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1. The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

   https://doi.org/10.3390/ijms22062895  

   Kolbaba-Kartchner, Bethany; Kazan, I. Can; Mills, Jeremy H.; Ozkan, S. Banu (March 2021, International Journal of Molecular Sciences)

   null (Ed.)

   The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes. 

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2. Penicillin-binding protein redundancy in Bacillus subtilis enables growth during alkaline shock

   https://doi.org/10.1128/aem.00548-23  

   Mitchell, Stephanie L.; Kearns, Daniel B.; Carlson, Erin E. (December 2023, Applied and Environmental Microbiology)

   Bose, Arpita (Ed.)

   ABSTRACT Penicillin-binding proteins (PBPs) play critical roles in cell wall construction, cell shape maintenance, and bacterial replication. Bacteria maintain a diversity of PBPs, indicating that despite their apparent functional redundancy, there is differentiation across the PBP family. Apparently-redundant proteins can be important for enabling an organism to cope with environmental stressors. In this study, we evaluated the consequence of environmental pH on PBP enzymatic activity inBacillus subtilis. Our data show that a subset of PBPs inB. subtilischange activity levels during alkaline shock and that one PBP isoform is rapidly modified to generate a smaller protein (i.e., PBP1a to PBP1b). Our results indicate that a subset of the PBPs are favored for growth under alkaline conditions, while others are readily dispensable. Indeed, we found that this phenomenon could also be observed inStreptococcus pneumoniae, implying that it may be generalizable across additional bacterial species and further emphasizing the evolutionary benefit of maintaining many, seemingly-redundant periplasmic enzymes. IMPORTANCEMicrobes adapt to ever-changing environments and thrive over a vast range of conditions. While bacterial genomes are relatively small, significant portions encode for “redundant” functions. Apparent redundancy is especially pervasive in bacterial proteins that reside outside of the inner membrane. While conditions within the cytoplasm are carefully controlled, those of the periplasmic space are largely determined by the cell’s exterior environment. As a result, proteins within this environmentally exposed region must be capable of functioning under a vast array of conditions, and/or there must be several similar proteins that have evolved to function under a variety of conditions. This study examines the activity of a class of enzymes that is essential in cell wall construction to determine if individual proteins might be adapted for activity under particular growth conditions. Our results indicate that a subset of these proteins are preferred for growth under alkaline conditions, while others are readily dispensable. 

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3. ROCker Models for Reliable Detection and Typing of Short-Read Sequences Carrying β-Lactamase Genes

   https://doi.org/10.1128/msystems.01281-21  

   Zhang, Si-Yu; Suttner, Brittany; Rodriguez-R, Luis M.; Orellana, Luis H.; Conrad, Roth E.; Liu, Fang; Rowell, Jessica L.; Webb, Hattie E.; Williams-Newkirk, Amanda J.; Huang, Andrew; et al (June 2022, mSystems)

   Marshall, Christopher W. (Ed.)

   ABSTRACT Identification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL homologs can be frequently missed during similarity searches, which has important practical consequences for monitoring antibiotic resistance. To address this limitation, we built ROCker models that targeted broad classes (e.g., class A, B, C, and D) and individual families (e.g., TEM) of BLs and challenged them with mock 150-bp- and 250-bp-read data sets of known composition. ROCker identifies most-discriminant bit score thresholds in sliding windows along the sequence of the target protein sequence and hence can account for nondiscriminative domains shared by unrelated proteins. BL ROCker models showed a 0% false-positive rate (FPR), a 0% to 4% false-negative rate (FNR), and an up-to-50-fold-higher F1 score [2 × precision × recall/(precision + recall)] compared to alternative methods, such as similarity searches using BLASTx with various e-value thresholds and BL hidden Markov models, or tools like DeepARG, ShortBRED, and AMRFinder. The ROCker models and the underlying protein sequence reference data sets and phylogenetic trees for read placement are freely available through http://enve-omics.ce.gatech.edu/data/rocker-bla . Application of these BL ROCker models to metagenomics, metatranscriptomics, and high-throughput PCR gene amplicon data should facilitate the reliable detection and quantification of BL variants encoded by environmental or clinical isolates and microbiomes and more accurate assessment of the associated public health risk, compared to the current practice. IMPORTANCE Resistance genes encoding β-lactamases (BLs) confer resistance to the widely prescribed antibiotic class β-lactams. Therefore, it is important to assess the prevalence of BL genes in clinical or environmental samples for monitoring the spreading of these genes into pathogens and estimating public health risk. However, detecting BLs in short-read sequence data is technically challenging. Our ROCker model-based bioinformatics approach showcases the reliable detection and typing of BLs in complex data sets and thus contributes toward solving an important problem in antibiotic resistance surveillance. The ROCker models developed substantially expand the toolbox for monitoring antibiotic resistance in clinical or environmental settings. 

