Periodic Reporting for period 1 - SPACE4AMPS (Chemical Space for Antimicrobials on a Peptide Basis)
Berichtszeitraum: 2021-01-01 bis 2022-06-30
Multidrug resistant (MDR) bacteria represent a global public health threat against which new drugs are urgently needed, however research towards new antibiotics is almost non-existent. Solving the antibiotics crisis is critical for society because MDR bacteria are becoming one of the main causes of death in hospitals.
Antimicrobial peptides (AMPs), mostly derived from naturally occurring linear or cyclic peptides, can contribute to solving the problem, however AMPS with optimal activity and toxicity profiles are difficult to identify. The objective of SPACE4AMPS is to develop new computational tools to explore chemical space in search for novel AMPs with optimal properties, and to synthesize and test these new AMPs to identify new antibiotics for clinical development. The impact of SPACE4AMPS is not only to discover new antibiotics, but also to develop and test new methods for exploring chemical space than can make drug discovery faster and easier.
Antimicrobial peptides (AMPs), mostly derived from naturally occurring linear or cyclic peptides, can contribute to solving the problem, however AMPS with optimal activity and toxicity profiles are difficult to identify. The objective of SPACE4AMPS is to develop new computational tools to explore chemical space in search for novel AMPs with optimal properties, and to synthesize and test these new AMPs to identify new antibiotics for clinical development. The impact of SPACE4AMPS is not only to discover new antibiotics, but also to develop and test new methods for exploring chemical space than can make drug discovery faster and easier.
We completed the initial computational study aiming at developing a molecular fingerprint suitable for large natural products and peptides to handle the chemical space analysis of AMP-like molecules. As a proof-of-concept, we applied that fingerprint to map the chemical space of natural products (NPs). We discovered that our molecular fingerprint, named MAP4, was able to classify NPs by their origin (plant, bacterial or fungal) and to assign the origin of newly discovered NPs.(1)
In view of also exploiting machine learning in our approach to new antimicrobials, we have performed a discovery study for non-hemolytic antimicrobial peptides (AMPs) relying on recurrent neural networks (RNN) as generative models and as activity classifiers and were able to readily identify new non-hemolytic AMPs.(2) We have also extended this methodology and compared it to an approach based on the MAP4 fingerprint using a genetic algorithm that is central to SPACE4AMPs methodology to investigate non-hemolytic anticancer peptides, providing a useful feasibility study for the method.(3)
Since SPACE4AMPS aims at NP analogs, we investigated the activity profile of the clinical NP polymyxin B, which we use as one of our reference compounds, and discovered a previously unknown effect of pH on its activity.(4) Furthermore, we have completed a study of bicyclic AMPs that have the unusual property of containing both D- and L-residues, which we think can be a central aspect of our development strategy for new AMPs.(5)
Experiments are ongoing regarding establishing advanced biological profiling of our new antimicrobials in the Galleria Mellonella test system and with a range of human cell lines in vitro, which will allow us to identify the most promising compounds. We are using these methods on several antimicrobials recently discovered in the laboratory, and results will be reported in the near future.
References:
(1) Capecchi, A.; Reymond, J.-L. Classifying Natural Products from Plants, Fungi or Bacteria Using the COCONUT Database and Machine Learning. Journal of Cheminformatics 2021, 13 (1), 82. https://doi.org/10.1186/s13321-021-00559-3.
(2) Capecchi, A.; Cai, X.; Personne, H.; Köhler, T.; Delden, C. van; Reymond, J.-L. Machine Learning Designs Non-Hemolytic Antimicrobial Peptides. Chem. Sci. 2021, 12 (26), 9221–9232. https://doi.org/10.1039/D1SC01713F.
(3) Zakharova, E.; Orsi, M.; Capecchi, A.; Reymond, J.-L. Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides. ChemMedChem 2022, e202200291. https://doi.org/10.1002/cmdc.202200291.
