Membrane and Protein Interactions of the Pleckstrin Homology Domain Superfamily.

The human genome encodes about 285 proteins that contain at least one annotated pleckstrin homology (PH) domain. As the first phosphoinositide binding module domain to be discovered, the PH domain recruits diverse protein architectures to cellular membranes. PH domains constitute one of the largest protein superfamilies, and have diverged to regulate many different signaling proteins and modules such as Dbl homology (DH) and Tec homology (TH) domains. The ligands of approximately 70 PH domains have been validated by binding assays and complexed structures, allowing meaningful extrapolation across the entire superfamily. Here the Membrane Optimal Docking Area (MODA) program is used at a genome-wide level to identify all membrane docking PH structures and map their lipid-binding determinants. In addition to the linear sequence motifs which are employed for phosphoinositide recognition, the three dimensional structural features that allow peripheral membrane domains to approach and insert into the bilayer are pinpointed and can be predicted ab initio. The analysis shows that conserved structural surfaces distinguish which PH domains associate with membrane from those that do not. Moreover, the results indicate that lipid-binding PH domains can be classified into different functional subgroups based on the type of membrane insertion elements they project towards the bilayer

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace

Retrospective analysis reveals significant association of hypoglycemia with tramadol and methadone in contrast to other opioids.

Tramadol is one of the most commonly used analgesics worldwide, classified as having a low abuse potential by U.S. Drug Enforcement Agency, and often recommended in pain management guidelines. Its pain-relieving mechanism of action is attributed to mild μ-opioid receptor agonism, serotonin and norepinephrine mediated nociception modulation, and N-methyl-D-aspartate receptor, NMDAR, antagonism. However, recent case reports and case-control studies have shown an association between tramadol use and hypoglycemia. The growing concern over increasing tramadol use and unexpected side effects warranted a further comparative and quantitative analysis of tramadol adverse reactions. In this study we analyzed over twelve million reports from United States Food and Drug Administration Adverse Event Reporting System and provided evidence of increased propensity for hypoglycemia in patients taking tramadol when compared to patients taking other opioids, serotonin-norepinephrine reuptake inhibitors, and drugs affecting NMDAR activity. Additionally, we identified that only methadone from the opioid cohort behaves similarly to tramadol and has an association with hypoglycemia

Match-Only Integral Distribution (MOID) Algorithm for high-density oligonucleotide array analysis

BACKGROUND: High-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics. RESULTS: A new algorithm, Match-Only Integral Distribution (MOID), was developed to analyze high-density oligonucleotide arrays. Using known data from both spiking experiments and no-change experiments performed with Affymetrix GeneChip(®) arrays, MOID and the Affymetrix algorithm implemented in Microarray Suite 4.0 (MAS4) were compared. While MOID gave similar performance to MAS4 in the spiking experiments, better performance was observed in the no-change experiments. MOID also provides a set of alternative statistical analysis tools to MAS4. There are two main features that distinguish MOID from MAS4. First, MOID uses continuous P values for the likelihood of gene presence, while MAS4 resorts to discrete absolute calls. Secondly, MOID uses heuristic confidence intervals for both gene expression levels and fold change values, while MAS4 categorizes the significance of gene expression level changes into discrete fold change calls. CONCLUSIONS: The results show that by using MOID, Affymetrix GeneChip(®) arrays may need as little as ten probes per gene without compromising analysis accuracy

Population scale data reveals the antidepressant effects of ketamine and other therapeutics approved for non-psychiatric indications.

Current therapeutic approaches to depression fail for millions of patients due to lag in clinical response and non-adherence. Here we provide new support for the antidepressant effect of an anesthetic drug, ketamine, by Inverse-Frequency Analysis of eight million reports from the FDA Adverse Effect Reporting System. The results of the examination of population scale data revealed that patients who received ketamine had significantly lower frequency of reports of depression than patients who took any other combination of drugs for pain. The analysis also revealed that patients who took ketamine had significantly lower frequency of reports of pain and opioid induced side effects, implying ketamine's potential to act as a beneficial adjunct agent in pain management pharmacotherapy. Further, the Inverse-Frequency Analysis methodology provides robust statistical support for the antidepressant action of other currently approved therapeutics including diclofenac and minocycline

Proton-pump inhibitor use is associated with a broad spectrum of neurological adverse events including impaired hearing, vision, and memory.

Proton-pump inhibitors, PPIs, are considered effective therapy for stomach acid suppression due to their irreversible inhibition of the hydrogen/potassium pump in the gastric parietal cells. They are widely prescribed and are considered safe for over-the-counter use. Recent studies have shown an association between PPI use and Alzheimer dementia, while others have disputed that connection. We analyzed over ten million United States Food and Drug Administration Adverse Event Reporting System reports, including over forty thousand reports containing PPIs, and provided evidence of increased propensity for memory impairment among PPI reports when compared to histamine-2 receptor antagonist control group. Furthermore, we found significant associations of PPI use with a wide range of neurological adverse reactions including, migraine, several peripheral neuropathies, and visual and auditory neurosensory abnormalities

Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles

Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling