Collagen-derived cryptides : machine-learning prediction and molecular dynamic interaction against Klebsiella pneumoniae biofilm synthesis precursor

Collagen-derived cryptic peptides (cryptides) are biologically active peptides derived from the proteolytic digestion of collagen protein. These cryptides possess a multitude of activities, including antihypertensive, antiproliferative, and antibacterial. The latter, however, has not been extensively studied. The cryptides are mainly obtained from the protein hydrolysate, followed by characterizations to elucidate the function, limiting the number of cryptides investigated within a short period. The recent threat of antimicrobial resistance microorganisms (AMR) to global health requires the rapid development of new therapeutic drugs. The current study aims to predict antimicrobial peptides (AMP) from collagen-derived cryptides, followed by elucidating their potential to inhibit biofilm-related precursors in Klebsiella pneumoniae using in silico approach. Therefore, cryptides derived from collagen amino acid sequences of various types and species were subjected to online machine-learning platforms (i.e., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). The peptide-protein interaction was elucidated using molecular docking, molecular dynamics, and MM-PBSA analysis against MrkH, a K. pneumoniae’s transcriptional regulator of type 3 fimbriae that promote biofilm formation. As a result, six potential antibiofilm inhibitory cryptides were screened and docked against MrkH. All six peptides bind stronger than the MrkH ligand (c-di-GMP; C2E)

Targeting Natural Plant Metabolites for Hunting SARS-CoV-2 Omicron BA.1 Variant Inhibitors: Extraction, Molecular Docking, Molecular Dynamics, and Physicochemical Properties Study

(1) Background: SARS-CoV-2 Omicron BA.1 is the most common variation found in most countries and is responsible for 99% of cases in the United States. To overcome this challenge, there is an urgent need to discover effective inhibitors to prevent the emerging BA.1 variant. Natural products, particularly flavonoids, have had widespread success in reducing COVID-19 prevalence. (2) Methods: In the ongoing study, fifteen compounds were annotated from Echium angustifolium and peach (Prunus persica), which were computationally analyzed using various in silico techniques. Molecular docking calculations were performed for the identified phytochemicals to investigate their efficacy. Molecular dynamics (MD) simulations over 200 ns followed by molecular mechanics Poisson–Boltzmann surface area calculations (MM/PBSA) were performed to estimate the binding energy. Bioactivity was also calculated for the best components in terms of drug likeness and drug score. (3) Results: The data obtained from the molecular docking study demonstrated that five compounds exhibited remarkable potency, with docking scores greater than −9.0 kcal/mol. Among them, compounds 1, 2 and 4 showed higher stability within the active site of Omicron BA.1, with ΔGbinding values of −49.02, −48.07, and −67.47 KJ/mol, respectively. These findings imply that the discovered phytoconstituents are promising in the search for anti-Omicron BA.1 drugs and should be investigated in future in vitro and in vivo research