ABSTRACT
The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer-generated sequences featured improved cancer-cell penetration, induced cancer-cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co-administered chemotherapeutic agents inâ vitro. This study demonstrates the potential of multidimensional machine-learning methods for rapidly obtaining peptides with the desired cellular activities.
Subject(s)
Antineoplastic Agents/pharmacology , Breast Neoplasms/drug therapy , Cell-Penetrating Peptides/pharmacology , Computer-Aided Design , Dermis/drug effects , Apoptosis/drug effects , Breast Neoplasms/pathology , Cell Survival/drug effects , Cells, Cultured , Dermis/cytology , Drug Therapy, Combination , Female , HumansABSTRACT
Using computational bioactivity prediction models we identified phosphodiesterase 3B (PDE3B) and cathepsin L as macromolecular targets of de novo designed compounds. By disclosing the most potent cathepsin L activator known to date, small molecule repurposing by target panel prediction represents a feasible route towards innovative leads for chemical biology and molecular medicine.
Subject(s)
Cathepsin L/metabolism , Cyclic Nucleotide Phosphodiesterases, Type 3/metabolism , Phosphodiesterase 3 Inhibitors/chemistry , Protease Inhibitors/chemistry , Cathepsin L/antagonists & inhibitors , Computer Simulation , Cyclic Nucleotide Phosphodiesterases, Type 3/chemistry , Phosphodiesterase 3 Inhibitors/metabolism , Protease Inhibitors/metabolism , Protein Binding , Structure-Activity RelationshipABSTRACT
Antimicrobial peptides (AMPs) show remarkable selectivity toward lipid membranes and possess promising antibiotic potential. Their modes of action are diverse and not fully understood, and innovative peptide design strategies are needed to generate AMPs with improved properties. We present a de novo peptide design approach that resulted in new AMPs possessing low-nanomolar membranolytic activities. Thermal analysis revealed an entropy-driven mechanism of action. The study demonstrates sustained potential of advanced computational methods for designing peptides with the desired activity.
Subject(s)
Antimicrobial Cationic Peptides/chemical synthesis , Antimicrobial Cationic Peptides/metabolism , Antimicrobial Cationic Peptides/chemistry , Drug Design , Microscopy, Atomic Force , ThermodynamicsABSTRACT
Kinase inhibitors: Ligand-based de novo design is validated as a viable technology for rapidly generating innovative compounds possessing the desired biochemical profile. The study discloses the discovery of the most selective vascular endothelial growth factor receptor-2 (VEGFR-2) kinase inhibitor (right in scheme) known to date as prime lead for antiangiogenic drug development.