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4. UNRAVELING MOLECULAR MECHANISMS OF ANTIBIOTIC RESISTANCE THROUGH MULTISCALE SIMULATIONS AND EXPLAINABLE MACHINE LEARNING

   Song, Zilin (May 2022, SMU Scholar)

   Pathogen resistance to β-lactam antibiotics compromises effective treatments of superbug infections. One major source of β-lactam resistance is the bacterial production of β-lactamases, which could effectively hydrolyze β-lactam drugs. In this thesis, the hydrolysis of various β-lactam antibiotics by class A serine-based β-lactamases (ASβLs) were investigated using hybrid Quantum Mechanical / Molecular Mechanical (QM/MM) minimum energy pathway (MEP) calculations and explainable machine learning (ML) approaches. The TEM-1/benzylpenicillin acylation reaction with QM/MM chain-of-states reaction pathways was firstly revisited. I proposed two decomposition methods for energy contribution analysis based on perturbing ML regression models. Both methods were shown to be model implementation invariant and successfully bridged the discrepancies between two pioneering mechanistic studies. The Toho-1 ASβL acylations of ampicillin and cefalexin were then investigated. I reported that the acylation pathway selection can be ligand dependent: ampicillin could undergo acylation via Lys73 or Glu166 acting as the general base while cefalexin acylation is limited to Lys73 as the general base. An explainable artificial intelligence (XAI) method, the Boltzmann-weighted Cumulative Integrated Gradients (BCIG), was developed to explain the different acylation pathway viability found for ampicillin and cefalexin. Lastly, conformational factors determining the GES-5/imipenem deacylation activity was investigated using edge-conditioned convolutional graph-learning (GL) methods. Critical mechanistic insights were derived from perturbative response of the GL latent representations, which explained the different deacylation reactivity between the two imipenem pyrroline tautomer states and identified the orientation of the carbapenem 6α-hydroxyethyl as the key factor that impacts the deacylation barrier heights. In summary, my thesis focuses on bridging QM/MM chain-of-states reaction pathway calculations and explainable ML to derive essential mechanistic insights into β-lactam resistance driven by ASβLs. 

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5. Interplay of cysteine exposure and global protein dynamics in small‐molecule recognition by a regulator of G‐protein signaling protein

   https://doi.org/10.1002/prot.25642  

   Mohammadi, Mohammadjavad; Mohammadiarani, Hossein; Shaw, Vincent S.; Neubig, Richard R.; Vashisth, Harish (December 2018, Proteins: Structure, Function, and Bioinformatics)

   Abstract Regulator of G protein signaling (RGS) proteins play a pivotal role in regulation of G protein‐coupled receptor (GPCR) signaling and are therefore becoming an increasingly important therapeutic target. Recently discovered thiadiazolidinone (TDZD) compounds that target cysteine residues have shown different levels of specificities and potencies for the RGS4 protein, thereby suggesting intrinsic differences in dynamics of this protein upon binding of these compounds. In this work, we investigated using atomistic molecular dynamics (MD) simulations the effect of binding of several small‐molecule inhibitors on perturbations and dynamical motions in RGS4. Specifically, we studied two conformational models of RGS4 in which a buried cysteine residue is solvent‐exposed due to side‐chain motions or due to flexibility in neighboring helices. We found that TDZD compounds with aromatic functional groups perturb the RGS4 structure more than compounds with aliphatic functional groups. Moreover, small‐molecules with aromatic functional groups but lacking sulfur atoms only transiently reside within the protein and spontaneously dissociate to the solvent. We further measured inhibitory effects of TDZD compounds using a protein–protein interaction assay on a single‐cysteine RGS4 protein showing trends in potencies of compounds consistent with our simulation studies. Thermodynamic analyses of RGS4 conformations in the apo‐state and on binding to TDZD compounds revealed links between both conformational models of RGS4. The exposure of cysteine side‐chains appears to facilitate initial binding of TDZD compounds followed by migration of the compound into a bundle of four helices, thereby causing allosteric perturbations in the RGS/Gα protein–protein interface. 

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