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Objectives: Antimicrobial resistance is a growing concern and claims over 1 million lives per year. The discovery of new antimicrobial drugs is expensive and often generates low profitability, with very low success rates. One way to combat this is by the improvement of known antimicrobials, such as antimicrobial peptides (AMPs). The aim of this study was to improve the antimicrobial activities of two known AMPs, UyCT3 and indolicidin, with the use of peptide libraries and growth curves., Methods: Peptide permutation libraries were synthesized for two AMPs, indolicidin and UyCT3, which included 520 peptides. These peptides were subsequently tested against MG1655-K12, to which subsequent peptide design was performed, then tested against three clinically Gram-negative relevant drug-resistant isolates. Best-performing candidates were subjected to a haemolysis assay for toxicity validation., Results: Single amino acid permutations of UyCT3 and indolicidin were sufficient to inhibit growth of MG1655-K12, and subsequent generations of peptide design were able to inhibit growth of clinical isolates at concentrations as low as 5 µM. Our best-performing AMP, UyCT3 I5A, W6Y, K10I, F13I , was not seen to be toxic towards sheep RBCs., Conclusions: The efficacy of the AMPs improved with the use of our peptide library technology, whereby an AMP was found that inhibited bacterial growth of clinical Gram-negative isolates 4-fold better than its WT counterpart., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.)

Proteins play a critical role as key regulators in various biological systems, influencing crucial processes such as gene expression, cell cycle progression, and cellular proliferation. However, the functions of proteins can be further modified through post-translational modifications (PTMs), which expand their roles and contribute to disease progression when dysregulated. In this review, we delve into the methodologies employed for the characterization of PTMs, shedding light on the techniques and tools utilized to help unravel their complexity. Furthermore, we explore the prevalence of crosstalk and competition that occurs between different types of PTMs, specifically focusing on both histone and non-histone proteins. The intricate interplay between different modifications adds an additional layer of regulation to protein function and cellular processes. To gain insights into the competition for lysine residues among various modifications, computational systems such as MethylSight have been developed, allowing for a comprehensive analysis of the modification landscape. Additionally, we provide an overview of the exciting developments in the field of inhibitors or drugs targeting PTMs, highlighting their potential in combatting prevalent diseases. The discovery and development of drugs that modulate PTMs present promising avenues for therapeutic interventions, offering new strategies to address complex diseases. As research progresses in this rapidly evolving field, we anticipate remarkable advancements in our understanding of PTMs and their roles in health and disease, ultimately paving the way for innovative treatment approaches., Competing Interests: Declaration of competing interest The authors declare no competition of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein-protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein-protein interaction predictors are more suitable for this purpose than the state-of-the-art deep learning methods publicly available at the time of writing. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein-protein interaction predictors for peptide therapeutic engineering, especially peptide analogs., (© 2022. The Author(s).)

Astroglial cells are key players in the development and maintenance of neurons and neuronal networks. Astroglia express steroid hormone receptors and show rapid responses to hormonal manipulations. However, despite important sex differences in the cortex and hippocampus, few studies have examined sex differences in astroglial cells in telencephalic development. To characterize the cortical astroglial translatome in male and female mice across postnatal development, we use translating ribosome affinity purification together with RNA sequencing and immunohistochemistry to phenotype astroglia at six developmental time points. Overall, we find two distinct astroglial phenotypes between early (P1-P7) and late development (P14-adult), independent of sex. We also find sex differences in gene expression patterns across development that peak at P7 and appear to result from males reaching a mature astroglial phenotype earlier than females. These developmental sex differences could have an impact on the construction of neuronal networks and windows of vulnerability to perturbations and disease., Competing Interests: Declaration of interest The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Protein lysine methylation mediates a variety of biological processes, and their dysregulation has been established to play pivotal roles in human disease. A number of these sites constitute attractive drug targets. However, systematic identification of methylation sites is challenging and resource intensive. Here, we present a protocol combining MethylSight, a machine learning model trained to identify promising lysine methylation sites, and mass spectrometry for subsequent validation. Our approach can reduce the time and investment required to identify novel methylation sites. For complete information on the use and execution of this protocol, please refer to Biggar et al. (2020)., Competing Interests: The authors declare no competing interests., (© 2020 The Author(s).)

