IDR-targeting compounds suppress HPV genome replication via disruption of phospho-BRD4 association with DNA damage response factors.

Compounds binding to the bromodomains of bromodomain and extra-terminal (BET) family proteins, particularly BRD4, are promising anticancer agents. Nevertheless, side effects and drug resistance pose significant obstacles in BET-based therapeutics development. Using high-throughput screening of a 200,000-compound library, we identified small molecules targeting a phosphorylated intrinsically disordered region (IDR) of BRD4 that inhibit phospho-BRD4 (pBRD4)-dependent human papillomavirus (HPV) genome replication in HPV-containing keratinocytes. Proteomic profiling identified two DNA damage response factors-53BP1 and BARD1-crucial for differentiation-associated HPV genome amplification. pBRD4-mediated recruitment of 53BP1 and BARD1 to the HPV origin of replication occurs in a spatiotemporal and BRD4 long (BRD4-L) and short (BRD4-S) isoform-specific manner. This recruitment is disrupted by phospho-IDR-targeting compounds with little perturbation of the global transcriptome and BRD4 chromatin landscape. The discovery of these protein-protein interaction inhibitors (PPIi) not only demonstrates the feasibility of developing PPIi against phospho-IDRs but also uncovers antiviral agents targeting an epigenetic regulator essential for virus-host interaction and cancer development.

Multiple sequence alignment-based RNA language model and its application to structural inference.

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Design and synthesis of large Stokes shift DNA dyes with reduced genotoxicity.

Despite intensive search over the past decades, only a few small-molecule DNA fluorescent dyes were found with large Stokes shifts. These molecules, however, are often too toxic for widespread usage. Here, we designed DNA-specific fluorescent dyes rooted in benzimidazole architectures with a hitherto unexplored molecular framework based on thiazole-benzimidazole scaffolding. We further incorporated a pyrazole ring with an extended sidechain to prevent cell penetration. These novel benzimidazole derivatives were predicted by quantum calculations and subsequently validated to have large Stokes shifts ranging from 135 to 143 nm, with their emission colors changed from capri blue for the Hoechst reference compound to iguana green. These readily-synthesized compounds, which displayed improved DNA staining intensity and detection limits along with a complete loss of capability for cellular membrane permeation and negligible mutagenic effects as designed, offer a safer alternative to the existing high-performance small-molecule DNA fluorescent dyes.

Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives.

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure-function relations of RNAs.

Epitope delimitation: A new method for defining epitopes of human IgG-reactive antigenic peptides based on rabbit-recognized epitope motifs.

The use of precise epitope peptides as antigens is essential for accurate serological diagnosis of viral-infected individuals, but now it remains an unsolvable problem for mapping precise B cell epitopes (BCEs) recognized by human serum. To address this challenge, we propose a novel epitope delimitation (ED) method to uncover BCEs in the delineated human IgG-reactive (HR) antigenic peptides (APs). Specifically, the method based on the rationale of similarities in humoral immune responses between mammalian species consists of a pair of elements: experimentally delineated HR-AP and rabbit-recognized (RR) BCE motif and corresponding pair of sequence alignment analysis. As a result of using the ED approach, after decoding four RR-epitomes of human papillomavirus types 16/18-E6 and E7 proteins utilizing rabbit serum against each recombinant protein and sequence alignment analysis of HR-APs and RR-BCEs, 19 fine BCEs in 17 of 22 known HR-APs were defined based on each corresponding RR-BCE motifs, including the type-specificity of each delimited BCE in homologous proteins. The test with 22 known 16/20mer HR-APs demonstrated that the ED method is effective and efficient, indicating that it can be used as an alternative method to the conventional identification of fine BCEs using overlapping 8mer peptides.

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.

Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformers, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures, "hallucinated" structures and diffusion models. Results suggest that low complexity regions in the sequences designed by deep learning, for generated structures in particular, remain to be improved, when compared to the native sequences.

