Contributions

Preprints or Under Review

  1. López-Olmos, O.D.; Aguilera-Puga, M. d. C. & Plisson, F.*. Structure-agnostic machine learning-guided discovery of antimicrobial peptides. 2025 [to be announced]
    2.  
  2. Osuna-Osuna, J.G.; Aguilar, S.; Marani, M. M.; Mancera Ramos, E.; Plisson, F.*. Machine learning uncovers novel antifungal peptides in hymenopteran venom. 2025 [to be announced]
    3.  
  3. Derguini, F.; Marcadet, F.; Plisson, F.; Rahmani, R.; Lavigne, F.; Menendez, C.; Long, C.; Marcourt, L. & Massiot, G.*. Tagitinin C and derivatives, as proteasome inhibitors. 2024 [to be announced]


Book Chapters
  1. Aguilera-Puga, M. d. C.; Cancelarich, N. L.; Marani, M. M.; de la Fuente-Nuñez, C.* & Plisson, F.*. Accelerating the discovery and design of antimicrobial peptides with artificial intelligence. Methods in Molecular Biology 2024; 2714 : 329-352. [DOI]


Journal Articles
  1. Aguilera-Puga, M. d. C. & Plisson, F.*. Structure-aware machine learning strategies for antimicrobial peptide discovery. 2024; 14 : 11995. [Scientific Reports] [Research Square] [Github]
    Our study is the first assessment of structural bias in model datasets and of the structural effects upon machine learning-guided peptide bioactivity prediction.
    2.  
  2. Robles-Ramirez, O.; Osuna, J. G.; Plisson, F.; Barrientos-Salcedo; C.*. Antimicrobial Peptides in Livestock: A Review with a One Health Approach. Frontiers in Cellular and Infection Microbiology 2024; vol. 14. [DOI]
    3.  
  3. López-López, E.; Robles, O.; Plisson, F.; Medina-Franco, J. L.*. Mapping the structure-activity landscape of non-canonical peptides with MAP4 fingerprinting. Digital Discovery 2023, 2, 1494-1505. [DOI] [ChemXriv]
    4.  
  4. Aldas-Bulos, V. D. & Plisson, F.*. Benchmarking protein structure predictors to assist machine learning-guided peptide discovery. Digital Discovery 2023, 2, 981-993. [DOI] [ChemXriv] [Github 1] [Github 2]
    A fast and robust method to assess structural bias in medium-large protein datasets before machine learning modeling.
    5.  
  5. Martínez-Hernández, C.; Aguilera-Puga, M. d. C. & Plisson, F.*. Deconstructing the potency and cell-line selectivity of membranolytic anticancer peptides. ChemBioChem 2023, 24(14):e202300058. [DOI] [ChemXriv]
    Membrane-active anticancer peptides can respond similarly (potency and selectivity) towards cell lines from different tissues.
    6.  
  6. Dennis, P. G.*; Kukulies, T.; Forstner, C.; Plisson, F.; Eaglesham, G.; Pattison, A. B. The Effects of Atrazine, Diuron, Fluazifop-P-butyl, Haloxyfop-P-methyl, and Pendimethalin on Soil Microbial Activity and Diversity. Applied Microbiology 2023, 3, 79-89. [DOI]
    7.  
  7. Plisson, F. Overcoming the Challenges in Machine Learning-Guided Antimicrobial Peptide Design. Proceedings of the 36th European and the 12th International Peptide Symposium 2022, 207-210. ISBN: 9798987214008. [PDF]
    8.  
  8. Guido-Patiño, J. C. & Plisson, F.*. Profiling hymenopteran venom toxins: protein families, structural landscape, biological activities, and pharmacological benefits. Toxicon:X 2022, 14, 100119. [DOI]
    9.  
  9. Bajorath, J.; Chávez-Hernández, A. L.; Duran-Frigola, M.; Fernández-de Gortari, E.; Gasteiger, J.; López-López, E.; Maggiora, G. M.; Medina-Franco, J. L.*; Méndez-Lucio, O.; Mestres, J.; Miranda-Quintana, R. A.; Oprea, T. I.; Plisson, F.; Prieto-Martínez, F. D.; Rodríguez-Pérez, R.; Rondón-Villarreal, P.; Saldívar-Gonzalez, F. I.; Sánchez-Cruz, N.; Valli, M. Chemoinformatics and artificial intelligence colloquium: progress and challenges to develop bioactive compounds. Journal of Chemoinformatics 2022, 14(1), 82. [DOI]
    10.  
  10. Robles-Loaiza, A. A.; Pinos-Tamayo, E. A.; Mendes, B.; Ortega-Pila, J. A.; Proaño-Bolaños, C.; Plisson, F.; Teixeira, C.; Gomes, P.; Almeida, J. R.*. Profiling hymenopteran venom toxins: protein families, structural landscape, biological activities, and pharmacological benefits. Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. Pharmaceuticals 2022, 15, 323. [DOI]
    11.  
  11. Saldivar-González, F. I.; Aldas-Bulos, V. D.; Medina-Franco, J. L. & Plisson, F.*. Natural product drug discovery in the artificial intelligence era. Chemical Science 2022, 13, 1526-1546. [DOI]
    Seminal and the most comprehensive review of artificial intelligence technologies (machine learning and natural language processing) applied to natural product drug discovery and molecular design.
    12.  
