Deep Learning Model Discovers Antibiotic Drugs in Extinct Organisms Effective Against Drug-Resistant Superbugs

Antibiotic Peptide de-extinction (APEX) is a deep-learning AI solution that can unearth ancient antibiotics to effectively combat superbugs and tackle antibiotic resistance on a global scale.
Problem:
Escalating antibiotic resistance around the globe has emerged as one of the pressing problems of our time, with antibiotic-resistant infections causing approximately 1.27 million deaths annually worldwide. Without effective new drugs, this number could surge to 10 million fatalities by 2050. Traditional drug discovery approaches have been unable to keep pace with the evolution of antibiotic-resistant bacterial strains, resulting in a critical shortage of novel antibiotics to combat these resistant pathogens. These developments can seriously jeopardize public health in the future.
Solution:
Researchers have developed a deep learning AI technology that analyzes a protein database of long-extinct organisms to rediscover lost, never-before-used antibiotic compounds, which display considerable efficacy against pathogens resistant to more traditional antibiotics.
Technology:
In their experimentation, the researchers employed the Antibiotic Peptide de-extinction (APEX) approach, which relies on deep learning techniques. APEX systematically scanned proteomes of extinct organisms, encompassing various species across different time periods. A multitask deep learning model, trained on both public and in-house peptide sequences from natural and synthetic sources. The peptide sequences generated by APEX range from 8 to 50 residues in length, and predicted antibiotic activity against the most relevant pathogenic strains. Based on predicted antibiotic properties, the highest-ranking peptides underwent extensive in vitro characterization, including assessing antibiotic activity, mechanism of action, secondary structure, synergy, and cytotoxicity. Testing the selected peptides using in vivo mouse models validated and showcased the peptides’ potential antibacterial effectiveness.
Advantages:

Automates the process of finding new antibiotic peptides essential to counter antibiotic-resistant pathogens.
The deep learning approach allows large databases of ancient proteomes to be mined for relevant molecules.
Encrypted peptides discovered reduce bacterial load in a mouse model by 2-3 orders of magnitude.
The encrypted peptides operate via new pathways that circumvent the drug resistance of superbugs.
Discovered peptides can be used as general antibiotics in non-specialised applications.

Stage of Development:

Proof of Concept

APEX mines available proteomes of extinct organisms. A multitask deep learning model trained on public and in-house peptide data evaluates amino acid sequences for potential antibiotic activity. The highest-ranked peptides were thoroughly characterized against clinically relevant pathogens in vitro and animal models and the resulting proteins were experimentally validated in mouse models.
Intellectual Property: