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Production of Pharmaceuticals01:30

Production of Pharmaceuticals

Industrial insulin production uses genetically engineered E. coli expressing a proinsulin gene controlled by a tryptophan promoter and containing a methionine linker for later cleavage. The cells also carry ampicillin resistance for selective growth. Seed cultures are stored at −80 °C and production begins by thawing a small amount to inoculate starter cultures, which are progressively scaled to a 50,000-L bioreactor. In the bioreactor, E. coli grow in nutrient-rich media under sterile, tightly...
Production of Antibiotics01:27

Production of Antibiotics

Penicillin, one of the earliest and most widely used antibiotics, is produced industrially by the filamentous fungus Penicillium chrysogenum. Large stirred-tank bioreactors ranging from tens to hundreds of thousands of liters maintain tightly controlled temperature, pH, and dissolved oxygen conditions to support fungal metabolism and maximize antibiotic yield. Penicillin is a secondary metabolite, synthesized primarily during the stationary growth phase, which requires a carefully managed...
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Inhibitors of Gram-positive Cell Wall Synthesis01:23

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Related Experiment Video

Updated: May 29, 2026

Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids
11:56

Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

Published on: May 4, 2018

A generative artificial intelligence approach for peptide antibiotic optimization.

Marcelo D T Torres1,2,3,4, Yimeng Zeng5, Fangping Wan1,2,3,4

  • 1Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.

Nature Machine Intelligence
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence tool ApexGO optimizes peptide scaffolds to discover new antibiotics. This method enhances antimicrobial properties and shows potent activity against resistant bacteria in preclinical models.

Keywords:
Computational biology and bioinformaticsMicrobiology

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Last Updated: May 29, 2026

Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids
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Published on: May 4, 2018

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10:35

Production and Testing of Antimicrobial Peptides and Their Mimics

Published on: April 10, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Antibiotic resistance is a growing global health crisis, necessitating novel antimicrobial agents.
  • Current artificial intelligence (AI) methods for drug discovery often focus on screening large libraries or broad generation, not optimizing existing peptide structures under specific constraints.

Purpose of the Study:

  • To introduce ApexGO, a novel generative AI approach for optimizing peptide scaffolds to accelerate antibiotic discovery.
  • To demonstrate ApexGO's capability in designing potent antimicrobial peptides by modifying existing templates.

Main Methods:

  • Utilized a transformer variational autoencoder to embed peptide sequences in a latent space.
  • Employed Bayesian optimization to efficiently propose sequence modifications for enhanced antimicrobial potency.
  • Generated and synthesized optimized peptide derivatives from ten template peptides.

Main Results:

  • ApexGO achieved an 85% experimental hit rate and a 72% success rate in enhancing antimicrobial activity against Gram-negative pathogens.
  • Synthesized 100 compounds, with optimized derivatives showing improved antimicrobial properties, mechanism of action, and cytotoxicity profiles.
  • AI-optimized molecules demonstrated potent anti-infective activity in preclinical models of *Acinetobacter baumannii* infection, outperforming controls.

Conclusions:

  • ApexGO represents a significant advancement in generative AI for peptide design and antibiotic optimization.
  • The approach offers a powerful tool to accelerate the discovery and development of new antimicrobial therapies.
  • ApexGO shows potential for overcoming the challenges posed by rising antibiotic resistance.