Inhibiting the transcription and replication of Ebola viruses by disrupting the nucleoprotein and VP30 protein interaction with small molecules.

Ebola virus (EBOV) causes hemorrhagic fever in humans with high morbidity and fatality. Although over 45 years have passed since the first EBOV outbreak, small molecule drugs are not yet available. Ebola viral protein VP30 is a unique RNA synthesis cofactor, and the VP30/NP interaction plays a critical role in initiating the transcription and propagation of EBOV. Here, we designed a high-throughput screening technique based on a competitive binding assay to bind VP30 between an NP-derived peptide and a chemical compound. By screening a library of 8004 compounds, we obtained two lead compounds, Embelin and Kobe2602. The binding of these compounds to the VP30-NP interface was validated by dose-dependent competitive binding assay, surface plasmon resonance, and thermal shift assay. Moreover, the compounds were confirmed to inhibit the transcription and replication of the Ebola genome by a minigenome assay. Similar results were obtained for their two respective analogs (8-gingerol and Kobe0065). Interestingly, these two structurally different molecules exhibit synergistic binding to the VP30/NP interface. The antiviral efficacy (EC50) increased from 1 µM by Kobe0065 alone to 351 nM when Kobe0065 and Embelin were combined in a 4:1 ratio. The synergistic anti-EBOV effect provides a strong incentive for further developing these lead compounds in future studies.

Self-Inhibitory Peptides Targeting the Neisseria gonorrhoeae MtrCDE Efflux Pump Increase Antibiotic Susceptibility.

Neisseria gonorrhoeae is an increasing public health threat due to its rapidly rising incidence and antibiotic resistance. There are an estimated 106 million cases per year worldwide, there is no vaccine available to prevent infection, and N. gonorrhoeae strains that are resistant to all antibiotics routinely used to treat the infection have emerged. In many strains, antibiotic resistance is mediated by overexpression of the MtrCDE efflux pump, which enables the bacteria to transport toxic antibiotics out of the cell. Genetic mutations that inactivate MtrCDE have previously been shown to render resistant strains susceptible to certain antibiotics. Here, we show that peptides rationally designed to target and disrupt the activity of each of the three protein components of MtrCDE were able to increase the susceptibility of N. gonorrhoeae strains to antibiotics in a dose-dependent manner and with no toxicity to human cells. Cotreatment of bacteria with subinhibitory concentrations of the peptide led to 2- to 64-fold increases in susceptibility to erythromycin, azithromycin, ciprofloxacin, and/or ceftriaxone in N. gonorrhoeae strains FA1090, WHO K, WHO P, and WHO X. The cotreatment experiments with peptides P-MtrC1 and P-MtrE1 resulted in increased susceptibilities of WHO P and WHO X to azithromycin, ciprofloxacin, and ceftriaxone that were of the same magnitude seen in MtrCDE mutants. P-MtrE1 was able to change the azithromycin resistance profile of WHO P from resistant to susceptible. Data presented here demonstrate that these peptides may be developed for use as a dual treatment with existing antibiotics to treat multidrug-resistant gonococcal infections.

De novo protein design by an energy function based on series expansion in distance and orientation dependence.

MOTIVATION: Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing. RESULTS: The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design. AVAILABILITY AND IMPLEMENTATION: The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis.

MOTIVATION: The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic pipeline that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by INFERNAL according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. RESULTS: We showed that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. AVAILABILITY AND IMPLEMENTATION: RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/ and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap_standalone. A platform independent and fully configured docker image of RNAcmap is also provided at https://hub.docker.com/r/jaswindersingh2/rnacmap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-throughput mapping of RNA solvent accessibility at the single-nucleotide resolution by RtcB ligation between a fixed 5'-OH-end linker and unique 3'-P-end fragments from hydroxyl radical cleavage.

