Effect of using multiple peptide templates.

<p>The Pearson's correlation coefficients between the predicted binding energies and SPOT data are shown.</p><p>*When sequences are separated into Class I and Class II, the class is marked in parentheses. Class I has (R/K)xxPxxP motif and Class II has PxxPx(R/K) motif. ABP1, Amphyphisin, Endophilin, and MYO5 do not have the canonical SH3 motifs.</p>†<p>Peptides 1, 2, and 3 have Class I orientation, and peptides 4, 5, 6, 7, 8, and 9 have Class II orientation. Class I and Class II are marked in parentheses.</p

MOESM1 of KRDS: a web server for evaluating drug resistance mutations in kinases by molecular docking

Additional file 1: Table S1. Re-docking of five ligands co-crystalized with CDK2 to the five RosettaBackrub generated CDK2 conformations. Table S2. RMSD values after re-docking of co-crystals into native structures. Table S3. The list of pdb ids of DFG-in and its corresponding DFG-out structures to perform docking in ABL1, BRAF, EGFR, FGFR4, and IGF1R. Table S4. The averaged docking values of DFG-in and its corresponding DFG-out structures. Table S5. The maximum docking values obtained among ensembles. Table S6. The docking results of ABL1 and EGFR using GOLD. Table S7. The docking results of ABL1 and EGFR using AutoDock Vina. Table S8. Comparison of docking scores and kinase activity data in ABL1 and EGFR. Table S9. Tanimoto coefficient scores between two drugs. Figure S1. Re-docking of imatinib and erlotinib in ABL1 and EGFR. Figure S2. The results of re-docking ligands on different DFG states

Performance Dependency on Number of Averaged Energies.

<p>Out of 11 conformations sampled via molecular dynamics simulation, the average energy of <i>n</i> lowest energies was used as the binding energy. At <i>n</i> = 0, the average performance when a single conformation was used for calculation is plotted. <b>‘+’</b>: ABP1, <b>‘×’</b>: Amphyphisin, ‘*’: Endophilin, empty box: MYO5, filled box: RVS167, empty circle: SHO1, filled circle: LSB3, triangle: YSC84, line: averaged performance.</p

Effect of structural ensemble sampled from MD simulation trajectory.

<p>The Pearson's correlation coefficients between the predicted binding energies and SPOT data are shown.</p><p>*Average correlation coefficient of 11 conformations.</p

Comparison to Other Binding Energy Calculation Methods.

<p>Area under ROC curves (AROC) are shown.</p><p>*Methods by Fernandez-Ballester <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012654#pone.0012654-FernandezBallester1" target="_blank">[19]</a> and Hou used different data sets<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012654#pone.0012654-Hou2" target="_blank">[11]</a>. Accordingly, our method was compared with the two methods separately.</p

Ensemble Based Binding Energy Calculation Method.

<p>Our method is composed of three steps: structure sampling, energy matrix generation, and binding energy calculation. Initial complex structures were generated by superimposing the peptides of crystal structures to the modeled SH3 domains. For each initial complex the near binding state conformations were sampled by molecular dynamics simulation. Sampled structures were used in calculating the contribution of each amino acid on the binding energy on each position, which is converted into energy matrices. The resulting energy matrices were used to calculate the binding energy of peptides.</p

Effect of sequence-structure mapping.

<p>The Pearson's correlation coefficients between the predicted binding energies and SPOT data are shown.</p>†<p>When sequences are separated into Class I and Class II, the class is marked in parentheses. Abp1, Amphyphisin, Endophilin, and Myo5 do not have the canonical SH3 motifs.</p><p>*Pearson's correlation coefficient for the best peptide template when alignments are adjusted.</p><p>**Pearson's correlation coefficient for the best template peptide when the alignment is fixed to that of canonical motif PxxP. The offset and class of peptide templates are indicated in parentheses.</p>‡<p>Cases when the class of the best peptide template is inconsistent with the class of sequence motifs. The best peptide belonging to the sequence motif is indicated in parentheses in the second column. The correlation of fixed alignment for that peptide is shown in the third column.</p

Self-assembling process of flash nanoprecipitation in a multi-inlet vortex mixer to produce drug-loaded polymeric nanoparticles

