Distribution of Predicted Binding Energy for DREAM4 Target.

<p>Binding energies were calculated for randomly generated sequences (upper panel, dashed lines), for random sequences with canonical SH3 binding peptide motifs (lower panel, dashed lines), and for sequences derived from the DREAM4 Gold Standard (solid lines).</p

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

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

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

DREAM4 Gold Standard and Predicted PSFM.

<p>Position specific frequency matrices are represented with WebLogo <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012654#pone.0012654-Crooks1" target="_blank">[22]</a>. Gold Standards are disclosed for three targets out of five challenges. They are displayed on the upper panel. The PSFM of 1000 sequences with 1000 lowest energies are displayed on the lower panel. Target 1: Homology to FISH, Target 2: Intersection-1-5, Target 3: PACSIN1. In case of target 2, the first position of the DREAM4 fold standard is matched with the fourth position in our prediction.</p

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

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

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

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