Molecular Dynamics Simulations and Structural Analysis of <i>Giardia duodenalis</i> 14-3‑3 Protein–Protein Interactions

Giardiasis is a gastrointestinal diarrheal illness caused by the protozoan parasite <i>Giardia duodenalis</i>, which affects annually over 200 million people worldwide. The limited antigiardial drug arsenal and the emergence of clinical cases refractory to standard treatments dictate the need for new chemotherapeutics. The 14-3-3 family of regulatory proteins, extensively involved in protein–protein interactions (PPIs) with pSer/pThr clients, represents a highly promising target. Despite homology with human counterparts, the single 14-3-3 of <i>G. duodenalis</i> (g14-3-3) is characterized by a constitutive phosphorylation in a region critical for target binding, thus affecting the function and the conformation of g14-3-3/clients interaction. However, to approach the design of specific small molecule modulators of g14-3-3 PPIs, structural elucidations are required. Here, we present a detailed computational and crystallographic study exploring the implications of g14-3-3 phosphorylation on protein structure and target binding. Self-Guided Langevin Dynamics and classical molecular dynamics simulations show that phosphorylation affects locally and globally g14-3-3 conformation, inducing a structural rearrangement more suitable for target binding. Profitable features for g14-3-3/clients interaction were highlighted using a hydrophobicity-based descriptor to characterize g14-3-3 client peptides. Finally, the X-ray structure of g14-3-3 in complex with a mode-1 prototype phosphopeptide was solved and combined with structure-based simulations to identify molecular features relevant for clients binding to g14-3-3. The data presented herein provide a further and structural understanding of g14-3-3 features and set the basis for drug design studies

Free Energy Profile and Kinetics Studies of Paclitaxel Internalization from the Outer to the Inner Wall of Microtubules

Several pieces of experimental evidence led us to hypothesize that the mechanism of action of paclitaxel (Taxol) could involve a two-steps binding process, with paclitaxel first binding within the outer wall of microtubules and then moving into the inner binding site. In this work, we first used multiply targeted molecular dynamics (MTMD) for steering paclitaxel from the outer toward the inner binding site. This rough trajectory was then submitted to a refinement procedure in the path collective variables space. Paclitaxel binding energy was monitored along the refined pathway, highlighting the relevance of residues belonging to the H6–H7 and the M- loops. Computational results were supported by kinetics studies performed on fluorescent paclitaxel derivatives

Flowchart of the <i>in silico</i> protocol.

<p>Computational steps applied to select all the hit compounds to be tested <i>in vitro</i>. In each set the percentage of success rate refers to the ratio between the number of active molecules and the number of tested molecules in the following experimental screening: purified GST-ERK8 protein (50 ng/sample) was used in kinase assays. Candidate compounds were dissolved in dimethyl sulfoxide (DMSO) and tested at fixed concentration of 50 µM (an equal volume of DMSO was added to control samples). Reactions were resolved by SDS-PAGE and ³²P incorporation on MBP was estimated by densitometry. Molecules were classified as active when the residual kinase activity was less than 50% in comparison to control samples.</p

Pharmacophore models.

<p>(A), Left panel, Structure-based pharmacophore generated from the Mg⁺⁺ loaded ERK8/ADP complex (coordinates were taken from the refined ERK8 structure) by using the Ligandscout software. Right panel, Structure-based pharmacophore generated by the GRID-based pharmacophore modeling approach, starting from the ligand-bound refined structure of ERK8. Features code: HYD = hydrophobic; HBA = H-bond acceptor; HBD = H-bond donor; AROM = aromatic ring; grey spheres are excluded volumes. (B), The two ligand-based pharmacophores generated with the training set of 18 different inhibitors active towards ERK8 (from Bain J, et al., 2007). Features code same as above.</p

Effect of selected molecular scaffolds on bacterial and eukaryotic GST-ERK8.

<p>(A), Molecular structure of selected compounds. (B), Binding mode of each compound as obtained after the molecular docking step. The ITT molecules are showed as sticks and colored by atom type. ERK8 protein structure is represented by secondary structure cyan elements. (C), Samples of GST-ERK8 from <i>E. coli</i> with the indicated concentration of inhibitors were subjected to kinase assay. Reactions were resolved by SDS-PAGE and ³²P incorporation on MBP was estimated by densitometry (upper panel). Coomassie staining verified that equal amounts of substrate were loaded (lower panel). (D), The average results of three independent experiments done in duplicate ± SD are plotted. (E), Samples of GST-ERK8_Bac with the indicated concentration of inhibitors were subjected to kinase assay. Reactions were resolved by SDS-PAGE and ³²P incorporation on MBP was estimated by densitometry (upper panel). Coomassie staining verified that equal amounts of substrate were loaded (lower panel). (F), The average results of three independent experiments done in duplicate ± SD are plotted.</p

<i>In vitro</i> characterization.

