Ensemble-Based Docking Using Biased Molecular Dynamics

Proteins are dynamic molecules, and understanding their movements, especially as they relate to molecular recognition and protein–ligand interactions, poses a significant challenge to structure-based drug discovery. In most instances, protein flexibility is underrepresented in computer-aided drug design due to uncertainties on how it should be accurately modeled as well as the computational cost associated with attempting to incorporate flexibility in the calculations. One approach that aims to address these issues is ensemble-based docking. With this technique, ligands are docked to an ensemble of rigid protein conformations. Molecular dynamics (MD) simulations can be used to generate the ensemble of protein conformations for the subsequent docking. Here we present a novel approach that uses biased-MD simulations to generate the docking ensemble. The MD simulations are biased toward an initial protein–ligand X-ray complex structure. The biasing maintains some of the original crystallographic pocket-ligand information and thereby enhances sampling of the more relevant conformational space of the protein. Resulting trajectories are clustered to select a representative set of protein conformations, and ligands are docked to that reduced set of conformations. Cross-docking to this ensemble and then selecting the lowest scoring pose enables reliable identification of the correct binding mode. Various levels of biasing are investigated, and the method is validated for cyclin-dependent kinase 2 and factor Xa

Evaluating Free Energies of Binding and Conservation of Crystallographic Waters Using SZMAP

The SZMAP method computes binding free energies and the corresponding thermodynamic components for water molecules in the binding site of a protein structure [SZMAP, 1.0.0; OpenEye Scientific Software Inc.: Santa Fe, NM, USA, 2011]. In this work, the ability of SZMAP to predict water structure and thermodynamic stability is examined for the X-ray crystal structures of a series of protein–ligand complexes. SZMAP results correlate with higher-level replica exchange thermodynamic integration double decoupling calculations of the absolute free energy of bound waters in the test set complexes. In addition, SZMAP calculations show good agreement with experimental data in terms of water conservation (across multiple crystal structures) and B-factors over a subset of the test set. In particular, the SZMAP neutral entropy difference term calculated at crystallographic water positions within each of the complex structures correlates well with whether that crystallographic water is conserved or displaceable. Furthermore, the calculated entropy of the water probe relative to the continuum shows a significant degree of correlation with the B-factors associated with the oxygen atoms of the water molecules. Taken together, these results indicate that SZMAP is capable of quantitatively predicting water positions and their energetics and is potentially a useful tool for determining which waters to attempt to displace, maintain, or build in through water-mediated interactions when evolving a lead series during a drug discovery program

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment–lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein–ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment–lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein–ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment–lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein–ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment–lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein–ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu

<i>In Vivo</i> Validation of Thymidylate Kinase (TMK) with a Rationally Designed, Selective Antibacterial Compound

There is an urgent need for new antibacterials that pinpoint novel targets and thereby avoid existing resistance mechanisms. We have created novel synthetic antibacterials through structure-based drug design that specifically target bacterial thymidylate kinase (TMK), a nucleotide kinase essential in the DNA synthesis pathway. A high-resolution structure shows compound TK-666 binding partly in the thymidine monophosphate substrate site, but also forming new induced-fit interactions that give picomolar affinity. TK-666 has potent, broad-spectrum Gram-positive microbiological activity (including activity against methicillin-resistant <i>Staphylococcus aureus</i> and vancomycin-resistant <i>Enterococcus</i>), bactericidal action with rapid killing kinetics, excellent target selectivity over the human ortholog, and low resistance rates. We demonstrate <i>in vivo</i> efficacy against <i>S. aureus</i> in a murine infected-thigh model. This work presents the first validation of TMK as a compelling antibacterial target and provides a rationale for pursuing novel clinical candidates for treating Gram-positive infections through TMK