Proteochemometrics-Based Prediction of Peptide Binding to HLA-DP Proteins

Human leukocyte antigens (HLA) class II proteins are involved in the antigen processing in the antigen presenting cells. They form complexes with antigen peptide fragments. The peptide–HLA protein complexes are presented on the cell surface where they are recognized by helper T cells (Th cells). HLA-DP is one of the three HLA class II loci. The HLA-DP proteins are associated with a significant number of autoimmune diseases, as well as with a susceptibility or resistance to a number of infectious agents. In the present study, we apply proteochemometricsa method for bioactivity modeling of multiple ligands binding to multiple target proteinsto derive and validate a robust model for peptide binding prediction to the 7 most frequent HLA-DP proteins. The model is able to identify 86% of the binders in the top 10% of the best predicted nonamers generated from one protein

Novel hits for acetylcholinesterase inhibition derived by docking-based screening on ZINC database

<p>The inhibition of the enzyme acetylcholinesterase (AChE) increases the levels of the neurotransmitter acetylcholine and symptomatically improves the affected cognitive function. In the present study, we searched for novel AChE inhibitors by docking-based virtual screening of the standard lead-like set of ZINC database containing more than 6 million small molecules using GOLD software. The top 10 best-scored hits were tested <i>in vitro</i> for AChE affinity, neurotoxicity, GIT and BBB permeability. The main pharmacokinetic parameters like volume of distribution, free fraction in plasma, total clearance, and half-life were predicted by previously derived models. Nine of the compounds bind to the enzyme with affinities from 0.517 to 0.735 µM, eight of them are non-toxic. All hits permeate GIT and BBB and bind extensively to plasma proteins. Most of them are low-clearance compounds. In total, seven of the 10 hits are promising for further lead optimisation. These are structures with ZINC IDs: 00220177, 44455618, 66142300, 71804814, 72065926, 96007907, and 97159977.</p

Analyses for Z-spectrum and MTR_asym of both types of lung tumors.

<p>Z-spectra of A549 (<b>A</b>) and LLC (<b>B</b>) tumors compared to that of spinal cord as a reference show that the LLC tumor has a larger CEST effect than A549 tumor. Corrected MTR_asym spectra of A549 and LLC (<b>C</b>) and corrected MTR_asym at 3.5 ppm (<b>D</b>) show that LLC has a larger APT effect than A549, which may be related to the malignancy of the tumors. *, <i>P</i>≤0.05; **, P≤0.01; ***, P≤0.001 by Student’s t-test.</p

Study design for APT imaging of the mice lung using the small animal ventilator.

<p>The animal was mechanically ventilated for constant amplitude and frequency of respiration at 32 breaths/min in which inhalation and end-expiration was 0.2 s and 1.6 s, respectively. The lung was inflated until the intrapulmonary pressure becomes 20 cm H₂O. Fast spin-echo images were obtained following a presaturation pulse (continuous-wave block pulse, B1 = 1.7 µT, duration = 4 s) in the end-expiratory phase.</p

Micrographs of the A549 tumor and LCC tumor.

<p>Hematoxylin-eosin staining (original magnification×400) demonstrates that LCC (<b>C</b>) have higher cell density and larger cell nuclei compared to A549 (<b>A</b>). Ki-67 staining (original magnification×200) reveals larger fraction of positive cells seen in LCC (<b>D</b>) than in A549 (<b>B</b>). This indicates the presence of a larger number of cells in active phases of the cell cycle (G₁, S, G₂, and mitosis) and thus the aggressive nature of LCC.</p

In-vivo APT imaging of lung tumors in the orthotopic mouse model.

<p>Representative T2-weighted images (left) and APT-weighted images (right, MTR_asym map at 3.5 ppm) of A549 (<b>A</b>) and LLC (<b>B</b>) where the tumors (open arrows) are delineated brighter than the surrounding tissues including spinal cord (closed arrows) and skeletal muscles. A typical region of interest to measure signal intensity on a tumor is demonstrated (<b>B</b>).</p

In vivo SNR profiles.

<p>Images of the right breast sagittal slice from a volunteer obtained with A) the FCE coil in T/R mode, B) the transmit FCE coil with the 16-channel receive array insert (different windowing was used compared to (A) due to the very high SNR values close to the array elements) and C) a comparison of the respective profiles. The <i>in vivo</i> results demonstrate comparable SNR gains to the phantom data; there is approximately a 3.5× improvement in mean SNR throughout the breast.</p

Noise correlation matrix of the 16 channel receive elements.

<p>Noise correlation matrix from the 16-channel receive array acquired with the uniform phantom. The mean correlated value is 6.6%, with a minimum of 3.6% and a maximum of 17.7%.</p

Comparison of SNR maps in a phantom between the 16-channel receive array and volume coil.

<p>Improvements in SNR when using close-fitting 16-channel array. SNR maps acquired with A) the FCE volume coil alone and B) the 16-channel receive array. The sagittal view through the middle of a hemispherical homogenous canola oil phantom is shown (a.u. SNR). C) SNR ratio between the 16-channel receive array and the volume coil demonstrates a mean SNR improvement of a factor of 3.3 over the entire area of the phantom, with a mean SNR gain of 2.1× in the middle of the phantom marked by the black ROI in (A). The periphery of the phantom experiences a local high (up to 10-fold) increase in SNR.</p

<i>g</i>-factor maps of a sagittal mid-slice <i>in vivo</i>.

<p>SENSE acceleration was applied in the left-to-right (L/R) and foot-to-head (F/H) directions using acceleration factors of 1×, 2×, or 3× in each direction. With R = 4 (2×2), the mean and the maximum <i>g</i>-factor were 1.03 and 1.08, respectively, allowing for acquisition of just 25% of the k-space data. With the low <i>g</i>-factors, there should be negligible propagation of artifacts in the final reconstructed image.</p