Outsmarting metallo-β-lactamases by mimicking their natural evolution

Oelschlaeger, Peter

doi:10.1016/j.jinorgbio.2008.05.007

Cited by 20 publications

(18 citation statements)

References 111 publications

(183 reference statements)

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“…Several groups 24,28,30–32,41 use the van Hove correlation function , P (Δ x (τ)), 42 which is the probability distribution function constructed from the observed increments or displacements, Δ x , at lag time τ, where

normalΔ x false(τ false) = x false(t + τ false) - x false(t false) .

For the majority of relevant stationary, stochastic increment processes that have been used to model PPTM, including normal diffusion, fractional Brownian motion, and generalized Langevin equations, the corresponding van Hove correlation function is Gaussian for each fixed set of model parameters. Paths generated from any of these classical stochastic processes can be considered homogeneous if they arise from the same set of model parameters, or within some small neighborhood of a parameter set.…”

Section: Current Metrics To Detect Heterogeneity In Pptm Datamentioning

confidence: 99%

“…While analysis of particle paths that violate this assumption pose an additional mathematical challenge, the results can provide insight into temporal or spatial dependencies in a particle’s dynamics. Transient behavior has been observed in a wide range of biological settings, including the movement of secretory vesicles, 41 viruses 26 and membrane proteins, 48,49 and multiple approaches exist for the identification and characterization of non-stationary behavior. 25,50 In this paper, we focus on the analysis of paths exhibiting stationary dynamics.…”

Section: Current Metrics To Detect Heterogeneity In Pptm Datamentioning

confidence: 99%

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Micro-heterogeneity metrics for diffusion in soft matter

et al. 2014

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Passive particle tracking of diffusive paths in soft matter, coupled with analysis of the path data, is firmly established as a fundamental methodology for characterization of both diffusive transport properties (the focus here) and linear viscoelasticity. For either focus, particle time series are typically analyzed by ensemble averaging over paths, a perfectly natural protocol for homogeneous materials or for applications where mean properties are sufficient. Many biological materials, however, are heterogeneous over length scales above the probe diameter, and the implications of heterogeneity for biologically relevant transport properties (e.g. diffusive passage times through a complex fluid layer) motivate this paper. Our goals are three-fold: first, to detect heterogeneity as reflected by the ensemble path data; second, to further decompose the ensemble of particle paths into statistically distinct clusters; and third, to fit the path data in each cluster to a model for the underlying stochastic process. After reviewing current best practices for detection and assessment of heterogeneity in diffusive processes, we introduce our strategy toward the first two goals with methods from the statistics and machine learning literature that have not found application thus far to passive particle tracking data. We apply an analysis based solely on the path data that detects heterogeneity and yields a decomposition of particle paths into statistically distinct clusters. After these two goals are achieved, one can then pursue model-fitting. We illustrate these heterogeneity metrics on diverse datasets: for numerically generated and experimental particle paths, with tunable and unknown heterogeneity, on numerical models for simple diffusion and anomalous sub-diffusion, and experimentally on sucrose, hyaluronic acid, agarose, and human lung culture mucus solutions.

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normalΔ x false(τ false) = x false(t + τ false) - x false(t false) .

Section: Current Metrics To Detect Heterogeneity In Pptm Datamentioning

confidence: 99%

Section: Current Metrics To Detect Heterogeneity In Pptm Datamentioning

confidence: 99%

Micro-heterogeneity metrics for diffusion in soft matter

et al. 2014

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“…MBLs have caused major concerns due to their efficient inactivation of most β-lactams [16]–[18] and the absence of clinically useful inhibitors [16], [19]. A better understanding of the sequence-function relationship of these enzymes and the mechanism of how mutations are acquired under the selective pressure of antibiotics and MBL inhibitors is crucial for the design of novel antibacterial drugs with long-term efficacy [20]. New MBL variants are discovered at a steady pace, making more and more information about protein variants available.…”

Section: Introductionmentioning

confidence: 99%

Protein Variants Form a System of Networks: Microdiversity of IMP Metallo-Beta-Lactamases

