Blind prediction of HIV integrase binding from the SAMPL4 challenge

Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components—a small “virtual screening” challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.     

Indirect detection of the 183W and 57Fe nuclei using 119Sn‐relayed 1H,X correlation spectroscopy

Recently reported triple‐resonance Y‐relayed ¹H,X correlation experiments have been utilized to characterize ¹⁸³W and ⁵⁷Fe chemical shifts using ¹¹⁹Sn as the Y‐relaying nucleus instead of the previously used ³¹P. Application of an adaptation of Gudat's original INEPT/HMQC sequence results in a significant enhancement of the signal‐to‐noise (S/N) ratio for two‐dimensional ¹¹⁹Sn‐relayed ¹H,¹⁸³W and ¹H, ⁵⁷Fe correlation spectra with efficient detection of the transition metal nucleus in tungsten and iron complexes lacking an observable direct scalar coupling between the transition metal and any hydrogen nuclei. Strengths and shortcomings of the novel sequence and the original sequences reported by Gudat are discussed in the context of ¹¹⁹Sn‐relayed proton detection of very low frequency transition metal nuclei. Copyright © 2010 John Wiley & Sons, Ltd.     

Guidelines for the analysis of free energy calculations

Journal of Computer-Aided Molecular Design - Free energy calculations based on molecular dynamics simulations show considerable promise for applications ranging from drug discovery to prediction of...     

A simple method for extracting both active oily and water soluble extract (WSE) from Nigella sativa (L.) seeds using a single solvent system

The current study provides a way of extraction for both active NSO and WSE from Nigella sativa seeds using 98% methanol. About 1?kg of ground seeds was macerated by 1:2.5 w/v (g/mL) for 72?hours. After rotary evaporation and 7 days of continuous drying and chilling at 50 and 4?°C, NSO and WSE were obtained at the same instant. Solubility tests of 24 solvents and 11 thin layer chromatographic analyses while 2, 2-diphenyl-1-picrylhydrazyl free radical scavenging assay of NSO (73.66) , WSE (33.32) and NSO?+?WSE (78.22) against ascorbic acid (IC50?=?4.28?mg/mL) was performed. WSE was found to be highly soluble in water and 5% NaOH exhibiting the same Rf value of 0.95 for EtOH:DMSO (9:1) against the honey. WSE has revealed more than twofold higher anti-oxidant activity than others. Formulation of WSE with Tualang honey may provide better targeted hydrophilic drug delivery systems.     

SAMPL6 challenge results from $$pK_a$$ predictions based on a general Gaussian process model

A variety of fields would benefit from accurate \(pK_a\) predictions, especially drug design due to the effect a change in ionization state can have on a molecule’s physiochemical properties. Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic \(pK_a\)s of 24 drug like small molecules. We recently built a general model for predicting \(pK_a\)s using a Gaussian process regression trained using physical and chemical features of each ionizable group. Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton. These features are fed into a Scikit-learn Gaussian process to predict microscopic \(pK_a\)s which are then used to analytically determine macroscopic \(pK_a\)s. Our Gaussian process is trained on a set of 2700 macroscopic \(pK_a\)s from monoprotic and select diprotic molecules. Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge. Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic. Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile–quantile plots, indicating it can predict its own accuracy. The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable.     

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge

Small molecule distribution coefficients between immiscible nonaqueuous and aqueous phases—such as cyclohexane and water—measure the degree to which small molecules prefer one phase over another at a given pH. As distribution coefficients capture both thermodynamic effects (the free energy of transfer between phases) and chemical effects (protonation state and tautomer effects in aqueous solution), they provide an exacting test of the thermodynamic and chemical accuracy of physical models without the long correlation times inherent to the prediction of more complex properties of relevance to drug discovery, such as protein-ligand binding affinities. For the SAMPL5 challenge, we carried out a blind prediction exercise in which participants were tasked with the prediction of distribution coefficients to assess its potential as a new route for the evaluation and systematic improvement of predictive physical models. These measurements are typically performed for octanol-water, but we opted to utilize cyclohexane for the nonpolar phase. Cyclohexane was suggested to avoid issues with the high water content and persistent heterogeneous structure of water-saturated octanol phases, since it has greatly reduced water content and a homogeneous liquid structure. Using a modified shake-flask LC-MS/MS protocol, we collected cyclohexane/water distribution coefficients for a set of 53 druglike compounds at pH 7.4. These measurements were used as the basis for the SAMPL5 Distribution Coefficient Challenge, where 18 research groups predicted these measurements before the experimental values reported here were released. In this work, we describe the experimental protocol we utilized for measurement of cyclohexane-water distribution coefficients, report the measured data, propose a new bootstrap-based data analysis procedure to incorporate multiple sources of experimental error, and provide insights to help guide future iterations of this valuable exercise in predictive modeling.     

高聚合度Ⅱ-型聚磷酸铵的合成

用聚合反应-热处理两段工艺合成了高聚合度的聚磷酸铵（APP）阻燃材料,其结构经XRD,粒度及平均聚合度表征。优化反应条件为：磷酸氢二铵1mol,n（磷酸氢二铵）：n（五氧化二磷）：n（脲）：1．0：1．0：0．3．干燥氨气氛下于290℃反应30min,再经250℃-280℃后处理100min-110min。APP的平均聚合度大于150,粒度小于50μm。     

Laser-induced fluorescence studies of polycyclic aromatic hydrocarbons (PAH) vapors at high temperatures.

In this work, we present the fluorescence spectra of anthracene and pyrene vapors at different elevated temperatures (from 150 to 650 degrees C) excited with the 337 nm line of a nitrogen laser. We describe the high temperature effects on the resulting spectral properties including spectral intensity, spectral bandwidth and spectral shift. We found that the PAH fluorescence spectral bandwidths become very broad as the temperature increases. The broadening is mainly due to thermal vibrational sequence congestion. We also have found that the fluorescence intensity of pyrene vapor increases with increasing temperature, which results from the increase of the pyrene vapor absorption cross section at 337 nm.