Computational identification of a systemic antibiotic for gram-negative bacteria

Area of Science:

• Microbiology
• Structural Biology
• Drug Discovery

Background:

• Gram-negative bacteria possess a challenging outer membrane penetration barrier for antibiotics.
• BamA, essential for outer membrane protein insertion, is a validated surface target.
• Existing antibiotics like darobactins target BamA.

Purpose of the Study:

• To identify novel peptide antibiotics targeting BamA using computational methods.
• To characterize the structure and mechanism of action of a newly discovered antibiotic, dynobactin.
• To evaluate dynobactin's efficacy against Gram-negative infections.

Main Methods:

• Computational search for genes distantly related to the darobactin operon.
• Isolation and structural elucidation of dynobactin A from Photorhabdus australis.
• Determination of BamA-dynobactin co-crystal structure and BAM-complex-dynobactin cryo-EM structure.
• In vivo efficacy testing in a mouse model of Escherichia coli infection.

Main Results:

• Identification of dynobactin A, a novel peptide antibiotic with two unlinked rings, structurally distinct from darobactins.
• Dynobactin targets BamA by binding to its lateral gate and protruding into the β-barrel lumen.
• Dynobactin demonstrated efficacy in treating systemic Escherichia coli infection in mice.

Conclusions:

• Computational approaches are effective for discovering novel antibiotic scaffolds.
• Dynobactin represents a promising new class of antibiotics targeting BamA.
• Dynobactin is a potential lead compound for developing new treatments against Gram-negative infections.