Researchers from ICMR-National Institute for Research in Reproductive Health, Bombay, College of Pharmacy, and Indian Institute of Technology Bombay in Mumbai used popular online algorithms and molecular dynamics, to design an effective antimicrobial peptide.
Antimicrobial peptides (AMPs) are a host of biological molecules part of the innate immune system found in almost all the classes of life. Unlike conventional antimicrobial drugs, like antibiotics, AMPs exhibit a broad spectrum of antimicrobial activity, attacking multiple cellular targets. This makes it particularly difficult for a pathogen to evolve resistance against AMPs. This has made AMPs a target of a host of studies with digital tools like machine learning algorithms employed, to classify and design more AMPs, with natural AMPs as template.
For their study, the researchers screened through a library of peptides, with DNA similarity to known AMPs—Myeloid Antimicrobial Peptide (MAP) family. MAPs exhibit a broad spectrum of antibacterial, antifungal, anticancer, and antiviral activity, and thus were chosen as the template. Next, around 1000 random peptides were designed, by making small changes to the template molecule. After screening the 1000 peptides using CD-HIT webserver, 935 of them with <85% identity of the template were selected for further studies.
The researchers then used popular online algorithms like AntiBp2, ADAM, CAMPR3, and iAMP-2L were used to predict the antimicrobial activity of the 935 peptides. “Seven of the 935 peptides had prediction scores greater than that of the known antimicrobial peptide BMAP-28(1–18)” say the authors of the study. Next, three samples out of the seven peptides were chosen at random for further studies. The three samples were tested for their antimicrobial activity against Gram-positive Staphylococcus aureus and Gram negative E-coli. Two out of the three were seen to have excellent antimicrobial activity against these pathogen.
Interestingly, the researchers noticed a switch in the activity of one of the designed AMPs, due a single residue mutation. This suggests that single residue change could have a drastic effect on the potency of AMPs. The authors suggest using molecular dynamics simulations to make such single residue changes to amplify the potency of AMPs
The study shows, although online prediction algorithms could help with designing newer AMPs, for an effective outcome, the algorithms have to be used along with molecular dynamics guided rational design for an effective outcome.