Biophysical Approaches Determining Ligand Binding to Biomolecular Targets

Keyword(s):  
2019 ◽  
Vol 476 (21) ◽  
pp. 3141-3159 ◽  
Author(s):  
Meiru Si ◽  
Can Chen ◽  
Zengfan Wei ◽  
Zhijin Gong ◽  
GuiZhi Li ◽  
...  

Abstract MarR (multiple antibiotic resistance regulator) proteins are a family of transcriptional regulators that is prevalent in Corynebacterium glutamicum. Understanding the physiological and biochemical function of MarR homologs in C. glutamicum has focused on cysteine oxidation-based redox-sensing and substrate metabolism-involving regulators. In this study, we characterized the stress-related ligand-binding functions of the C. glutamicum MarR-type regulator CarR (C. glutamicum antibiotic-responding regulator). We demonstrate that CarR negatively regulates the expression of the carR (ncgl2886)–uspA (ncgl2887) operon and the adjacent, oppositely oriented gene ncgl2885, encoding the hypothetical deacylase DecE. We also show that CarR directly activates transcription of the ncgl2882–ncgl2884 operon, encoding the peptidoglycan synthesis operon (PSO) located upstream of carR in the opposite orientation. The addition of stress-associated ligands such as penicillin and streptomycin induced carR, uspA, decE, and PSO expression in vivo, as well as attenuated binding of CarR to operator DNA in vitro. Importantly, stress response-induced up-regulation of carR, uspA, and PSO gene expression correlated with cell resistance to β-lactam antibiotics and aromatic compounds. Six highly conserved residues in CarR were found to strongly influence its ligand binding and transcriptional regulatory properties. Collectively, the results indicate that the ligand binding of CarR induces its dissociation from the carR–uspA promoter to derepress carR and uspA transcription. Ligand-free CarR also activates PSO expression, which in turn contributes to C. glutamicum stress resistance. The outcomes indicate that the stress response mechanism of CarR in C. glutamicum occurs via ligand-induced conformational changes to the protein, not via cysteine oxidation-based thiol modifications.


1975 ◽  
Vol 80 (1_Suppla) ◽  
pp. S15
Author(s):  
K. H. Rudorff ◽  
H. J. Kröll ◽  
J. Herrmann

2002 ◽  
Vol 76 (6) ◽  
pp. 606 ◽  
Author(s):  
Takahiro Hirano ◽  
In Taek Lim ◽  
Don Moon Kim ◽  
Xiang-Guo Zheng ◽  
Kazuo Yoshihara ◽  
...  

2020 ◽  
Author(s):  
Jeffrey Mendenhall ◽  
Benjamin Brown ◽  
Sandeepkumar Kothiwale ◽  
Jens Meiler

<div>This paper describes recent improvements made to the BCL::Conf rotamer generation algorithm and comparison of its performance against other freely available and commercial conformer generation software. We demonstrate that BCL::Conf, with the use of rotamers derived from the COD, more effectively recovers crystallographic ligand-binding conformations seen in the PDB than other commercial and freely available software. BCL::Conf is now distributed with the COD-derived rotamer library, free for academic use. The BCL can be downloaded at <a href="http://meilerlab.org/index.php/bclcommons/show/b_apps_id/1">http://meilerlab.org/ bclcommons</a> for Windows, Linux, or Apple operating systems.<br></div>


2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>


2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>


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