Machine learning approaches to study the structure-activity relationships of LpxC inhibitors
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ABSTRACT:
SUBMITTER: Yu T
PROVIDER: S-EPMC10630528 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:The zinc-dependent deacetylase LpxC catalyzes the committed step of lipid A biosynthesis in Gram-negative bacteria and is a validated target for the development of novel antibiotics to combat multidrug-resistant Gram-negative infections. Many potent LpxC inhibitors contain an essential threonyl-hydroxamate headgroup for high-affinity interaction with LpxC. We report the synthesis, antibiotic activity, and structural and enzymatic characterization of novel LpxC inhibitors containing an additional aryl group in the threonyl-hydroxamate moiety, which expands the inhibitor-binding surface in LpxC. These compounds display enhanced potency against LpxC in enzymatic assays and superior antibiotic activity against Francisella novicida in cell culture. The comparison of the antibiotic activities of these compounds against a leaky Escherichia coli strain and the wild-type strain reveals the contribution of the formidable outer-membrane permeability barrier that reduces the compounds efficacy in cell culture and emphasizes the importance of maintaining a balanced hydrophobicity and hydrophilicity profile in developing effective LpxC-targeting antibiotics.
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Project description:In the search for novel Gram-negative agents, we performed a comprehensive search of the AstraZeneca collection and identified a tetrahydropyran-based matrix metalloprotease (MMP) inhibitor that demonstrated nanomolar inhibition of UDP-3-O-(acyl)-N-acetylglucosamine deacetylase (LpxC). Crystallographic studies in Aquifex aeolicus LpxC indicated the tetrahydropyran engaged in the same hydrogen bonds and van der Waals interactions as other known inhibitors. Systematic optimization of three locales on the scaffold provided compounds with improved Gram-negative activity. However, the optimization of LpxC activity was not accompanied by reduced inhibition of MMPs. Comparison of the crystal structure of the native product, UDP-3-O-(acyl)-glucosamine, in Aquifex aeolicus to the structure of a tetrahydropyran-based inhibitor indicates pathways for future optimization.
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Project description:Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350-700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%-96% (sensitivity 50%-87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs-"Simple" and "Data Fusion". The "Simple" RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The "Data Fusion" RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%-80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%-95% range across 9 health hazards with no constraints on tested compounds.
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Project description:The nation's opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is a FDA-approved reversal agent, antagonizes opioids through competitive binding at the μ-opioid receptor (mOR). Thus, knowledge of the opioid's residence time is important for assessing the effectiveness of naloxone. Here, we estimated the residence times (τ) of 15 fentanyl and 4 morphine analogs using metadynamics and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann et al. Clin. Pharmacol. Therapeut. 2022, 120, 1020-1232). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning approach to analyze the kinetic impact of fentanyl's substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computer-aided drug discovery.
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