We present an experimental study of self-assembled polymeric nanoparticles in the process of flash nanoprecipitation using a multi-inlet vortex mixer (MIVM). beta-Carotene and polyethyleneimine (PEI) are used as a model drug and a macromolecule, respectively, and encapsulated in diblock copolymers. Flow patterns in the MIVM are microscopically visualized by mixing iron nitrate (Fe(NO(3))(3)) and potassium thiocyanate (KSCN) to precipitate Fe(SCN) (x) ((3-x)+) . Effects of physical parameters, including Reynolds number, supersaturation rate, interaction force, and drug-loading rate, on size distribution of the nanoparticle suspensions are investigated. It is critical for the nanoprecipitation process to have a short mixing time, so that the solvent replacement starts homogeneously in the reactor. The properties of the nanoparticles depend on the competitive kinetics of polymer aggregation and organic solute nucleation and growth. We report the existence of a threshold Reynolds number over which nanoparticle sizes become independent of mixing. A similar value of the threshold Reynolds number is confirmed by independent measurements of particle size, flow-pattern visualization, and our previous numerical simulation along with experimental study of competitive reactions in the MIVM

In Vitro Evaluation of Dendrimer–Polymer Hybrid Nanoparticles on Their Controlled Cellular Targeting Kinetics

Although polymeric nanoparticles (NPs) and dendrimers represent some of the most promising cancer-targeting nanocarriers, each of them has drawbacks such as limited tissue diffusivity/tumor penetration and rapid in vivo elimination, respectively. To address these issues, we have designed a multiscale hybrid NP system (nanohybrid) that combines folate (FA)-targeted poly­(amidoamine) dendrimers and poly­(ethylene glycol)-<i>b</i>-poly­(d,l-lactide) NPs. The nanohybrids (∼100 nm NPs encapsulating ∼5 nm targeted dendrimers) were extensively characterized through a series of in vitro experiments that validate the design rationale of the system, in an aim to simulate their in vivo behaviors. Cellular uptake studies using FA receptor (FR)-overexpressing KB cells (KB FR⁺) revealed that the nanohybrids maintained high FR selectivity resembling the selectivity of free dendrimers, while displaying temporally controlled cellular interactions due to the presence of the polymeric NP shells. The cellular interactions of the nanohybrids were clathrin-dependent (characteristic of polymer NPs) at early incubation time points (4 h), which were partially converted to caveolae-mediated internalization (characteristic of FA-targeted dendrimers) at longer incubation hours (24 h). Simulated penetration assays using multicellular tumor spheroids of KB FR⁺ cells also revealed that the targeted dendrimers penetrated deep into the spheroids upon their release from the nanohybrids, whereas the NP shell did not. Additionally, methotrexate-containing systems showed the selective, controlled cytotoxicity kinetics of the nanohybrids. These results all demonstrate that our nanohybrids successfully integrate the unique characteristics of dendrimers (effective targeting and penetration) and polymeric NPs (controlled release and suitable size for long circulation) in a kinetically controlled manner

Epithelial–Mesenchymal Transition Enhances Nanoscale Actin Filament Dynamics of Ovarian Cancer Cells

Epithelial ovarian cancer cells enhance their ability to migrate and invade through the epithelial–mesenchymal transition (EMT), resulting in cell seeding and metastasis in the peritoneal cavity and onto adjacent organ surfaces. It has been speculated that cytoskeletal dynamics, such as those of the actin filament, play a role in enhanced cell motility; however, direct evidence has not been provided. Herein, we have directly measured pico- to nanonewton-scale mechanical forces generated by actin dynamics of ovarian cancer SKOV-3 cells upon binding of integrin α5β1 to fibronectin (FN), i.e., formation of a focal adhesion, using real-time atomic force microscopy (AFM) in a force spectroscopy mode. The dendrimer surface chemistry through which FN was immobilized on the AFM probe surfaces further enhanced the sensitivity of the force measurement by 1.5-fold. Post-EMT SKOV-3 cells, induced by transforming growth factor-β, generated larger focal adhesion mechanical forces (17 and 41 nN before and after EMT, respectively) with migration faster than that of pre-EMT cells. Importantly, 22% of the forces transmitted through a single FN–integrin α5β1 pair from post-EMT cells were shown to be sufficient to rupture the binding between FN and integrin α5β1 on the cells, a result which is not observed on pre-EMT cells. This implies that post-EMT cells, by generating forces strong enough to break the FN–integrin binding, migrate and metastasize beyond the ovary, whereas pre-EMT cancer cells are confined in the ovary without such force generation. These results demonstrate quantitative and direct evidence for the role of actin dynamics in the enhanced motility of post-EMT ovarian cancer cells, providing a fundamental insight into the mechanism of ovarian cancer metastasis