Pharmacophore Fingerprint-Based Approach to Binding Site Subpocket Similarity and Its Application to Bioisostere Replacement

Bioisosteres have been defined as structurally different molecules or substructures that can form comparable intermolecular interactions, and therefore, fragments that bind to similar protein structures exhibit a degree of bioisosterism. We present KRIPO (<u>K</u>ey <u>R</u>epresentation of <u>I</u>nteraction in <u>PO</u>ckets): a new method for quantifying the similarities of binding site subpockets based on pharmacophore fingerprints. The binding site fingerprints have been optimized to improve their performance for both intra- and interprotein family comparisons. A range of attributes of the fingerprints was considered in the optimization, including the placement of pharmacophore features, whether or not the fingerprints are fuzzified, and the resolution and complexity of the pharmacophore fingerprints (2-, 3-, and 4-point fingerprints). Fuzzy 3-point pharmacophore fingerprints were found to represent the optimal balance between computational resource requirements and the identification of potential replacements. The complete PDB was converted into a database comprising almost 300 000 optimized fingerprints of local binding sites together with their associated ligand fragments. The value of the approach is demonstrated by application to two crystal structures from the Protein Data Bank: (1) a MAP kinase P38 structure in complex with a pyridinylimidazole inhibitor (1A9U) and (2) a complex of thrombin with melagatran (1K22). Potentially valuable bioisosteric replacements for all subpockets of the two studied protein are identified

Kripo PDB Dec 2015

<p>KRIPO stands for Key Representation of Interaction in POckets.</p> <p>All fragments form all proteins-ligand complexes in PDB compared with all.<br> Data set contains PDB entries that where available at 23 December 2015.</p> <p>* Kripo.*.sqlite - Fragments sqlite database<br> * Distance matrix is too big to ship with VM so use http://3d-e-chem.vu-compmedchem.nl/kripodb webservice url to query.<br> * kripo_fingerprint_2015_*.fp.gz - Fragment fingerprints, see https://github.com/3D-e-Chem/kripodb/blob/master/README.md#create-distance-matrix-from-text-files for instructions how to convert to a distance matrix.</p> <p>Dataset was generated using http://dx.doi.org/10.5281/zenodo.53891</p> <p> </p

Selection of compounds fitted into the pharmacophore.

<p><b>A)</b> Fitting of agonist kaempferol (gray) into the pharmacophore features <b>0</b>, <b>3</b>, <b>5</b>, <b>6</b>, <b>8</b>. Residues, which make hydrogen bonds to the agonist, are shown as sticks. <b>B)</b> Fitting of the blocker 4’-fluoro-6-methoxyflavanone (S-enantiomer, blue) into the pharmacophore features <b>0</b>, <b>1</b>, <b>2</b>, <b>7</b>, <b>8</b>. Residues, which make hydrogen bonds (yellow dashes) to the blocker, are shown as sticks. <b>C)</b> Fitting of kaempferol (gray), luteolin (pink), naringenin (green), and epicatechin (cyan). <b>D)</b> Fitting of the kaempferol (gray) and 4’-fluoro-6-methoxyflavanone (blue). The structures of 4’-fluoro-6-methoxyflavanone, luteolin, naringenin, and epicatechin are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118200#pone.0118200.g004" target="_blank">Fig. 4</a></b>.</p

Best pharmacophore validation results per set and 5 or 6 feature combination.

<p>Best pharmacophore validation results per set and 5 or 6 feature combination.</p

Pharmacophore results plot.

<p>5 feature pharmacophore lab set (black x), 5 feature pharmacophore literature set (green circle), 5 featured combined set (magenta plus), 6 feature pharmacophore lab set (red circle), 6 feature pharmacophore literature set (cyan box) and 6 featured combined set (orange diamond).</p

Snooker pharmacophore features. Feature type, feature number, radius and residue contributions by amino acid number.

<p>Snooker pharmacophore features. Feature type, feature number, radius and residue contributions by amino acid number.</p

Homology model of the TM domains of hTAS2R39.

