The complex multicellular communities of bacteria are held together by self-produced polymers, forming a bacterial biofilm. The ability of bacteria to form biofilms on a variety of substrates, from tissues to medical devices, and their significant resistance to almost all common antibiotics make bacterial biofilms a critical challenge in healthcare systems. Developing new therapeutic agents with specific properties to perform well in the biofilm environment should be sought proactively. Peptides have promising potential as antimicrobial agents, although designing peptides with significant antibiofilm effects is cumbersome and expensive. Prospecting peptides’ structures and developing computational approaches for predicting their anti-biofilm properties seem unavoidable. We applied the combination of primary sequence-based, order-based, and structure-based features along with high antibiofilm activity for the first time. There is a long way from computer algorithms to FDA-approved drugs; however, the role of computational methods in facilitating the drug design and development process is undeniable.
We provided a computational platform for predicting the high activity of peptide sequences against bacteria in the biofilm state of life.(hiABFs)