(4) Cai, X.; Javor, S.; Gan, B. H.; Köhler, T.; Reymond, J.-L. The Antibacterial Activity of Peptide Dendrimers and Polymyxin B Increases Sharply above PH 7.4. Chem. Commun. 2021, 57 (46), 5654–5657. https://doi.org/10.1039/D1CC01838H.
(5) Baeriswyl, S.; Personne, H.; Bonaventura, I. D.; Köhler, T.; Delden, C. van; Stocker, A.; Javor, S.; Reymond, J.-L. A Mixed Chirality α-Helix in a Stapled Bicyclic and a Linear Antimicrobial Peptide Revealed by X-Ray Crystallography. RSC Chem. Biol. 2021, 2, 1608–1617. https://doi.org/10.1039/D1CB00124H.
In view of also exploiting machine learning in our approach to new antimicrobials, we have performed a discovery study for non-hemolytic antimicrobial peptides (AMPs) relying on recurrent neural networks (RNN) as generative models and as activity classifiers and were able to readily identify new non-hemolytic AMPs.(2) We have also extended this methodology and compared it to an approach based on the MAP4 fingerprint using a genetic algorithm that is central to SPACE4AMPs methodology to investigate non-hemolytic anticancer peptides, providing a useful feasibility study for the method.(3)
Since SPACE4AMPS aims at NP analogs, we investigated the activity profile of the clinical NP polymyxin B, which we use as one of our reference compounds, and discovered a previously unknown effect of pH on its activity.(4) Furthermore, we have completed a study of bicyclic AMPs that have the unusual property of containing both D- and L-residues, which we think can be a central aspect of our development strategy for new AMPs.(5)
Experiments are ongoing regarding establishing advanced biological profiling of our new antimicrobials in the Galleria Mellonella test system and with a range of human cell lines in vitro, which will allow us to identify the most promising compounds. We are using these methods on several antimicrobials recently discovered in the laboratory, and results will be reported in the near future.
References:
(1) Capecchi, A.; Reymond, J.-L. Classifying Natural Products from Plants, Fungi or Bacteria Using the COCONUT Database and Machine Learning. Journal of Cheminformatics 2021, 13 (1), 82. https://doi.org/10.1186/s13321-021-00559-3.
(2) Capecchi, A.; Cai, X.; Personne, H.; Köhler, T.; Delden, C. van; Reymond, J.-L. Machine Learning Designs Non-Hemolytic Antimicrobial Peptides. Chem. Sci. 2021, 12 (26), 9221–9232. https://doi.org/10.1039/D1SC01713F.
(3) Zakharova, E.; Orsi, M.; Capecchi, A.; Reymond, J.-L. Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides. ChemMedChem 2022, e202200291. https://doi.org/10.1002/cmdc.202200291.
(4) Cai, X.; Javor, S.; Gan, B. H.; Köhler, T.; Reymond, J.-L. The Antibacterial Activity of Peptide Dendrimers and Polymyxin B Increases Sharply above PH 7.4. Chem. Commun. 2021, 57 (46), 5654–5657. https://doi.org/10.1039/D1CC01838H.
(5) Baeriswyl, S.; Personne, H.; Bonaventura, I. D.; Köhler, T.; Delden, C. van; Stocker, A.; Javor, S.; Reymond, J.-L. A Mixed Chirality α-Helix in a Stapled Bicyclic and a Linear Antimicrobial Peptide Revealed by X-Ray Crystallography. RSC Chem. Biol. 2021, 2, 1608–1617. https://doi.org/10.1039/D1CB00124H.
We are currently investigating mixed peptide/peptoids designed by a chemical space guided approach incorporating L-, D- and stereroandomized residues, with various chain topologies (dendritic, cyclic and linear). These unprecedented molecules are proving surprisingly active as antimicrobials, stable against serum degradation, and non-toxic, while their chemical synthesis remains simple and therefore scalable. We anticipate that our design/synthesis/screening efforts will result in one or several novel antimicrobials with promising properties for clinical development.