Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase

Avery, Chris; Baker, Lonnie; Jacobs, Donald J.

doi:10.3390/e24050729

Cited by 3 publications

(4 citation statements)

References 58 publications

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“…The compounds present interaction, primarily, with the enzymes' binding site through the residues LYS73, TYR105, SER130, ASN132, ASN170, VAL216, LYS234, ALA237, and ARG244. Interactions with the important residue LYS73 further potentiates the inhibition capabilities of the extract-hydrogen bond interactions-can be observed, notably formed with SER130, ASN132, and ALA237, with some hydrophobic interactions also observed; this may contribute to their better compatibility in the enzyme's binding pocket [49]. Metallo β-lactamases are notorious for their ability to hydrolyse a wide class of b-lactam drugs, including carbapenems.…”

Section: Discussionmentioning

confidence: 99%

In Vitro Antioxidant and In Silico Evaluation of the Anti-β-Lactamase Potential of the Extracts of Cylindrospermum alatosporum NR125682 and Loriellopsis cavenicola NR117881

Ikhane,

Sithole,

Cele

et al. 2024

Antioxidants

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Cyanobacteria in recent times have been touted to be a suitable source for the discovery of novel compounds, including antioxidants and antibiotics, due to their large arsenal of metabolites. This study presents the in vitro antioxidant and in silico evaluation of Cylindrospermum alatosporum NR125682 and Loriellopsis cavenicola NR117881, isolated from freshwater ponds around the campus of the University of Zululand, South Africa. The isolates were confirmed using 16S rRNA. Various crude extracts of the isolated microbes were prepared through sequential extraction using hexane, dichloromethane, and 70% ethanol. The chemical constituents of the crude extracts were elucidated by FTIR and GC-MS spectroscopy. The antioxidant potential of the extracts was determined by the free radical (DPPH, ABTS, •OH, and Fe2+) systems. Molecular docking of the major constituents of the extracts against β-lactamase was also evaluated. GC-MS analysis indicated the dominating presence of n-alkanes. The extracts exhibited varying degrees of antioxidant activity (scavenging of free radicals; an IC50 range of 8–10 µg/mL was obtained for ABTS). A good binding affinity (−6.6, −6.3 Kcal/mol) of some the organic chemicals (diglycerol tetranitrate, and 2,2-dimethyl-5-(3-methyl-2-oxiranyl)cyclohexanone) was obtained following molecular docking. The evaluated antioxidant activities, coupled with the obtained docking score, potentiates the antimicrobial activity of the extracts.

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Section: Discussionmentioning

confidence: 99%

In Vitro Antioxidant and In Silico Evaluation of the Anti-β-Lactamase Potential of the Extracts of Cylindrospermum alatosporum NR125682 and Loriellopsis cavenicola NR117881

Ikhane,

Sithole,

Cele

et al. 2024

Antioxidants

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“…The target for learning is fully defined by the data presented, rather than underlying assumptions [ 319 ] such as variance in PCA. The method was demonstrated by describing the dynamic differences in multiple TEM beta-lactamase, which explain differences in enzyme efficiency among several ligands [ 302 ].…”

Section: Selected Applications Of Machine Learning In Computational B...mentioning

confidence: 99%

“…In addition, if a protein is mutated, its sensitivity in vibrational characteristics is likely to change. As such, like other functional mechanisms, dynamic allostery is affected strongly by evolution as noted in beta-lactamase [ 302 ]. It is computationally feasible for methods detecting dynamic allostery to be extended to design proteins with planned allostery communication signals.…”

Section: Selected Applications Of Machine Learning In Computational B...mentioning

confidence: 99%

“…The unbound structure ( a ) binds to calcium ions ( b ), then the resulting structure is able to bind with a substrate ( c ) [ 300 ]. On the bottom row, ( d , e ) show the native state motions of proteins including surface loop flexibility [ 301 , 302 ] and concerted domain fluctuations [ 303 ]. ( f ) A visual of dynamic allostery pathways resulting from changes to the vibrational modes of a protein upon binding a ligand [ 304 ].…”

Section: Figurementioning

confidence: 99%

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Protein Function Analysis through Machine Learning

et al. 2022

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Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

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