Computed insight into a peptide inhibitor preventing the induced fit mechanism of MurA enzyme from <i>Pseudomonas aeruginosa</i>

Lima, Anderson Henrique Lima e; Santos, Alberto Monteiro dos; Alves, Cláudio Nahum; Lameira, Jerônimo

doi:10.1111/cbdd.12882

Cited by 11 publications

(7 citation statements)

References 70 publications

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“…Our findings underscore the role of covalent interactions between PEP and Cys115 in ensuring protein stability. Cys115 is part of a crucial loop region (Pro112–Pro121) responsible for the induced‐fit mechanism that triggers the conformational change induced by the interaction of MurA with UNAG (Schönbrunn et al, 2000; Lima et al, 2017). When attached to Cys115, PEP interacts with Arg91, Gly114, Ala116, Ile117, Gly118, Arg120, and Arg397 in the UNAG–PEP–MurA and UNAM–PEP–MurA complexes.…”

Section: Discussionmentioning

confidence: 99%

“…This preservation allowed the movement of the Pro112-Pro121 loop, which contains the QPA and Arg120 residues. These residues are responsible for the open-closed conformations of MurA in various organisms, such as Pseudomonas aeruginosa (Lima et al, 2017), producing an open state following ligand unbinding (Figure 10). Notably, the interactions observed in both the MD and τRAMD simulations maintain exceptional strength, persisting throughout the simulation period, even after the ligand leaves the binding site.…”

Section: Ligand Unbinding Mechanismsmentioning

confidence: 99%

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Role of UDP‐N‐acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations

de Oliveira,

da Costa,

Silva

et al. 2024

Protein Science

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The peptidoglycan biosynthesis pathway plays a vital role in bacterial cells, and facilitates peptidoglycan layer formation, a fundamental structural component of the bacterial cell wall. The enzymes in this pathway are candidates for antibiotic development, as most do not have mammalian homologues. The UDP‐N‐acetylglucosamine (UNAG) enolpyruvyl transferase enzyme (MurA) in the peptidoglycan pathway cytoplasmic step is responsible for the phosphoenolpyruvate (PEP)–UNAG catalytic reaction, forming UNAG enolpyruvate and inorganic phosphate. Reportedly, UDP‐N‐acetylmuramic acid (UNAM) binds tightly to MurA forming a dormant UNAM–PEP–MurA complex and acting as a MurA feedback inhibitor. MurA inhibitors are complex, owing to competitive binding interactions with PEP, UNAM, and UNAG at the MurA active site. We used computational methods to explore UNAM and UNAG binding. UNAM showed stronger hydrogen‐bond interactions with the Arg120 and Arg91 residues, which help to stabilize the closed conformation of MurA, than UNAG. Binding free energy calculations using end‐point computational methods showed that UNAM has a higher binding affinity than UNAG, when PEP is attached to Cys115. The unbinding process, simulated using τ–random acceleration molecular dynamics, showed that UNAM has a longer relative residence time than UNAG, which is related to several complex dissociation pathways, each with multiple intermediate metastable states. This prevents the loop from opening and exposing the Arg120 residue to accommodate UNAG and potential new ligands. Moreover, we demonstrate the importance of Cys115‐linked PEP in closed‐state loop stabilization. We provide a basis for evaluating novel UNAM analogues as potential MurA inhibitors.Public significanceMurA is a critical enzyme involved in bacterial cell wall biosynthesis and is involved in antibiotic resistance development. UNAM can remain in the target protein's active site for an extended time compared to its natural substrate, UNAG. The prolonged interaction of this highly stable complex known as the ‘dormant complex’ comprises UNAM–PEP–MurA and offers insights into antibiotic development, providing potential options against drug‐resistant bacteria and advancing our understanding of microbial biology.

