New Antibiotics Are Desperately Needed: Machine Learning could Help

As the threat of antibiotic resistance looms, microbiologists aren’t the only ones thinking up new solutions. James Zou, PhD, assistant professor of biomedical data science at Stanford, has applied machine learning to create an algorithm that generates thousands of entirely new virtual DNA sequences with the intent of one day creating antimicrobial proteins.

The algorithm, called Feedback GAN, essentially acts as a mass producer of different DNA snippets. And while these sequence attempts are somewhat random, the algorithm isn't working blindly. It’s basing the new possible peptides, or small groups of amino acids, on previous research that lays out the DNA sequences most likely to align with antimicrobial properties.

For now, these templates, which don't exist in nature, are theoretical, generated on a computer. But in the face of rising concerns about microbe resistance, Zou said it's critical to think about solutions that don't already exist.

"We chose to pursue antimicrobial proteins because it's a very important, high-impact problem that's also a relatively tractable problem for the algorithm," Zou said. "There are existing tools that we incorporate into our system that evaluate if a new sequence is likely to have the properties of a successful antimicrobial protein."

Feedback GAN builds on that, working to incorporate just the right balance of random chance and precision.

A paper describing the algorithm was published online Feb. 11 in Nature Machine Learning. Anvita Gupta, a student in computer science, is the first author; Zou is the senior author.

Self-refining

Gupta and Zou's algorithm doesn't just churn out new combinations of DNA. It also actively refines itself, learning what works and what doesn’t through a feedback loop: After the algorithm spits out a wide range of DNA sequences, it runs a trial-and-error learning process that sifts through the peptide suggestions. Based on their resemblance to other known antimicrobial peptides, the “good” ones get fed back into the algorithm to inform future DNA sequences generated from the code, and to get refined themselves.

"There's a built-in arbiter and, by having this feedback loop, the system learns to model newly generated sequences after those that are deemed likely to have antimicrobial properties," Zou said. "So the idea is both individual peptide sequences and the generation of the sequences get better and better."

Zou has also considered another core component of hypothetical proteins: protein folding. Proteins contort into very specific structures linked to their functions. An algorithm could create the perfect sequence, but unless it can fold up, it's useless - like the cogs of a clock strewn on a table.

Zou can tweak the algorithm so that instead of analyzing a propensity for antimicrobial properties, it determines the likelihood of correct folding.

"We can actually do these two things in parallel where we look at antimicrobial properties of one sequence and folding likelihood of another," said Zou. "We run both so that we’re optimizing either the antimicrobial properties or its ability to fold."

Next, Zou hopes to merge the two variations of the algorithm to create peptide sequences that are optimized for both their microbe-killing abilities and their ability to fold into a genuine protein.

Demo, instructions and code for FBGAN are available at https://github.com/av1659/fbgan.

Anvita Gupta, James Zou.
Feedback GAN for DNA optimizes protein functions.
Nature Machine Intelligence, 1, 105-111 (2019). doi: 10.1038/s42256-019-0017-4.

Most Popular Now

Artificial Intelligence Solution Improve…

Clinical trials are a critical tool for getting new treatments to people who need them, but research shows that difficulty finding the right volunteer subjects can undermine the effectiveness of...

Cardio-Respiratory Synchronization may R…

Researchers from the School of Engineering at the University of Warwick have managed to expand the knowledge of the cardio-respiratory system after conducting an experiment measuring heart rate during fast-paced...

South West London Pathology Picks CliniS…

One of the first pathology networks in the country, set up to serve more than two million people in south west London, has signed a contract with CliniSys for a...

AI-based AI-Rad Companion Chest CT Softw…

AI-Rad Companion Chest CT(1), an intelligent software assistant for radiology, was recently awarded the CE mark, which means Siemens Healthineers can start marketing this artificial intelligence (AI)-based software as a...

Spot On for Healthcare Technology Startu…

10 - 12 October 2019, Berlin, Germany. XPOMET Medicinale brings together care providers, patients, and in general stakeholders from all health-related fields and geographic regions. The Festival of Future Medicine and...

Call for Tenders: Studies on eHealth, In…

The European Commission is launching a tender for two studies to survey and analyse progress on the digital transformation of the health and care in the EU, in particular with...

7th MEDICA MEDICINE + SPORTS CONFERENCE

18 - 21 November 2019, Düsseldorf, Germany. What lengths do top athletes go to in order to reach peak performances and which findings in the field of professional sports are relevant...

Carestream Health Completes Sale of Heal…

Carestream Healthhas completed the sale of the company's healthcare information solutions business to Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, in 26 of the 38...

How can We Successfully Converge the Hea…

Opinion Article by Erik Janssen, VP, Innovative Solutions, Neurology, UCB Pharma. The fourth industrial revolution is upon us and fundamentally changing the way we live, work and interact across all industries...

Isansys Named as Finalist for OBN's Most…

Isansys Lifecare is proud to announce it has been shortlisted in the Most Transformative Digital Healthcare Company category at the OBN Annual Awards 2019. The award recognises the significant uptake...