Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.