  12. Barona-Gómez, F.#; Delaye L.#; Díaz-Valenzuela, E.#; Plisson, F.#; Cruz-Pérez, A.; Díaz-Sánchez, M.; García-Sepúlveda, C. A.; Sanchez-Flores, A.; Pérez-Abreu, R.; Valencia-Valdespino, F. J.; Vega-Magaña, N.; Muñoz-Valle, J. F.; García-González, O. P.; Bernal-Silva, S.; Comas-García, A.; Cibrián-Jaramillo, A.*. Phylogenomics and population genomics of SARS-CoV-2 in Mexico during the pre-vaccination stage reveals variants of interest B.1.1.28.4, B.1.1.222, or B.1.1.519 and the nucleocapsid mutation S194L associated with symptoms. Microbial Genomics 2021, Nov;7(11):000684. PMID: 34846283 [PMCID: PMC8743546] [DOI] [medRxiv] [Github]
    Part of the genomic surveillance program of SARS-CoV-2 variants in Mexico. # Co-first authors.
    13.  
  13. González-Castro, R.; Gómez-Lim, M. A. & Plisson, F.*. Cysteine-rich peptides: hyperstable scaffolds for protein engineering. ChemBioChem 2020, 22(6), 961-973. [DOI] [Cover Feature]
    14.  
  14. Plisson, F.*; Ramírez-Sánchez, O. & Martínez-Hernández, C. Machine learning-guided discovery and design of non-hemolytic peptides. Scientific Reports 2020, 10, 16581. [DOI] [Github]
    The first study to report the domain of applicability of peptide predictive models and its limitations using multivariate outlier detectors.
    15.  
  15. Plisson, F.* & Piggott, A. M. Predicting blood-brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders. Marine Drugs 2019, 17, 81. [DOI] [Github]
    One of the earliest studies to develop machine learning predictive models applied to (marine) natural products.
    16.  
  16. Plisson, F.#; Hill, T. A.#; Mitchell, J. M.; Hoang, H. N.; de Araujo, A. D.; Xu, W. J.; Cotterell, A.; Edmonds, D. J.; Stanton, R. V.; Derksen, D. R.; Loria, P. M.; Griffith, D. A.; Price, D. A.; Liras, S. & Fairlie, D. P.*. Helix-constraints and amino acid substitution in GLP-1 increase cAMP and insulin secretion but not β-arrestin 2 signalling European Journal of Medicinal Chemistry 2017, 127, 703-714. [DOI]
    # Co-first authors.
    17.  
  17. Hoang, H. N.; Driver, R.W.; Beyer, R.L.; Hill, T.A.; de Araujo, A.D.; Plisson, F.; Harrison, R.S.; Goedecke, L.; Shepherd, N. E. & Fairlie, D. P.*. Helix Nucleation by the Smallest Known α-Helix in Water. Angewandte Chemie International Edition 2016, 55(29), 8275-8279. [DOI]
    18.  
  18. Plisson, F.; Prasad P.; Xiao, X.; Piggott, A. M.; Huang, X.-C.; Khalil, Z. & Capon, R. J.*. Callyspongisines A-D: bromopyrrole alkaloids from an Australian marine sponge, Callyspongia sp.. Organic & Biomolecular Chemistry 2014, 12, 1579-1584. [DOI]
    19.  
  19. Plisson, F.; Conte, M.; Khalil, Z.; Huang, X.-C.; Piggott A. M. & Capon, R. J.*. Kinase inhibitor scaffolds against neurodegenerative disorders from a southern Australian ascidian, Didemnum sp.. ChemMedChem 2012, 7(6), 980-993. [DOI]
    First example of executing molecular docking experiments to understand the mechanism(s) of action of natural products.
    20.  
  20. Plisson, F.; Huang, X.-C.; Khalil, Z.; Zhang, H. & Capon, R. J.*. Lamellarins as substrates and inhibitors of P-glycoprotein-mediated multidrug resistance in a human colon cancer cell line. Chemistry – An Asian Journal 2012, 7(7), 1616-1623. [DOI]
    21.  
  21. Zhang, H.; Khalil, Z.; Conte, M. M.; Plisson, F. & Capon, R. J.*. A search for kinase inhibitors and antibacterial agents: Bromopyrrolo-2-aminoimidazoles from a deep-water Great Australian Bight sponge, Axinella sp. Tetrahedron Letters 2012, 53(29), 3784-3787. [DOI]
    22.  
  22. Long, C.; Beck, J.; Cantagrel, F.; Marcourt, L.; Vendier, L.; David, B.; Plisson, F.; Derguini, F.; Vandenberghe, I.; Aussagues, Y.; Ausseil, F.; Lavaud, C.; Sautel, F. & Massiot, G.*. Proteasome inhibitors from Neoboutonia melleri. Journal of Natural Products 2012, 75 (1), 34-47. [DOI]
    23.  
  23. Beck, J.; Guminski, Y.; Long, C.; Marcourt, L.; Derguini, F.; Plisson, F.; Grondin, A.; Vandenberghe, I.; Vispé, S.; Brel, V.; Aussagues, Y.; Ausseil, F.; Arimondo, P. B.; Massiot, G.; Sautel, F. & Cantagrel, F.*. Semisynthetic Neoboutomellerone derivatives as ubiquitin-proteasome pathway inhibitors. Bioorganic and Medicinal Chemistry 2012, 20 (2), 819-831. [DOI]
    24.  
  24. Ajala, O. S.; Piggott, A. M.; Plisson, F.; Khalil, Z.; Huang, X.-C.; Adesegun, S. A.; Coker, H. A. B. & Capon, R. J.*. Ikirydinium A: a new indole alkaloid from the seeds of Hunteria umbellata. Tetrahedron Letters 2011, 52, 7125-7127. [DOI]


Patents
  1. Derguini, F.; Plisson, F.; Massiot G. Preparation of tagitinin C and F derivatives as anti-cancer agents.
    Patent application FR 2941697 A1 20100806 2010. [Europe PMC] [Google Patents]