RESUMEN
Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.
RESUMEN
At the beginning of 2020, the pandemic caused by the SARS-CoV-2 virus led to the fast sequencing of its genome to facilitate molecular engineering strategies to control the pathogen's spread. The spike (S) glycoprotein has been identified as the leading therapeutic agent due to its role in localizing the ACE2 receptor in the host's pulmonary cell membrane, binding, and eventually infecting the cells. Due to the difficulty of delivering bioactive molecules to the intracellular space, we hypothesized that the S protein could serve as a source of membrane translocating peptides. AHB-1, AHB-2, and AHB-3 peptides were identified and analyzed on a membrane model of DPPC (dipalmitoylphosphatidylcholine) using molecular dynamics (MD) simulations. An umbrella sampling approach was used to quantify the energy barrier necessary to cross the boundary (13.2 to 34.9 kcal/mol), and a flat-bottom pulling helped to gain a deeper understanding of the membrane's permeation dynamics. Our studies revealed that the novel peptide AHB-1 exhibited comparable penetration potential of already known potent cell-penetrating peptides (CPPs) such as TP2, Buforin II, and Frenatin 2.3s. Results were confirmed by in vitro analysis of the peptides conjugated to chitosan nanoparticles, demonstrating its ability to reach the cytosol and escape endosomes, while maintaining high biocompatibility levels according to standardized assays.
RESUMEN
One of the challenges of modern biotechnology is to find new routes to mitigate the resistance to conventional antibiotics. Antimicrobial peptides (AMPs) are an alternative type of biomolecules, naturally present in a wide variety of organisms, with the capacity to overcome the current microorganism resistance threat. Here, we reviewed our recent efforts to develop a new library of non-rationally produced AMPs that relies on bacterial genome inherent diversity and compared it with rationally designed libraries. Our approach is based on a four-stage workflow process that incorporates the interplay of recent developments in four major emerging technologies: artificial intelligence, molecular dynamics, surface-display in microorganisms, and microfluidics. Implementing this framework is challenging because to obtain reliable results, the in silico algorithms to search for candidate AMPs need to overcome issues of the state-of-the-art approaches that limit the possibilities for multi-space data distribution analyses in extremely large databases. We expect to tackle this challenge by using a recently developed classification algorithm based on deep learning models that rely on convolutional layers and gated recurrent units. This will be complemented by carefully tailored molecular dynamics simulations to elucidate specific interactions with lipid bilayers. Candidate AMPs will be recombinantly-expressed on the surface of microorganisms for further screening via different droplet-based microfluidic-based strategies to identify AMPs with the desired lytic abilities. We believe that the proposed approach opens opportunities for searching and screening bioactive peptides for other applications.