Given the challenges for the experimental determination of RNA tertiary structures, probing solvent accessibility has become increasingly important to gain functional insights. Among various chemical probes developed, backbone-cleaving hydroxyl radical is the only one that can provide unbiased detection of all accessible nucleotides. However, the readouts have been based on reverse transcription (RT) stop at the cleaving sites, which are prone to false positives due to PCR amplification bias, early drop-off of reverse transcriptase, and the use of random primers in RT reaction. Here, we introduced a fixed-primer method called RL-Seq by performing RtcB Ligation (RL) between a fixed 5'-OH-end linker and unique 3'-P-end fragments from hydroxyl radical cleavage prior to high-throughput sequencing. The application of this method to E. coli ribosomes confirmed its ability to accurately probe solvent accessibility with high sensitivity (low required sequencing depth) and accuracy (strong correlation to structure-derived values) at the single-nucleotide resolution. Moreover, a near-perfect correlation was found between the experiments with and without using unique molecular identifiers, indicating negligible PCR biases in RL-Seq. Further improvement of RL-Seq and its potential transcriptome-wide applications are discussed.

Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity.

Despite the large number of noncoding RNAs in human genome and their roles in many diseases include cancer, we know very little about them due to lack of structural clues. The centerpiece of the structural clues is the full RNA base-pairing structure of secondary and tertiary contacts that can be precisely obtained only from costly and time-consuming 3D structure determination. Here, we performed deep mutational scanning of self-cleaving CPEB3 ribozyme by error-prone PCR and showed that a library of <5 × 104 single-to-triple mutants is sufficient to infer 25 of 26 base pairs including non-nested, nonhelical, and noncanonical base pairs with both sensitivity and precision at 96%. Such accurate inference was further confirmed by a twister ribozyme at 100% precision with only noncanonical base pairs as false negatives. The performance was resulted from analyzing covariation-induced deviation of activity by utilizing both functional and nonfunctional variants for unsupervised classification, followed by Monte Carlo (MC) simulated annealing with mutation-derived scores. Highly accurate inference can also be obtained by combining MC with evolution/direct coupling analysis, R-scape or epistasis analysis. The results highlight the usefulness of deep mutational scanning for high-accuracy structural inference of self-cleaving ribozymes with implications for other structured RNAs that permit high-throughput functional selections.

Nitrogen and phosphorus removal efficiency and algae viability in an immobilized algae and bacteria symbiosis system with pink luminescent filler.

In this study, an immobilized algae and bacteria symbiotic biofilm reactor (ABSBR) with pink luminescent filler (PLF) was constructed. The effects of PLF addition in the construction of an algae and bacteria symbiotic biofilm system on the nitrogen and phosphorus removal efficiencies and algae viability were evaluated. Our results showed that for influent TN and TP concentrations of 40 ± 5 and 5 ± 0.8 mg/L, respectively, the pollutant removal rates (PRRs) of TN and TP by the ABSBR can reach up to 74.74% and 88.36%, respectively. The chlorophyll-a (chl-a) concentration on the PLF reaches approximately 5,500 µg/L with a specific oxygen generation rate (SOGR) of 65.48 µmolO2 mg-1Chl-a h-1. These results indicate that the adding PLF into algae and bacteria symbiosis systems can effectively improve the nitrogen and phosphorus removal efficiencies of the sewage as well as increase biomass and viability of the algae in the system.

Polygenic Risk Scores have high diagnostic capacity in ankylosing spondylitis.