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Section: Discussionmentioning

confidence: 99%

Section: Ligand Unbinding Mechanismsmentioning

confidence: 99%

Role of UDP‐N‐acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations

de Oliveira,

da Costa,

Silva

et al. 2024

Protein Science

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“…This computational approach toward more open binding site of MurA was reported to produce inhibitor with a K i value of 9.52 μM (Kumar, Saranathan, Prashanth, Tiwary, & Krishna, 2017). Transition from open to the closed state of MurA can also be interrupted as shown by a peptidic MurA inhibitor discovered by modern peptide docking methodology coupled to MD (Lima, Dos Santos, Alves, & Lameira, 2017).…”

Section: Recent Successes In Targeting Mur Enzymesmentioning

confidence: 99%

Reaching toward underexplored targets in antibacterial drug design

Jukič

Gobec

Sova

2018

Drug Development Research

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The increase of antimicrobial resistance necessitates the renewal and strong research involvement in antibacterial drug design. In the following work, we comment on the key approaches used in development of new antibacterials, focusing on intracellular therapeutic targets that have been so far mostly underexplored: the enzymes of the Mur pathway MurA to MurF. We identify common obstacles observed during research on MurA, MurB, and Mur ligases inhibitors and their development into potential antibacterial compounds, and discern several approaches and solutions to tackle the whole‐cell activity of designed compounds. Furthermore, we consolidate recent literature reports and encourage the further research on Mur enzymes.

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“…Due to the limitations in current techniques of prediction of efficacy and selectivity, drug design is still bounded to rationality and serendipity 81 . With the discovery of large molecules, the challenge becomes more obvious but with huge opportunities, however, it is still possible with advanced techniques if combined in accordance such as machine learning and artificial intelligence with advanced drug discovery methods like molecular dynamics simulation and QSAR 82 .…”

Section: Introductionmentioning

confidence: 99%

“…Recent development in protein-protein interaction and peptide docking has triggered the rapid development of computational approaches. So far, various drug discovery applications to handle peptide ligands have been designed which include virtual screening of inhibitors, predicting models of subangstrom-quality, prediction of specificity, experimental data interpretation and designing of peptides interfering with protein-protein interactions 80 – 82 . Figure 4 is depicting the level of complexity in the drug discovery process for large molecule therapeutics.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics

Yadav¹,

Eswari²

2022

AJMB

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Computer-aided drug designing is a promising approach to defeating the dry pipeline of drug discovery. It aims at reduced experimental efforts with cost-effectiveness. Naturally occurring large molecules with molecular weight higher than 500 Dalton such as cationic peptides, cyclic peptides, glycopeptides and lipopeptides are a few examples of large molecules which have successful applications as the broad spectrum antibacterial, anticancer, antiviral, antifungal and antithrombotic drugs. Utilization of microbial metabolites as potential drug candidates incur cost effectiveness through large scale production of such molecules rather than a synthetic approach. Computational studies on such compounds generate tremendous possibilities to develop novel leads with challenges to handle these complex molecules with available computational tools. The opportunities begin with the desired structural modifications in the parent drug molecule. Virtual modifications followed by molecular interaction studies at the target site through molecular modeling simulations and identification of structure-activity relationship models to develop more prominent and potential drug molecules. Lead optimization studies to develop novel compounds with increased specificity and reduced off targeting is a big challenge computationally for large molecules. Prediction of optimized pharmacokinetic properties facilitates development of a compound with lower toxicity as compared to the natural compounds. Generating the library of compounds and studies for target specificity and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) for large molecules are laborious and incur huge cost and chemical wastage through in-vitro methods. Hence, computational methods need to be explored to develop novel compounds from natural large molecules with higher specificity. This review article is focusing on possible challenges and opportunities in the pathway of computer-aided drug discovery of large molecule therapeutics.

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Computed insight into a peptide inhibitor preventing the induced fit mechanism of MurA enzyme from Pseudomonas aeruginosa

Cited by 11 publications

References 70 publications

Role of UDP‐N‐acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations

Role of UDP‐N‐acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations

Reaching toward underexplored targets in antibacterial drug design

Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics

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