of protein structures », J. Appl. Crystallogr., vol. 26, no 2, p. 283‑291, avr. 1993, doi: 10.1107/S0021889892009944.

D. Eisenberg, R. Lüthy, et J. U. Bowie, « [20] VERIFY3D: Assessment of protein models with three- dimensional profiles », in Methods in Enzymology, vol. 277, in Macromolecular Crystallography Part B, vol. 277., Academic Press, 1997, p. 396‑404. doi: 10.1016/S0076-6879(97)77022-8.

C. Sharma, A. Nigam, et R. Singh, « Computational-approach understanding the structure-function prophecy of Fibrinolytic Protease RFEA1 from Bacillus cereus RSA1 », PeerJ, vol. 9, p. e11570, juin 2021, doi: 10.7717/peerj.11570.

G. M. Morris et al., « AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility », J. Comput. Chem., vol. 30, no 16, p. 2785‑2791, déc. 2009, doi: 10.1002/jcc.21256.

T. A. Halgren, « MMFF VII. Characterization of MMFF94, MMFF94s, and other widely available force fields for conformational energies and for intermolecular-interaction energies and geometries », J. Comput. Chem., vol. 20, no 7, p. 730‑748, mai 1999, doi: 10.1002/(SICI)1096- 987X(199905)20:7<730::AID-JCC8>3.0.CO;2-T.

N. M. O’Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, et G. R. Hutchison, « Open Babel: An open chemical toolbox », J. Cheminformatics, vol. 3, p. 33, oct. 2011, doi: 10.1186/1758-2946-3-33.

N. Baba et E. Akaho, « VSDK: Virtual screening of small molecules using AutoDock Vina on Windows platform », Bioinformation, vol. 6, no 10, p. 387‑388, août 2011, doi: 10.6026/97320630006387.

A. Daina, O. Michielin, et V. Zoete, « SwissADME: a free web tool to evaluate pharmacokinetics, drug- likeness and medicinal chemistry friendliness of small molecules », Sci. Rep., vol. 7, no 1, Art. no 1, mars 2017, doi: 10.1038/srep42717.

« Lipinski’s Rule of Five - an overview | ScienceDirect Topics ». Consulté le: 11 octobre 2023. [En ligne]. Disponible sur: pharmaceutical-science/lipinskis-rule-of-five

M. El fadili et al., « In-silico screening based on molecular simulations of 3,4-disubstituted pyrrolidine sulfonamides as selective and competitive GlyT1 inhibitors », Arab. J. Chem., vol. 16, no 10, p. 105105, oct. 2023, doi: 10.1016/j.arabjc.2023.105105.

L.-H. Hung et R. Samudrala, « Accelerated protein structure comparison using TM-score-GPU », Bioinformatics, vol. 28, no 16, p. 2191‑2192, août 2012, doi: 10.1093/bioinformatics/bts345.

L. Jendele, R. Krivak, P. Skoda, M. Novotny, et D. Hoksza, « PrankWeb: a web server for ligand binding site prediction and visualization », Nucleic Acids Res., vol. 47, no W1, p. W345‑W349, juill. 2019, doi: 10.1093/nar/gkz424.

N. Baba et E. Akaho, « VSDK: Virtual screening of small molecules using AutoDock Vina on Windows platform », Bioinformation, vol. 6, no 10, p. 387‑388, 2011, doi: 10.6026/97320630006387.

T. Sterling et J. J. Irwin, « ZINC 15--Ligand Discovery for Everyone », J. Chem. Inf. Model., vol. 55, no 11, p. 2324‑2337, nov. 2015, doi: 10.1021/acs.jcim.5b00559.

G. Ramakrishnan, N. Chandra, et N. Srinivasan, « Exploring anti-malarial potential of FDA approved drugs: an in silico approach », Malar. J., vol. 16, no 1, p. 290, juill. 2017, doi: 10.1186/s12936-017-1937- 2.

T. Rajguru, D. Bora, et M. K. Modi, « Identification of promising inhibitors for Plasmodium haemoglobinase Falcipain-2, using virtual screening, molecular docking, and MD Simulation », J. Mol. Struct., vol. 1248, p. 131427, janv. 2022, doi: 10.1016/j.molstruc.2021.131427.

R. Afolabi, S. Chinedu, Y. Ajamma, Y. Adam, R. Koenig, et E. Adebiyi, « Computational identification of Plasmodium falciparum RNA pseudouridylate synthase as a viable drug target, its physicochemical properties, 3D structure prediction and prediction of potential inhibitors », Infect. Genet. Evol., vol. 97, p. 105194, janv. 2022, doi: 10.1016/j.meegid.2021.105194.



  • There are currently no refbacks.