<p>TM I is depicted in dark blue, TM II in light blue, TM III in cyan, TM IV in light green, TM V in yellow, TM VI in orange, and TM VII in red. <b>A)</b> The Snooker pharmacophore hypothesis consists of acceptor features (numbers <b>0</b>, <b>1</b>, and <b>2</b> in gray), donor features (numbers <b>3</b>, <b>4</b>, and <b>5</b> in green), and hydrophobic features (numbers <b>6</b>, <b>7</b>, and <b>8</b> in magenta). Residues contributing to <b>B)</b> acceptor and donor features (<b>0</b>, <b>3</b>, and <b>5</b>) and <b>C)</b> hydrophobic features (<b>6</b>, and <b>8</b>) of the best performing feature combination are shown as sticks. All common rotamers are shown.</p

<i>In Silico</i> Identification and <i>in Vitro</i> Validation of Potential Cholestatic Compounds through 3D Ligand-Based Pharmacophore Modeling of BSEP Inhibitors

Drug-induced cholestasis is a frequently observed side effect of drugs and is often caused by an unexpected interaction with the bile salt export pump (BSEP/ABCB11). BSEP is the key membrane transporter responsible for the transport of bile acids from hepatocytes into bile. Here, we developed a pharmacophore model that describes the molecular features of compounds associated with BSEP inhibitory activity. To generate input and validation data sets, <i>in vitro</i> experiments with membrane vesicles overexpressing human BSEP were used to assess the effect of compounds (50 μM) on BSEP-mediated ³H-taurocholic acid transport. The model contains two hydrogen bond acceptor/anionic features, two hydrogen bond acceptor vector features, four hydrophobic/aromatic features, and exclusion volumes. The pharmacophore was validated against a set of 59 compounds, including registered drugs. The model recognized 9 out of 12 inhibitors (75%), which could not be identified based on general parameters, such as molecular weight or SlogP, alone. Finally, the model was used to screen a virtual compound database. A number of compounds found via virtual screening were tested and displayed statistically significant BSEP inhibition, ranging from 13 ± 1% to 67 ± 7% of control (<i>P</i> < 0.05). In conclusion, we developed and validated a pharmacophore model that describes molecular features found in BSEP inhibitors. The model may be used as an <i>in silico</i> screening tool to identify potentially harmful drug candidates at an early stage in drug development

Structural basis for recognition of the central conserved region of RSV G by neutralizing human antibodies

<div><p>Respiratory syncytial virus (RSV) is a major cause of severe lower respiratory tract infections in infants and the elderly, and yet there remains no effective treatment or vaccine. The surface of the virion is decorated with the fusion glycoprotein (RSV F) and the attachment glycoprotein (RSV G), which binds to CX3CR1 on human airway epithelial cells to mediate viral attachment and subsequent infection. RSV G is a major target of the humoral immune response, and antibodies that target the central conserved region of G have been shown to neutralize both subtypes of RSV and to protect against severe RSV disease in animal models. However, the molecular underpinnings for antibody recognition of this region have remained unknown. Therefore, we isolated two human antibodies directed against the central conserved region of RSV G and demonstrated that they neutralize RSV infection of human bronchial epithelial cell cultures in the absence of complement. Moreover, the antibodies protected cotton rats from severe RSV disease. Both antibodies bound with high affinity to a secreted form of RSV G as well as to a peptide corresponding to the unglycosylated central conserved region. High-resolution crystal structures of each antibody in complex with the G peptide revealed two distinct conformational epitopes that require proper folding of the cystine noose located in the C-terminal part of the central conserved region. Comparison of these structures with the structure of fractalkine (CX3CL1) alone or in complex with a viral homolog of CX3CR1 (US28) suggests that RSV G would bind to CX3CR1 in a mode that is distinct from that of fractalkine. Collectively, these results build on recent studies demonstrating the importance of RSV G in antibody-mediated protection from severe RSV disease, and the structural information presented here should guide the development of new vaccines and antibody-based therapies for RSV.</p></div

Fabs CB017.5 and CB002.5 bind with high affinity to RSV G and a G peptide.

<p>Surface plasmon resonance (SPR) response curves of Fab CB002.5 (top) and Fab CB017.5 (bottom) binding to wild-type RSV sG from strain A2 (A) and a subtype A RSV G peptide encompassing the central conserved region (B). The raw data are plotted in black, and the calculated best fit to a 1:1 binding model is plotted in red. The equilibrium dissociation constant (<i>K</i>_D) for each interaction is displayed above the respective SPR curve. (C) Sequence alignment of the 45-residue G peptide and the corresponding region of RSV G from strains A2 and B1. The strictly conserved residues, the cystine noose, and CX3C motif are labeled.</p