Relationship between chiroptical properties, structural changes and interactions in enzymes: A computational study on β-lactamases from class A

2008 ◽  
Vol 32 (3) ◽  
pp. 167-175 ◽  
Author(s):  
Christo Z. Christov ◽  
Tatyana G. Karabencheva ◽  
Alessio Lodola
2014 ◽  
Vol 16 (27) ◽  
pp. 14220-14230 ◽  
Author(s):  
M. Horch ◽  
A. F. Pinto ◽  
T. Utesch ◽  
M. A. Mroginski ◽  
C. V. Romão ◽  
...  

Local and global structural changes that enable reductive activation of superoxide reductase are revealed by a combined approach of infrared difference spectroscopy and computational methods.


2015 ◽  
Vol 119 (36) ◽  
pp. 20801-20809 ◽  
Author(s):  
M. B. Smirnov ◽  
E. M. Roginskii ◽  
V. Yu. Kazimirov ◽  
K. S. Smirnov ◽  
R. Baddour-Hadjean ◽  
...  

2017 ◽  
Vol 1 (S1) ◽  
pp. 12-12
Author(s):  
Pinaki Sarder ◽  
Rabi Yacoub ◽  
John E. Tomaszewski

OBJECTIVES/SPECIFIC AIMS: (i) Digitally quantify pathologically relevant glomerular microcompartmental structures in murine renal tissue histopathology images. (ii) Digitally model disease trajectory in a mouse model of diabetic nephropathy (DN). METHODS/STUDY POPULATION: We have developed a computational pipeline for glomerular structural compartmentalization based on Gabor filtering and multiresolution community detection (MCD). The MCD method employs improved, efficient optimization of a Potts model Hamiltonian, adopted from theoretical physics, modeling interacting electron spins. The method is parameter-free and capable of simultaneously selecting relevant structure at all biologically relevant scales. It can segment glomerular compartments from a large image containing hundreds of glomeruli in seconds for quantification—which is not possible manually. We will analyze the performance of our computational pipeline in healthy and streptozotocin induced DN mice using renal tissue images, and model the structural distributions of automatically quantified glomerular features as a function of DN progression. The performance of this structural-disease model will be compared with existing visual quantification methods used by pathologists in the clinic. RESULTS/ANTICIPATED RESULTS: Computational modeling will reveal digital biomarkers for early proteinuria in DN, able to predict disease trajectory with greater precision and accuracy than manual inspection alone. DISCUSSION/SIGNIFICANCE OF IMPACT: Automated detection of microscopic structural changes in renal tissue will eventually lead to objective, standardized diagnosis, reflecting cost savings for DN through discovery of digital biomarkers hidden within numerical structural distributions. This computational study will pave the path for the creation of new digital tools which provide clinicians invaluable quantitative information about expected patient disease trajectory, enabling earlier clinical predictions and development of early therapeutic interventions for kidney diseases.


Chirality ◽  
2005 ◽  
Vol 17 (9) ◽  
pp. 577-589 ◽  
Author(s):  
Chiara Cappelli ◽  
Simona Bronco ◽  
Susanna Monti

2020 ◽  
Vol 7 ◽  
Author(s):  
Thinh-Phat Cao ◽  
Hyojeong Yi ◽  
Immanuel Dhanasingh ◽  
Suparna Ghosh ◽  
Jin Myung Choi ◽  
...  

Despite class A ESBLs carrying substitutions outside catalytic regions, such as Cys69Tyr or Asn136Asp, have emerged as new clinical threats, the molecular mechanisms underlying their acquired antibiotics-hydrolytic activity remains unclear. We discovered that this non-catalytic-region (NCR) mutations induce significant dislocation of β3-β4 strands, conformational changes in critical residues associated with ligand binding to the lid domain, dynamic fluctuation of Ω-loop and β3-β4 elements. Such structural changes increase catalytic regions’ flexibility, enlarge active site, and thereby accommodate third-generation cephalosporin antibiotics, ceftazidime (CAZ). Notably, the electrostatic property around the oxyanion hole of Cys69Tyr ESBL is significantly changed, resulting in possible additional stabilization of the acyl-enzyme intermediate. Interestingly, the NCR mutations are as effective for antibiotic resistance by altering the structure and dynamics in regions mediating substrate recognition and binding as single amino-acid substitutions in the catalytic region of the canonical ESBLs. We believe that our findings are crucial in developing successful therapeutic strategies against diverse class A ESBLs, including the new NCR-ESBLs.


1998 ◽  
Vol 330 (2) ◽  
pp. 581-598 ◽  
Author(s):  
André MATAGNE ◽  
Josette LAMOTTE-BRASSEUR ◽  
Jean-Marie FRÈRE

β-Lactamases are the main cause of bacterial resistance to penicillins, cephalosporins and related β-lactam compounds. These enzymes inactivate the antibiotics by hydrolysing the amide bond of the β-lactam ring. Class A β-lactamases are the most widespread enzymes and are responsible for numerous failures in the treatment of infectious diseases. The introduction of new β-lactam compounds, which are meant to be ‘β-lactamase-stable’ or β-lactamase inhibitors, is thus continuously challenged either by point mutations in the ubiquitous TEM and SHV plasmid-borne β-lactamase genes or by the acquisition of new genes coding for β-lactamases with different catalytic properties. On the basis of the X-ray crystallography structures of several class A β-lactamases, including that of the clinically relevant TEM-1 enzyme, it has become possible to analyse how particular structural changes in the enzyme structures might modify their catalytic properties. However, despite the many available kinetic, structural and mutagenesis data, the factors explaining the diversity of the specificity profiles of class A β-lactamases and their amazing catalytic efficiency have not been thoroughly elucidated. The detailed understanding of these phenomena constitutes the cornerstone for the design of future generations of antibiotics.


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