OBJECTIVE: We sought to test the hypothesis that Polygenic Risk Scores (PRSs) have strong capacity to discriminate cases of ankylosing spondylitis (AS) from healthy controls and individuals in the community with chronic back pain. METHODS: PRSs were developed and validated in individuals of European and East Asian ethnicity, using data from genome-wide association studies in 15 585 AS cases and 20 452 controls. The discriminatory values of PRSs in these populations were compared with other widely used diagnostic tests, including C-reactive protein (CRP), HLA-B27 and sacroiliac MRI. RESULTS: In people of European descent, PRS had high discriminatory capacity with area under the curve (AUC) in receiver operator characteristic analysis of 0.924. This was significantly better than for HLA-B27 testing alone (AUC=0.869), MRI (AUC=0.885) or C-reactive protein (AUC=0.700). PRS developed and validated in individuals of East Asian descent performed similarly (AUC=0.948). Assuming a prior probability of AS of 10% such as in patients with chronic back pain under 45 years of age, compared with HLA-B27 testing alone, PRS provides higher positive values for 35% of patients and negative predictive values for 67.5% of patients. For PRS, in people of European descent, the maximum positive predictive value was 78.2% and negative predictive value was 100%, whereas for HLA-B27, these values were 51.9% and 97.9%, respectively. CONCLUSIONS: PRS have higher discriminatory capacity for AS than CRP, sacroiliac MRI or HLA-B27 status alone. For optimal performance, PRS should be developed for use in the specific ethnic groups to which they are to be applied.

Association between Dietary Intake of Folate and the Risks of Multiple Cancers in Chinese Population: A Dose-Response Meta-Analysis of Observational Studies.

BACKGROUND AND OBJECTIVE: A lot of existing studies had revealed the associations between dietary folate intake and certain cancers, and generated inconsistent results. In addition, evidence on the association among Chinese population has not been systematically assessed. Therefore, we conducted a meta-analysis on this topic. METHODS: PubMed and Embase were searched up to March 20, 2020 for potential studies. Pooled relative risks (RR) and dose-response analysis were used to examine dietary folate intake and cancers risks among Chinese population. RESULTS: Seven cohort studies and 9 case-control studies that involved 561538 participants and 10073 cancer events were included. The summary RR of cancer events for the highest vs. lowest category was 0.73 (0.61, 0.88). Dose-response analysis revealed that those maintaining 250 µg daily intake of dietary folate had the lowest risk of cancers events among Chinese. CONCLUSION: Our findings indicate the inverse association between dietary folate intake and risk of cancers events, the association is especially clear in women.

Argonaute proteins: structures and their endonuclease activity.

Argonaute proteins are highly conserved and widely expressed in almost all organisms. They not only play a critical role in the biogenesis of small RNAs but also defend against invading nucleic acids via small RNA or DNA-mediated gene silencing pathways. One functional mechanism of Argonaute proteins is acting as a nucleic-acid-guided endonuclease, which can cleave targets complementary to DNA or RNA guides. The cleavage then leads to translational silencing directly or indirectly by recruiting additional silencing proteins. Here, we summarized the latest research progress in structural and biological studies of Argonaute proteins and pointed out their potential applications in the field of gene editing.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.

BACKGROUND: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment. METHODS: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method. RESULTS: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods. CONCLUSIONS: The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method-with other evaluation methods, such as EEG-is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

A survey of current practices, attitudes and demands of anaesthesiologists regarding the depth of anaesthesia monitoring in China.