Protein Lys methylation plays a critical role in numerous cellular processes, but it is challenging to identify Lys methylation in a systematic manner. Here we present an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We develop MethylSight, a program that predicts Kme events solely on the physicochemical properties of residues surrounding the putative methylation sites, which then requires validation by targeted MS. Using this approach, we identify 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts that Lys methylation is a prevalent post-translational modification in the human proteome. Our work provides a useful resource for guiding systematic exploration of the role of Lys methylation in human health and disease., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Recent mobile and automated audiometry technologies have allowed for the democratization of hearing healthcare and enables non-experts to deliver hearing tests. The problem remains that a large number of such users are not trained to interpret audiograms. In this work, we outline the development of a data-driven audiogram classification system designed specifically for the purpose of concisely describing audiograms. More specifically, we present how a training dataset was assembled and the development of the classification system leveraging supervised learning techniques. We show that three practicing audiologists had high intra- and inter-rater agreement over audiogram classification tasks pertaining to audiogram configuration, symmetry and severity. The system proposed here achieves a performance comparable to the state of the art, but is significantly more flexible. Altogether, this work lays a solid foundation for future work aiming to apply machine learning techniques to audiology for audiogram interpretation.

The ferric uptake regulator (Fur) is a superfamily of transcription factors found in bacteria which control the expression of a myriad of genes. In this study, we report a simple protocol for the purification of recombinant untagged Campylobacter jejuni Fur (CjFur). CjFur was isolated using a combination of three ion exchange chromatography steps followed by size exclusion chromatography on a Superdex 75. ESI-MS analysis shows that our method yields pure CjFur and that this tag-free version incorporates metal more efficiently than recombinant CjFur harboring a tag or tag remnants. Finally, electrophoretic mobility shift assays show that this new purification method yields a CjFur preparation that binds DNA more efficiently. These results suggest that adding a N-terminus tag onto CjFur is detrimental to its activity. Overall, the approaches detailed in this study offer an alternative strategy for the purification of CjFur, and likely other metalloregulators, for future biochemical and biophysical studies.

In Campylobacter jejuni (Cj), the metal-cofactored peroxide response regulator (PerR) transcription factor allows C. jejuni to respond to oxidative stresses. The crystal structure of the metalated form of CjPerR shows that the protein folds as an asymmetric dimer displaying structural differences in the orientation of its DNA-binding domain. Comparative analysis shows that such asymmetry is a conserved feature among crystallized PerR proteins, and mutational analysis reveals that residues found in the first α-helix of CjPerR contribute to DNA binding. These studies present the structure of CjPerR protein and highlight structural heterogeneity in the orientation of the metalated PerR DNA-binding domain which may underlie the ability of PerR to recognize DNA, control gene expression, and contribute to bacterial pathogenesis., (© 2018 Federation of European Biochemical Societies.)

Ferric uptake regulators (Fur) are a family of transcription factors coupling gene regulatory events to metal concentration. Recent evidence has expanded the mechanistic repertoires employed by Fur to activate or repress gene expression in the presence or absence of regulatory metals. However, the mechanistic basis underlying this extended repertoire has remained largely unexplored. In this study, we used an extensive set of mutations to demonstrate that Campylobacter jejuni Fur (CjFur) employs the same surface to positively and negatively control gene expression regardless of the presence or absence of metals. Moreover, the crystal structure determination of a CjFur devoid of any regulatory metals shows that subtle reorientation of the transcription factor DNA binding domain negatively impacts DNA binding, gene expression and gut colonization in chickens. Overall, these results highlight the versatility of the CjFur DNA binding domain in mediating all gene regulatory events controlled by the metalloregulator and that the full metalation of CjFur is critical to the Campylobacter jejuni life cycle in vivo.