Widmann

Pleiss

2014

PLoS ONE

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Genome and metagenome sequencing projects support the view that only a tiny portion of the total protein microdiversity in the biosphere has been sequenced yet, while the vast majority of existing protein variants is still unknown. By using a network approach, the microdiversity of 42 metallo-β-lactamases of the IMP family was investigated. In the networks, the nodes are formed by the variants, while the edges correspond to single mutations between pairs of variants. The 42 variants were assigned to 7 separate networks. By analyzing the networks and their relationships, the structure of sequence space was studied and existing, but still unknown, functional variants were predicted. The largest network consists of 10 variants with IMP-1 in its center and includes two ubiquitous mutations, V67F and S262G. By relating the corresponding pairs of variants, the networks were integrated into a single system of networks. The largest network also included a quartet of variants: IMP-1, two single mutants, and the respective double mutant. The existence of quartets indicates that if two mutations resulted in functional enzymes, the double mutant may also be active and stable. Therefore, quartet construction from triplets was applied to predict 15 functional variants. Further functional mutants were predicted by applying the two ubiquitous mutations in all networks. In addition, since the networks are separated from each other by 10–15 mutations on average, it is expected that a subset of the theoretical intermediates are functional, and therefore are supposed to exist in the biosphere. Finally, the network analysis helps to distinguish between epistatic and additive effects of mutations; while the presence of correlated mutations indicates a strong interdependency between the respective positions, the mutations V67F and S262G are ubiquitous and therefore background independent.

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“…In addition, they are often encoded on mobile genetic elements, which facilitate their horizontal transfer (45). A better understanding of the sequence-structure-function relationship of these enzymes and how mutations may be acquired under the selective pressure of antibiotics and MBL inhibitors (once they become available) is critical for the design of novel antibacterial drugs with long-term efficacy (29). One important step toward better understanding these processes is to systematically analyze the current knowledge on MBL gene nucleotide sequences, MBL amino acid sequences, MBL structures, and MBL activities found in databases and in the scientific literature.…”

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confidence: 99%

Systematic Analysis of Metallo-β-Lactamases Using an Automated Database

Widmann

Pleiss

Oelschlaeger

2012

Antimicrob Agents Chemother

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b Metallo-␤-lactamases (MBLs) are enzymes that hydrolyze ␤-lactam antibiotics, resulting in bacterial resistance to these drugs. These proteins have caused concerns due to their facile transference, broad substrate spectra, and the absence of clinically useful inhibitors. To facilitate the classification, nomenclature, and analysis of MBLs, an automated database system was developed, the Metallo-␤-Lactamase Engineering Database (MBLED) (http://www.mbled.uni-stuttgart.de). It contains information on MBLs retrieved from the NCBI peptide database while strictly following the nomenclature by Jacoby and Bush (http://www.lahey.org /Studies/) and the generally accepted class B ␤-lactamase (BBL) standard numbering scheme for MBLs. The database comprises 597 MBL protein sequences and enables systematic analyses of these sequences. A systematic analysis employing the database resulted in the generation of mutation profiles of assigned IMP-and VIM-type MBLs, the identification of five MBL protein entries from the NCBI peptide database that were inconsistent with the Jacoby and Bush nomenclature, and the identification of 15 new IMP candidates and 9 new VIM candidates. Furthermore, the database was used to identify residues with high mutation frequencies and variability (mutation hot spots) that were unexpectedly distant from the active site located in the ␤␤ sandwich: positions 208 and 266 in the IMP family and positions 215 and 258 in the VIM family. We expect that the MBLED will be a valuable tool for systematically cataloguing and analyzing the increasing number of MBLs being reported.

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Outsmarting metallo-β-lactamases by mimicking their natural evolution

Cited by 20 publications

References 111 publications

Micro-heterogeneity metrics for diffusion in soft matter

Micro-heterogeneity metrics for diffusion in soft matter

Protein Variants Form a System of Networks: Microdiversity of IMP Metallo-Beta-Lactamases

Systematic Analysis of Metallo-β-Lactamases Using an Automated Database

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