<p>(A), Dose/response curves for ITT53 and ITT57 on GST-ERK8_Bac. Results are reported as residual MBP phosphorylation levels compared with the control (DMSO). The average results of two independent experiments done in triplicate ± SD are plotted with the curve-fitting PRISM software (GraphPad). The concentration of drug that inhibited activity by 50% (IC₅₀) is shown. (B), ITT53, ITT57 and Ro-318220 ATP competition assay on GST-ERK8_Bac. Inhibition values are reported as percentage of residual MBP phosphorylation levels (i.e., residual kinase activity) compared with the control (DMSO). Results for the two indicated concentrations of ITT53, ITT57 and Ro-318220 (top, middle, bottom panel, respectively) at four different ATP doses were plotted. The average results of two independent experiments done in triplicate ± SD are plotted.</p

Gatekeeper mutants.

<p>(A), Multiple sequence alignment of gatekeeper region among different members of the MAPK and CDK families of kinases. The position corresponding to the gatekeeper residue is highlighted. (B), Superimposition of the refined ERK8 structure (cyan) and CDK2 (magenta) X-ray structure. (C), Western Blot control of GST-fusion proteins from <i>E. coli</i>. Each lane was loaded with 100 ng of purified protein. ERK8_KD sample (lane 6) is a point mutant on the conserved lysine (Lys, K) in position 42 to arginine (Arg, R). (D), Representative kinase assay blot of gatekeeper mutants (200 ng/sample of purified protein) (upper panel). Reactions were resolved by SDS-PAGE and ³²P incorporation on MBP was estimated by densitometry. Coomassie staining verified that equal amounts of substrate were loaded (lower panel). Quantification of kinase activity in comparison to WT, as scored by MBP phosphorylation, from three independent experiments is reported in the lower panel.</p

ERK8 kinase domain model.

<p>(A), Multiple sequence alignment between ERK8 and the selected templates FUS3 and ERK2. Numbering is referred to human ERK8 cDNA sequence as defined in Uniprot accession number Q8TD08. Consensus code: “yellow” indicates positions which have a single, fully conserved residue; “green” indicates conservation between groups of strongly similar properties; “blue” indicates conservation between groups of weakly similar properties. Gatekeeper residue is in bold and indicated by a full black circle. The TEY activation motif is in red (activation loop spans from the DFG motif to the APE motif, residues 155–187). The region in square brackets has been substituted (starting from the position indicated with the dashed red line) with the alignment highlighted in the bottom square that includes p38α. (B), Model of the ERK8 kinase domain (residues 12–345 of the full-length 1–544 protein) obtained by means of homology modeling protocol. Conserved kinase domain features are indicated, β-sheets colored in yellow, α-helices colored in red, loops colored in green, TEY activation motif colored in blue. (C), Superimposition of the same ERK8 model (grey) with the ERK2 template (purple). (D), Evolution of ERK8 structure with the MD refinement. Superimposition of the ERK8 model (grey), used as MD input, with the representative final structure (the refined ERK8 structure) (cyan) obtained after the simulation. (E), Superimposition of the refined ERK8 model (cyan) with the ERK2 template (purple).</p

A resistant ERK8_F92I mutant confirms the predicted ATP pocket-binding mode.

<p>(A), Representative structures from MD simulation of the complex between ITT57 and both ERK8_WT (left panel) and ERK8_F92I mutant (right panel). The residue at position 92 is labeled and showed as sticks. The ITT57 ligand is showed as sticks. Protein residues and ligand atoms are colored by atom type. (B), GST tagged ERK8_WT and ERK8_F92I proteins (200 ng/sample) were used in kinase assays in presence of the indicated concentrations of ITT53, ITT57 and Ro-318220 molecules. Using the paper-spotted kinase assay technique, we quantified and normalized the activities of the WT and of the mutant protein. MBP phosphorylation levels were evaluated by β-counting protocol of triplicates and results expressed as percentage of residual kinase activity compared with control samples. Significance (p-value) was obtained by one-way ANOVA test. Asterisks were attributed for the following significance values: p<0.05 (*), p<0.01 (**), p<0.001 (***).</p

Hit Recycling: Discovery of a Potent Carbonic Anhydrase Inhibitor by <i>in Silico</i> Target Fishing

<i>In silico</i> target fishing is an emerging tool in drug discovery, which is mostly used for primary target or off-target prediction and drug repositioning. In this work, we developed an <i>in silico</i> target fishing protocol to identify the primary target of GV2–20, a false-positive hit highlighted in a cell-based screen for 14–3–3 modulators. Although GV2–20 does not bind to 14–3–3 proteins, it showed remarkable antiproliferative effects in CML cells, thus raising interest toward the identification of its primary target. Six potential targets of GV2–20 were prioritized <i>in silico</i> and tested <i>in vitro</i>. Our results show that the molecule is a potent inhibitor of carbonic anhydrase 2 (CA2), thus confirming the predictive capability of our protocol. Most notably, GV2–20 experienced a remarkable selectivity for CA2, CA7, CA9, and CA12, and its scaffold was never explored before as a chemotype for CA inhibition, thus becoming an interesting lead candidate for further development