In 1928, Alexander Fleming discovered a mold that had contaminated his bacteria’s cultures and where the mold grew the bacteria was destroyed. This serendipitous event lead to the discovery of penicillin, the first antibiotic discovered. Since then, several new compounds have been discovered with antibiotic activity. Unfortunately, the discovery of new antibiotic compounds in nature hasn’t led to new significant discoveries in the last years. This could be due to the fact that we have discovered all the compounds nature had available or that we are not looking for them in the right place.

This is a big problem because some bacteria are becoming drug-resistant. This means that the antibiotics known up-to-date are not effective against them. This problem is a consequence of the nature of the problem itself. The incorrect use of antibiotics, like stopping the treatment with antibiotics before the infection is destroyed or using them as a preventive method against infection in situations where it is not strictly needed, allowed the bacteria to evolve and develop their immunity to drugs. This led to Center for Disease Control and Prevention (the American CDC) to publish a report in 2013 where it sounded the alarm considering this issue “one of the biggest public health challenges of our time”.

Therefore, a new approach is needed to tackle this problem. A possible solution is the use of peptides. Peptides are chains of amino acids, they are the base of proteins and enzymes, and they are synthesized by all living beings. It was discovered that some of these peptides present antibiotic activity, so what if instead of trying to search for more active peptides in nature, we force them or direct them to evolve? And what if instead of doing it in vitro we do it in silico?

What this means is that some scientists are focused on digitalizing peptides existing in nature to accelerate the discovery process. The natural occurring peptides are processed in a logic mathematical way to transform all their chemical information into what is called descriptors. Then the search is directed towards a peptide with an optimal set of descriptors for an efficient activity against bacteria. Furthermore, we use the powers of computers to create derivate peptides of which the antibacterial activity is predicted by the computer too.

One of the algorithms used by the researchers are genetic algorithms. These algorithms use an initial population of peptides and make them follow Darwin’s evolutionary principles. They are mutated and then the “fittest” ones are selected to be recombined and generate a new population of peptides that contain random mutations and traits inherited from the peptides in the previous generation.

These algorithms are combined with machine and deep learning ones, a new area of research in computer science that has applications in many fields including this one. They are used for the design of the peptides and also for predicting their properties. This has led to the discovery of peptides that were 160 times more active than the natural peptide.

On top of helping in the search for a medicine to fight drug-resistant bacteria, the new peptides can be used to target different kinds of bacteria in the gut microbiota. The gut microbiota is the group of different microorganisms that live in our digestive system. For a long time, there has been discussion about the implications that our gut’s health has on our mood. But, during the last years, several studies have focused on studying the bi-directional communication between our gut and our brain, in what is called the gut-brain axis. These studies have related the gut microbiota with behaviors and symptoms typical of disorders in the autism spectrum and others linked it to diseases such as Parkinson and Alzheimer.

The newly peptides can target specific bacterias to destroy them

In conclusion, there is no doubt that this is an interesting topic for research. It involves several disciplines like biology, computational science, chemistry, and neurosciences. The rapid progress of new artificial intelligence technologies and cheaper computational resources will accelerate the rate at which discoveries are found. Hopefully these discoveries will lead to solutions to fight against drug-resistant infections and perhaps generate new therapies for neurological disorders.

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Sources:

https://en.wikipedia.org/wiki/Alexander_Fleming

https://msystems.asm.org/content/4/3/e00151-19

https://www.cdc.gov/drugresistance/biggest_threats.html

https://en.wikipedia.org/wiki/In_silico

https://www.sciencedirect.com/science/article/pii/S0022283618312890

https://en.wikipedia.org/wiki/Molecular_descriptor

https://www.sciencedirect.com/science/article/abs/pii/S1369527419300050

https://en.wikipedia.org/wiki/Genetic_algorithm

https://en.wikipedia.org/wiki/Machine_learning

https://en.wikipedia.org/wiki/Human_gastrointestinal_microbiota

https://www.nature.com/articles/d42859-019-00005-3