There is a pressing need for new therapeutics to combat multi-drug and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrugresistant (MDR) clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci (CoNS) strains. High activity against extended-spectrum beta-lactamase (ESBL), meticillin-resistant S. aureus (MRSA), and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.Antibiotic resistance is an ever-increasing threat which is gradually rendering our current repertoire of antibiotics obsolete. If no new drugs are developed, deaths due to antimicrobial resistance are expected to exceed 10 million annually by 2050 (1). Antimicrobial peptides (AMPs) are one potential solution to this problem. Naturally occurring AMPs continue to remain an important component of the innate immune system despite their ancient evolutionary origin and widespread prevalence across many forms of life (2). Some derivatives of these such as pexiganan (3), omiganan (4), and OP-145 (5) are currently undergoing late-stage clinical trials (for diabetic foot ulcers, rosacea, and ear infections respectively (6)). Other peptides such as Novexatin (7) and Lytixar (8) are currently undergoing early-stage clinical trials (for the treatment of toenail fungal infections and MRSA respectively (6)). Currently, over 2000 natural and designed antimicrobial peptides are curated in various databases (9-11), and have displayed broad-spectrum activity against Gram positive, Gram negative, fungal, mycobacterial, and protozoal pathogens (9). Designed AMPs vs. MDR isolates.positive charge are attracted and incorporated into negatively charged bacterial membranes. Once inside the membrane, they are believed to cause disruption through three possible mechanisms: toroidal pore formation (12), carpet formation (13), and barrel stave formation (14). Although the specifics of each mechanism differ, all propose peptide-induced membrane rupture, allowing cytoplasmic leak...
Drug resistance is a public health concern that threatens to undermine decades of medical progress. ESKAPE pathogens cause most nosocomial infections, and are frequently resistant to carbapenem antibiotics, usually leaving tigecycline and colistin as the last treatment options. However, increasing tigecycline resistance and colistin’s nephrotoxicity severely restrict use of these antibiotics. We have designed antimicrobial peptides using a maximum common subgraph approach. Our best peptide (Ω76) displayed high efficacy against carbapenem and tigecycline-resistant Acinetobacter baumannii in mice. Mice treated with repeated sublethal doses of Ω76 displayed no signs of chronic toxicity. Sublethal Ω76 doses co-administered alongside sublethal colistin doses displayed no additive toxicity. These results indicate that Ω76 can potentially supplement or replace colistin, especially where nephrotoxicity is a concern. To our knowledge, no other existing antibiotics occupy this clinical niche. Mechanistically, Ω76 adopts an α-helical structure in membranes, causing rapid membrane disruption, leakage, and bacterial death.
Many bacterial genomes exclusively display an N4-methyl cytosine base (m4C), whose physiological significance is not yet clear. Helicobacter pylori is a carcinogenic bacterium and the leading cause of gastric cancer in humans. Helicobacter pylori strain 26695 harbors a single m4C cytosine methyltransferase, M2.HpyAII which recognizes 5′ TCTTC 3′ sequence and methylates the first cytosine residue. To understand the role of m4C modification, M2.hpyAII deletion strain was constructed. Deletion strain displayed lower adherence to host AGS cells and reduced potential to induce inflammation and apoptosis. M2.hpyAII gene deletion strain exhibited reduced capacity for natural transformation, which was rescued in the complemented strain carrying an active copy of M2.hpyAII gene in the genome. Genome-wide gene expression and proteomic analysis were carried out to discern the possible reasons behind the altered phenotype of the M2.hpyAII gene deletion strain. Upon the loss of m4C modification a total of 102 genes belonging to virulence, ribosome assembly and cellular components were differentially expressed. The present study adds a functional role for the presence of m4C modification in H. pylori and provides the first evidence that m4C signal acts as a global epigenetic regulator in H. pylori.
Most of the biological processes are governed through specific protein–ligand interactions. Discerning different components that contribute toward a favorable protein– ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of ∼68 000 protein–ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein–ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites (84 846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein–ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database—protein–ligand interaction clusters (PLIC).Database URL: http://proline.biochem.iisc.ernet.in/PLIC
Natural molecular machines contain protein components that undergo motion relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein machinery from designed axle and rotor components with internal cyclic or dihedral symmetry. We find that the axle-rotor systems assemble in vitro and in vivo as designed. Using cryo–electron microscopy, we find that these systems populate conformationally variable relative orientations reflecting the symmetry of the coupled components and the computationally designed interface energy landscape. These mechanical systems with internal degrees of freedom are a step toward the design of genetically encodable nanomachines.
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