BACKGROUND: In this study, we aimed to analyse survey data to explore two different hypotheses; and for this purpose, we distributed an online survey to Chinese anaesthesiologists. The hypothetical questions in this survey include: (1) Chinese anaesthesiologists mainly use the depth of anaesthesia (DoA) monitors to prevent intraoperative awareness and (2) the accuracy of these monitors is the most crucial performance factor during the clinical daily practice of Chinese anaesthesiologists. METHODS: We collected and statistically analysed the response of a total of 12,750 anesthesiologists who were invited to participate in an anonymous online survey. The Chinese Society of Anaesthesiologists (CSA) trial group provided the email address of each anaesthesiologist, and the selection of respondents was random from the computerized system. RESULTS: The overall response rate was 32.0% (4037 respondents). Only 9.1% (95% confidence interval, 8.2-10.0%) of the respondents routinely used DoA monitors. Academic respondents (91.5, 90.3-92.7%) most frequently used DoA monitoring to prevent awareness, whereas nonacademic respondents (88.8, 87.4-90.2%) most frequently used DoA monitoring to guide the delivery of anaesthetic agents. In total, the number of respondents who did not use a DoA monitor and whose patients experienced awareness (61.7, 57.8-65.6%) was significantly greater than those who used one or several DoA monitors (51.5, 49.8-53.2%). Overall, the crucial performance factor during DoA monitoring was considered by 61.9% (60.4-63.4%) of the respondents to be accuracy. However, most respondents (95.7, 95.1-96.3%) demanded improvements in the accuracy of the monitors for DoA monitoring. In addition, broad application in patients of all ages (86.3, 85.2-87.4%), analgesia monitoring (80.4, 79.2-81.6%), and all types of anaesthetic agents (75.6, 74.3-76.9%) was reported. In total, 65.0% (63.6-66.5%) of the respondents believed that DoA monitors should be combined with EEG and vital sign monitoring, and 53.7% (52.1-55.2%) believed that advanced DoA monitors should include artificial intelligence. CONCLUSIONS: Academic anaesthesiologists primarily use DoA monitoring to prevent awareness, whereas nonacademic anaesthesiologists use DoA monitoring to guide the delivery of anaesthetics. Anaesthesiologists demand high-accuracy DoA monitors incorporating EEG signals, multiple vital signs, and antinociceptive indicators. DoA monitors with artificial intelligence may represent a new direction for future research on DoA monitoring.

Experimental study on the selection of common adsorption substrates for extensive green roofs (EGRs).

Adsorption substrate in the substrate layer of an extensive green roof (EGR) is one of the most important factors affecting rainwater retention and pollution interception capacity. However, the contact time between runoff and adsorption substrate is extremely short in actual rainfall, and adsorption substrate cannot show fully rainwater retention and pollution interception capacity. So, selection of adsorption substrate based on its physical properties and theoretical adsorption capacity is unreliable. In this study, eight commonly-used adsorption substrate experimental devices are constructed with the same configuration. The delayed outflow time and runoff reduction rate of each device, along with event measurement concentration (EMC), average EMC, and cumulative pollutant quantity of SS, ammonium (NH4+), nitrate (NO3-), total nitrogen (TN), and total phosphorus (TP) in each device outflow under nine simulated rainfall events are measured and evaluated. The results indicate that vermiculite has a significant interception effect on NH4+ and TP with the advantages of low bulk density, high porosity, low cost, and a good rainfall runoff retention capacity under torrential rain and downpour events. In future practical engineering and related studies of EGR, attention should be paid to ameliorating the deficiencies of the adsorption substrates and optimizing their synergistic effects when combined with nutrient substrates.

Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides.

Bacterial infection is one of the leading causes of death in young, elderly, and immune-compromised patients. The rapid spread of multi-drug-resistant (MDR) bacteria is a global health emergency and there is a lack of new drugs to control MDR pathogens. We describe a heretofore-unexplored discovery pathway for novel antibiotics that is based on self-targeting, structure-disrupting peptides. We show that a helical peptide, KFF- EcH3, derived from the Escherichia coli methionine aminopeptidase can disrupt secondary and tertiary structure of this essential enzyme, thereby killing the bacterium (including MDR strains). Significantly, no detectable resistance developed against this peptide. Based on a computational analysis, our study predicted that peptide KFF- EcH3 has the strongest interaction with the structural core of the methionine aminopeptidase. We further used our approach to identify peptide KFF- NgH1 to target the same enzyme from Neisseria gonorrhoeae. This peptide inhibited bacterial growth and was able to treat a gonococcal infection in a human cervical epithelial cell model. These findings present an exciting new paradigm in antibiotic discovery using self-derived peptides that can be developed to target the structures of any essential bacterial proteins.-Zhan, J., Jia, H., Semchenko, E. A., Bian, Y., Zhou, A. M., Li, Z., Yang, Y., Wang, J., Sarkar, S., Totsika, M., Blanchard, H., Jen, F. E.-C., Ye, Q., Haselhorst, T., Jennings, M. P., Seib, K. L., Zhou, Y. Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides.