Quantitative Prediction of Dissociation Rates of PYK2 Ligands Using Umbrella Sampling and Milestoning.

We used umbrella sampling and the milestoning simulation method to study the dissociation of multiple ligands from protein kinase PYK2. The activation barriers obtained from the potential of mean force of the umbrella sampling simulations correlated well with the experimental dissociation rates. Using the zero-temperature string method, we obtained optimized paths along the free-energy surfaces for milestoning simulations of three ligands with a similar structure. The milestoning simulations gave an absolute dissociation rate within 2 orders of magnitude of the experimental value for two ligands but at least 3 orders of magnitude too high for the third. Despite the similarity in their structures, the ligands took different pathways to exit from the binding site of PYK2, making contact with different sets of residues. In addition, the protein experienced different conformational changes for dissociation of the three ligands.

15 Years of molecular simulation of drug-binding kinetics.

INTRODUCTION: Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made. AREAS COVERED: This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years. EXPERT OPINION: The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.

How are N-methylcarbamates encapsulated by ß-cyclodextrin: insight into the binding mechanism.

Guest molecules containing chromophore groups encapsulated by ß-cyclodextrin (ß-CD) generate circular dichroism (CD) signals, which enables a preliminary prediction of their binding modes. However, the accurate determination of the representative binding conformation (RC) remains a challenging task due to the complex conformational space of these host-guest systems. Here, we combine a molecular dynamics/quantum mechanics/continuum solvent model (MD/QM/CSM) with induced circular dichroism (ICD) data (N. L. Pacioni, A. B. Pierini and A. V. Veglia, Spectrochim. Acta A Mol. Biomol. Spectrosc., 2013, 103, 319-324.) to explore the binding mechanism of ß-CD with four N-methylcarbamate molecules: promecarb (PC), bendiocarb (BC), carbaryl (CY) and carbofuran (CF). In aqueous solution, their stability decreases as: PC > BC > CY > CF. Comparing the ECD spectra computed from TD-DFT with the ICD data can help eliminate many common binding configurations and identify the RC. The host-guest binding affinities (BAs) estimated using a ONIOM2(B971:PM6)/SMD model reproduce the measured binding trend, reveal the competition between the non-covalent interaction and solvent effect and explain the large difference in their binding modes. We also examine the fluctuations in the computed BA using similar structures.

Milestoning simulation of ligand dissociation from the glycogen synthase kinase 3ß.

As drug-binding kinetics has become an important factor to be considered in modern drug discovery, this work evaluated the ability of the Milestoning method in computing the absolute dissociation rate of a ligand from the serine-threonine kinase, glycogen synthase kinase 3ß, which is a target for designing drugs to treat diseases such as neurodegenerative disorders and diabetes. We found that the Milestoning method gave good agreement with experiment with modest computational costs. Although the time scale for dissociation lasted tens of seconds, the collective molecular dynamics simulations total less than 1µs. Computing the committor function helped to identify the transition states (TSs), in which the ligand moved substantially away from the binding pocket. The glycine-rich loop with a serine residue attaching to its tips was found to undergo large movement from the bound to the TSs and might play a role in controlling drug-dissociation kinetics.

Simulation of ligand dissociation kinetics from the protein kinase PYK2.

Early-stage drug discovery projects often focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. The kinetics of drug binding are ignored but can have significant influence on drug efficacy. Therefore, increasing attention has been paid on evaluating drug-binding kinetics early in a drug discovery process. Simulating drug-binding kinetics at the atomic level is challenging for the long time scale involved. Here, we used the transition-based reweighting analysis method (TRAM) with the Markov state model to study the dissociation of a ligand from the protein kinase PYK2. TRAM combines biased and unbiased simulations to reduce computational costs. This work used the umbrella sampling technique for the biased simulations. Although using the potential of mean force from umbrella sampling simulations with the transition-state theory over-estimated the dissociation rate by three orders of magnitude, TRAM gave a dissociation rate within an order of magnitude of the experimental value.

Postoperative outcomes in surgical patients with obstructive sleep apnoea diagnosed by sleep studies: a meta-analysis and trial sequential analysis.

Identifying surgical patients with obstructive sleep apnoea may assist with anaesthetic management to minimise postoperative complications. Using trial sequential analysis, we evaluated the impact of obstructive sleep apnoea diagnosed by polysomnography or home sleep apnoea testing on postoperative outcomes in surgical patients. Multiple databases were systematically searched. Outcomes included: total postoperative complications, systemic complications (cardiovascular, respiratory, neurological, renal, infectious) and specific complications (atrial fibrillation, myocardial infarction, combined hospital and intensive care unit re-admission, mortality). The pooled odds ratios of postoperative complications were evaluated by the Mantel-Haenszel method random-effects model. Meta-analysis and meta-regression were conducted, and the GRADE approach was used to evaluate the certainty of evidence. Twenty prospective cohort studies with 3756 patients (2127 obstructive sleep apnoea and 1629 non-obstructive sleep apnoea) were included (9 in non-cardiac surgery and 11 in cardiac surgery). Postoperative complications were almost two-fold higher with obstructive sleep apnoea, OR (95%CI) 1.92 (1.52-2.42), p < 0.001; certainty of evidence, moderate. Obstructive sleep apnoea was associated with a 1.5 times increased risk of postoperative cardiovascular complications, OR (95%CI) 1.56 (1.20-2.02), p = 0.001; certainty of evidence, moderate; an almost two-fold increase in respiratory complications, OR (95%CI) 1.91 (1.39-2.62), p < 0.001; certainty of evidence, moderate; and hospital and ICU re-admission, OR (95%CI) 2.25 (1.21-4.19), p = 0.01; certainty of evidence, low. Trial sequential analysis showed adequate information size for postoperative complications. Baseline confounding factors were adjusted by meta-regression, and the sub-group analysis did not materially change our results. This increased risk occurred especially in patients in whom obstructive sleep apnoea had been newly diagnosed, emphasising the importance of pre-operative screening.

Lactic acid bacteria feeding reversed the malformed eye structures and ameliorated gut microbiota profiles of Drosophila melanogaster Alzheimer's disease model.

AIMS: To utilize transgenic GMR-Aß42 Drosophila melanogaster as a model to evaluate potential Alzheimer's disease (AD)-reversal effects via the administration of lactic acid bacteria (LAB) strains, and associations of LAB with changes in gut microbiota profiles. METHODS AND RESULTS: Wild-type flies (Oregon-R) were crossed with glass multimer reporter-GAL4 (GMR-GAL4) to produce GMR-OreR (Control), while UAS-Aß42 (#33769) were crossed with GMR-GAL4 to produce transgenic Drosophila line that expressed Aß42 (GMR-Aß42). Feed containing seven different LAB strains (Lactobacillus paracasei 0291, Lactobacillus helveticus 1515, Lactobacillus reuteri 30242, L. reuteri 8513d, Lactobacillus fermentum 8312, Lactobacillus casei Y, Lactobacillus sakei Probio65) were given to GMR-Aß42 respectively, while feed without LAB strains were given to control and transgenic GMR-Aß42.nf Drosophila lines. The morphology of the eyes was viewed with scanning electron microscopy (SEM). The changes in gut microbiota profiles associated with LAB were analysed using 16s high throughput sequencing. Malformation of eye structures in transgenic GMR-Aß42 Drosophila were reversed upon the administration of LAB strains, with more prevalent effects from L. sakei Probio65 and L. paracasei 0291. The GMR-Aß42.nf group showed dominance of Wolbachia in the gut, a genus that was almost absent in the normal control group (P < 0·05). The administration of L. sakei Probio65 and L. paracasei 0291 reduced the abundance of Wolbachia accompanied by increased abundance of Stenotrophomonas and Acetobacter (P < 0·05), resembling the microbial profile of the control group. CONCLUSIONS: Lactobacillus sakei Probio65 and Lactobacillus paracasei 0291 have more prominent effects in reversing malformed eye of transgenic GMR-Aß42 Drosophila, and reducing the abundance of Wolbachia accompanied by an increased abundance of Stenotrophomonas and Acetobacter. SIGNIFICANCE AND IMPACT OF THE STUDY: Potentials of LAB to prevent and/or alleviate the onset and pathogenesis of neurodegenerative diseases such as AD, supporting brain health strategies along the gut-brain axis.

Lactobacilli reduce recurrences of vaginal candidiasis in pregnant women: a randomized, double-blind, placebo-controlled study.

AIMS: The aim of this study was to investigate the effects of lactobacilli strains in preventing the recurrences of vaginal candidiasis (VC) in 78 pregnant women with VC (lactobacilli, n = 39; placebo, n = 39) and the potential benefits on quality of life. METHODS AND RESULTS: The lactobacilli putative probiotic (SynForU-HerCare; two capsules/day of 9·5 log CFU per capsule) or placebo was administered for 8-weeks in a randomized, double-blind, placebo-controlled study. Subjects were assessed for vaginal and gut health conditions at baseline, week-4 and week-8 via questionnaires. The vulvovaginal symptom questionnaire not only covered aspects pertaining to vulvovaginal symptoms but also the quality of life impacts such as emotional, social and sexual. The administration of lactobacilli reduced symptoms of irritation (P = 0·023) and discharge (P = 0·011) starting week-4 and continued after week-8 (P < 0·05), accompanied by reduced symptoms for burning after week-8 (P = 0·046) as compared to the placebo. Patients consuming lactobacilli also showed reduced concern about symptoms after week-4 (P = 0·010) and continued after week-8 (P = 0·001), accompanied by reduced impairment of daily activities attributed to vulvovaginal symptoms (P = 0·012) and continued after week-8 (P = 0·026). Insignificant differences were observed for sexual impacts between treatment groups. The administration of lactobacilli also reduced recurrences of both emotional and social stress as compared to the placebo at both week-4 and week-8 (P < 0·05). Patients consuming lactobacilli showed higher defecation times per week at week-4 (P = 0·010) and week-8 (P = 0·001) as compared to the placebo group, indicating the potential to reduce risks of pregnancy-induced constipation. CONCLUSIONS: Lactobacilli probiotics are beneficial towards pregnant women, especially in reducing vulvovaginal symptoms and recurrences of VC, accompanied by improved emotional and social distress attributed to VC. SIGNIFICANCE AND IMPACT OF THE STUDY: The study demonstrated the preventive and modulatory roles of lactobacilli strains against VC in pregnant women. Taken altogether, our present data illustrated that lactobacilli probiotics are beneficial towards pregnant women, especially in reducing vulvovaginal symptoms and recurrences of VC, accompanied by improved emotional and social distress attributed to VC, thus could be a potential strategy for the maintenance of vaginal health during pregnancy.

EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery.

EDock-ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock-ML takes a bottom-up approach in which machine-learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine-learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324-2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock-ML can be accessed freely at edock-ml.umsl.edu.

Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics.

Most early-stage drug discovery projects focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. Since many approved drugs have nonequilibrium binding characteristics, there has been increasing interest in optimizing binding kinetics early in the drug discovery process. As focal adhesion kinase (FAK) is an important drug target, we examine whether steered molecular dynamics (SMD) can be useful for identifying drug candidates with the desired drug-binding kinetics. In simulating the dissociation of 14 ligands from FAK, we find an empirical power-law relationship between the simulated time needed for ligand unbinding and the experimental rate constant for dissociation, with a strong correlation depending on the SMD force used. To improve predictions, we further develop regression models connecting experimental dissociation rate with various structural and energetic quantities derived from the simulations. These models can be used to predict dissociation rates from FAK for related compounds.

Inter-relationship of risk factors and pathways associated with chronic kidney disease in patients with type 2 diabetes mellitus: a structural equation modelling analysis.

OBJECTIVES: Diabetes mellitus is the most common cause of chronic kidney disease (CKD); however, the inter-relationships and pathogenetic mechanisms among risk factors are still largely unknown. Structural equation modelling (SEM) was applied to test a hypothesis of causal pathways related to CKD in patients with type 2 diabetes mellitus (T2DM). STUDY DESIGN: This is a prospective observational study. METHODS: A total of 3395 patients with T2DM were enrolled in this study. A hypothesised SEM was applied to assess associations among demographic data, diabetic self-management behaviours, diabetes control, lifestyle, psycho-social, chronic inflammation factors, anthropometric and metabolic variables simultaneously and the risk of CKD. RESULTS: Demographic data (including education, marital status and mini-mental state examination score) (-0.075), white blood cell count (0.084), high blood pressure (0.144), World Health Organisation (WHO) 5 well-being index (-0.082), diabetes control (0.099), triglyceride (0.091) and uric acid (0.282) levels had direct effects on the risk of CKD. The final model could explain 26% of the variability in baseline CKD status. In addition, the same direct and specific indirect factors at baseline CKD status analysis contributed to the risk of CKD at the 12-month follow-up. The final model could explain 31% of the variability in the risk of CKD at the 12-month follow-up. CONCLUSIONS: This study investigates associations between factors obtained from real-world daily practice and CKD status simultaneously and delineates the potential pathways and inter-relationships of the risk factors that contribute to the development of CKD in patients with T2DM.

Using machine learning to improve ensemble docking for drug discovery.

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activity, researchers have not yet come up with effective ways to use these scores to classify compounds into actives and inactives. This shortcoming has led to the decrease, rather than an increase in the performance of classifying compounds when more structures are added to the ensemble. Previously, we suggested machine learning, implemented in the form of a naïve Bayesian model could alleviate this problem. However, the naïve Bayesian model assumed that the probabilities of observing the docking scores to different structures to be independent. This approximation might prevent it from achieving even higher performance. In the work presented in this paper, we have relaxed this approximation when using several other machine learning methods-k nearest neighbor, logistic regression, support vector machine, and random forest-to improve ensemble docking. We found significant improvement.

Steered molecular dynamics simulations for uncovering the molecular mechanisms of drug dissociation and for drug screening: A test on the focal adhesion kinase.

Drug-binding kinetics could play important roles in determining the efficacy of drugs and has caught the attention of more drug designers. Using the dissociation of 1H-pyrrolo[2,3-b]-pyridines from the focal adhesion kinase as an example, this work finds that steered molecular dynamics simulations could help screen compounds with long-residence times. It also reveals a two-step mechanism of ligand dissociation resembling the release of ADP from protein kinase A reported earlier. A phenyl group attaching to the pyrrole prolongs residence time by creating a large activation barrier for transition from the bound to the intermediate state when it becomes exposed to the solvent. Principal component analysis shows that ligand dissociation does not couple with large-scale collective motions of the protein involving many of its amino acids. Rather, a small subset of amino acids dominates. Some of these amino acids do not contact the ligands directly along the dissociation pathways and could exert long-range allosteric effects. © 2018 Wiley Periodicals, Inc.

Death or near-death in patients with obstructive sleep apnoea: a compendium of case reports of critical complications.

The care of surgical patients with obstructive sleep apnoea (OSA) invokes concerns with safety and liability because of the risk that exists for perioperative death or near-death. The purpose of this review is to analyse the available literature to identify risk factors for perioperative critical complications in patients with OSA. Literature reports were screened for life threatening complications and deaths in surgical patients with OSA. The critical complications were sub-grouped as death/near-death events (death and anoxic brain damage) vs critical respiratory events (CRE)/other events and analysed for various risk factors. Both univariate and multivariate analyses were conducted to identify the potential risk factors.In total, 15 case reports and two medico-legal reports, comprising of 60 total patients with OSA were included in our analysis. Overall, there were 43 deaths or near-death events and 12 critical respiratory events and five other life threatening events. Ten patients (17%) with OSA were undiagnosed before surgery. Only 31% (11/35) were on preoperative continuous positive airway pressure (CPAP), with 36% (4/11) of them continuing CPAP in the postoperative period. The majority of them received a morphine equivalent daily dose less than 10 mg. Eighty percent of the events occurred in the first 24 h and 67% occurred on the general hospital ward.Patients with OSA are at risk of critical complications including death during the initial 24 h after surgery. Morbid obesity, male sex, undiagnosed OSA, partially treated/untreated OSA, opioids, sedatives, and lack of monitoring are risk factors for death or near-death events.

Comparison of chemical profiles and effectiveness between Erxian decoction and mixtures of decoctions of its individual herbs: a novel approach for identification of the standard chemicals.

BACKGROUND: Identification of bioactive standard chemicals is a major challenge in the study of the Chinese medicinal formula. In particular, the chemical components may interact differently depending on the preparative methods, therefore affecting the amounts of bioactive components and their pharmacological properties in the medicinal formula. With the use of Erxian decoction (EXD) as a study model-a  well-known Chinese medicinal formula for treating menopausal symptoms, a novel and rapid approach in seeking standard chemicals has been established by differentially comparing the HPLC profiles and the menopause-related biochemical parameters of combined decoction of EXD (EXD-C) and mixtures of the decoctions of its individual herbs (EXD-S). METHODS: The levels of six chemicals, which exerted actions on the HPO axis, have been measured in EXD-C and EXD-S by HPLC. Twelve-month-old female Sprague-Dawley rats were employed and treated with EXD-C and EXD-S. Their endocrine functions after treatment were evaluated by determining the ovarian mRNA levels of aromatase, a key enzyme for estradiol biosynthesis. The effect of the antioxidant regimen was determined by the hepatic superoxide dismutase-1 (SOD), catalase (CAT) and glutathione peroxidase (GPx-1) mRNA levels. RESULTS: The amounts of mangiferine, ferulic acid, jatrorrhizine and palmatine in EXD-S were twofold higher than those in EXD-C. EXD-S was more effective in stimulating ovarian aromatase and the expression of the antioxidant enzymes compared with EXD-C. CONCLUSION: Mangiferine, ferulic acid, jatrorrhizine and palmatine are suitable for use as standard chemicals for quality evaluation of EXD according to our approach. EXD-S could be more effective than EXD-C.

Pressure drop of two-phase helium along long cryogenic flexible transfer lines to support a superconducting RF operation at its cryogenic test stand.

BACKGROUND: Establishing a stand-alone cryogenic test stand is of vital importance to ensure the highly reliable and available operation of superconducting radio-frequency module in a synchrotron light source. Operating a cryogenic test stand relies strongly on a capability to deliver two-phase helium along long cryogenic transfer lines. A newly constructed cryogenic test stand with flexible cryogenic transfer lines of length 220 m at National Synchrotron Radiation Research Center is required to support a superconducting radio-frequency module operated at 126.0 kPa with a 40-W dynamic load for a long-term reliability test over weeks. It is designed based on a simple analytical approach with the introduction of a so-called tolerance factor that serves to estimate the pressure drops in transferring a two-phase helium flow with a substantial transfer cryogenic heat load. Tolerance factor 1.5 is adopted based on safety factor 1.5 commonly applied in cryogenic designs to estimate the total mass flow rate of liquid helium demanded. A maximum 60-W dynamic load is verified with experiment measured with heater power 60 W instead after the cryogenic test stand has been installed. RESULTS: Aligning the modeled cryogenic accumulated static heat load with the results measured in situ, actual tolerance factor 1.287 is obtained. The feasibility and validity of our simple analytical approach with actual tolerance factor 1.287 have been scrutinized by using five test cases with varied operating conditions. Calculated results show the discrepancies of the pressure drops between the estimated and measured values for both liquid helium and cold gaseous helium transfer lines have an underestimate 0.11 kPa and an overestimate 0.09 kPa, respectively. A discrepancy is foreseen, but remains acceptable for engineering applications from a practical point of view. CONCLUSIONS: The simple analytical approach with the introduction of a tolerance factor can provide not only insight into optimizing the choice of each lossy cryogenic piping element of the transfer lines in the design phase but also firm guidance for upgrading the present cryogenic transfer lines for its subsequent application.