Structure-based approach for identification of novel phenylboronic acids as serine-β-lactamase inhibitors

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Development of AMBER Parameters for Molecular Simulations of Selected Boron-Based Covalent Ligands

Boron containing compounds (BCCs) aroused increasing interest in the scientific community due to their wide application as drugs in various fields. In order to design new compounds hopefully endowed with pharmacological activity and also investigate their conformational behavior, the support of computational studies is crucial. Nevertheless, the suitable molecular mechanics parameterization and the force fields needed to perform these simulations are not completely available for this class of molecules. In this paper, Amber force field parameters for phenyl-, benzyl-, benzylamino-, and methylamino-boronates, a group of boron-containing compounds involved in different branches of the medicinal chemistry, were created. The robustness of the obtained data was confirmed through molecular dynamics simulations on ligand/β-lactamases covalent complexes. The ligand torsional angles, populated over the trajectory frames, were confirmed by values found in the ligand geometries, located through optimizations at the DFT/B3LYP/6-31g(d) level, using water as a solvent. In summary, this study successfully provided a library of parameters, opening the possibility to perform molecular dynamics simulations of this class of boron-containing compounds.

Comparison of the Performance of Phenotypic Methods for the Detection of Carbapenem-Resistant Enterobacteriaceae (CRE) in Clinical Practice

In order to investigate the diagnostic performance characteristics of four phenotypic assays in detecting carbapenem-resistant Enterobacteriaceae (CRE), we collected the CRE strains from infected patients. The results of carbapenemase gene detection, bla_KPC-2, bla_OXA-23, bla_NDM-1, bla_NDM-4, bla_NDM-5, bla_IMP-4, and bla_IMP-8, were used as a standard to evaluate the performances of combined disk test (CDT), modified carbapenem inactivation method(mCIM)/EDTA-modified carbapenem inactivation method(eCIM), NG-Test CARBA 5 (CARBA), and color developing immunoassay (CDI). The compliance of phenotype results based on CDT, mCIM/eCIM, CARBA, and CDI with genetic detection results was 94% (231/247), 95% (235/247), 98% (242/247), and 99% (246/247), respectively. CDT demonstrated a low specificity for carbapenemase detection, low negative predictive value (NPV), and low sensitivity for metallo-β-lactamase (79%, 55%, and 88%, respectively); it also failed to accurately detect IMP. The mCIM/eCIM assay had serious problems in detecting OXA-23-like carbapenemases. The sensitivity and specificity of CARBA and CDI were higher than those of the first two methods. However, CARBA did not cover the detection of OXA-23, while CDI cannot detect IMP-8, resulting in low NPVs (70% and 88%, respectively). In conclusion, CARBA and CDI assays are highly accurate except individual rare genes and allow direct genotype detections. CDT and mCIM/eCIM assays are moderately accurate and can only distinguish serine-β-lactamases from metallo-β-lactamases. Laboratories should choose the appropriate method that meets their needs based on its characteristic.

Kinetic and Structural Characterization of the First B3 Metallo-β-Lactamase with an Active Site Glutamic Acid

The structural diversity in metallo-β-lactamases (MBLs), especially in the vicinity of the active site, has been a major hurdle in the development of clinically effective inhibitors. Representatives from three variants of the B3 MBL subclass, containing either the canonical HHH/DHH active site motif (present in the majority of MBLs in this subclass) or the QHH/DHH (B3-Q) or HRH/DQK (B3-RQK) variations were reported previously. Here, we describe the structure and kinetic properties of the first example (SIE-1) of a fourth variant containing the EHH/DHH active site motif (B3-E). SIE-1 was identified in the hexachlorocyclohexane-degrading bacterium Sphingobium indicum, and kinetic analyses demonstrate that although it is active against a wide range of antibiotics its efficiency is lower than that of other B3 MBLs, but with improved efficiency towards cephalosporins relative to other β-lactam substrates. The overall fold of SIE-1 is characteristic of the MBLs; the notable variation is observed in the Zn1 site due to the replacement of the canonical His116 by a glutamate. The unusual preference of SIE-1 for cephalosporins and its occurrence in a widespread environmental organism suggests scope for increased MBL-mediated β-lactam resistance. It is thus relevant to include SIE-1 into MBL inhibitor design studies to widen the therapeutic scope of much needed anti-resistance drugs.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Can multiscale simulations unravel the function of metallo-enzymes to improve knowledge-based drug discovery?

Metallo-enzymes are a large class of biomolecules promoting specialized chemical reactions. Quantum-classical quantum mechanics/molecular mechanics molecular dynamics, describing the metal site at quantum mechanics level, while accounting for the rest of system at molecular mechanics level, has an accessible time-scale limited by its computational cost. Hence, it must be integrated with classical molecular dynamics and enhanced sampling simulations to disentangle the functions of metallo-enzymes. In this review, we provide an overview of these computational methods and their capabilities. In particular, we will focus on some systems such as CYP19A1 a Fe-dependent enzyme involved in estrogen biosynthesis, and on Mg2+-dependent DNA/RNA processing enzymes/ribozymes and the spliceosome, a protein-directed ribozyme. This information may guide the discovery of drug-like molecules and genetic manipulation tools.

Docking of Covalent Ligands: Challenges and Approaches

Covalent ligands have recently regained considerable attention in drug discovery. The rational design of such ligands, however, is still faced with particular challenges, mostly related to the fact that covalent bond formation is a quantum mechanical phenomenon which cannot adequately be handled by the force fields or empirical approaches typically used for noncovalent protein-ligand interactions. Although the necessity for quantum chemical approaches is clear, they cannot yet routinely be applied on large data sets of ligands or for a broader exploration of binding modes in docking calculations. On the other hand, technical solutions for performing docking calculations with covalent ligands are available, but their scope is normally quite limited. Scoring functions typically neglect the contribution from covalent bond formation completely. In this situation, the question arises how to approach covalent ligands and which methods to choose for their docking and design.

Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Binding affinity of a small ligand to a receptor is the important quantity in drug design, and it might be characterized by different quantities. The most popular one is the binding free energy, which can be estimated by several methods in conventional molecular dynamics simulation. So far in steered molecular dynamics (SMD), one can use either the rupture force or nonequilibrium pulling work as a measure for binding affinity. In this paper, we have shown that the nonequilibrium binding free energy Δ G_neq^Jar, obtained by Jarzynski's equality at a finite pulling speed, has good correlation with experimental data on inhibition constants, implying that this quantity can be used as a good scoring function for binding affinity. A similar correlation has also been disclosed for binding and unbinding free energy barriers. Applying the SMD method to unbinding of 23 small compounds from the binding site of β-lactamase protein, a bacteria-produced enzyme, we have demonstrated that the rupture or unbinding time strongly correlates with experimental data with correlation level R ≈ 0.84. As follows from the Jarzynski's equality, the rupture time depends on the unbinding barrier exponentially. We show that Δ G_neq^Jar, the rupture time, and binding and unbinding free energy barriers are good descriptors for binding affinity. Our observation may be useful for fast screening of potential leads as the SMD simulation is not time-consuming. On the basis of nonequilibrium simulation, we disclosed that, in agreement with the experiment, the binding time is much longer than the unbinding one.