Dataset Information

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling.

ABSTRACT: Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure-activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q _loo ²) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.

SUBMITTER: Mahmoodi-Reihani M

PROVIDER: S-EPMC7097998 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Antimicrobial resistance is a growing health concern. Antimicrobial peptides are a potential solution because they bypass conventional drug resistance mechanisms. Previously, we isolated a peptide from Crocodylus siamensis hemoglobin hydrolysate, which has antimicrobial activity and identified the main peptide from this mixture (QL17). The objective of this work was to evaluate and rationally modify QL17 in order to: (1) control its mechanism of action through bacterial membrane disruption; (2) improve its antimicrobial activity; and (3) ensure it has low cytotoxicity against normal eukaryotic cells. QL17 was rationally designed using physicochemical and template-based methods. These new peptide variants were assessed for: (1) their in vitro inhibition of microbial growth, (2) their cytotoxicity against normal cells, (3) their selectivity for microbes, and (4) the mode of action against bacteria using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and confocal microscopy. The results indicate that all designed peptides have more potent antimicrobial efficacy than QL17 and IL15 peptides. However, only the most rationally modified peptides showed strong antimicrobial activity and minimal toxicity against normal cells. In particular, IL15.3 (hydrophobicity of 47% and net charge of + 6) was a potent antimicrobial agent (MIC = 4-12 μg/mL; MBC = 6-25 μg/mL) and displayed excellent selectivity for microbes (cf. human cells) via FACS assays. Microscopy confirmed that IL15.3 acts against bacteria by disrupting the cell membrane integrity and penetrating into the membrane. This causes the release of intracellular content into the outer environment leading to the death of bacteria. Moreover, IL15.3 can also interact with DNA suggesting it could have dual mode of action. Overall, a novel variant of QL17 is described that increases antimicrobial activity by over 1000-fold (~ 5 μg/mL MIC) and has minimal cytotoxicity. It may have applications in clinical use to treat and safeguard against bacteria.

Dataset Information

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets