Prevalence of genotypic HIV-1 drug resistance in Thailand, 2002

BACKGROUND: The prices of reverse transcriptase (RT) inhibitors in Thailand have been reduced since December 1, 2001. It is expected that reduction in the price of these inhibitors may influence the drug resistance mutation pattern of HIV-1 among infected people. This study reports the frequency of HIV-1 genetic mutation associated with drug resistance in antiretroviral-treated patients from Thailand. METHODS: Genotypic resistance testing was performed on samples collected in 2002 from 88 HIV-1 infected individuals. Automated DNA sequencing was used to genotype the HIV-1 polymerase gene isolated from patients' plasma. RESULTS: Resistance to protease inhibitors, nucleoside and non-nucleoside reverse transcriptase inhibitors were found in 10 (12%), 42 (48%) and 19 (21%) patients, respectively. The most common drug resistance mutations in the protease gene were at codon 82 (8%), 90 (7%) and 54 (6%), whereas resistant mutations at codon 215 (45%), 67 (40%), 41 (38%) and 184 (27%) were commonly found in the RT gene. This finding indicates that genotypic resistance to nucleoside reverse transcriptase inhibitors was prevalent in 2002. The frequency of resistant mutations corresponding to non-nucleoside reverse transcriptase inhibitors was three times higher-, while resistant mutation corresponding to protease inhibitors was two times lower than those frequencies determined in 2001. CONCLUSION: This study shows that the frequencies of RT inhibitor resistance mutations have been increased after the reduction in the price of RT inhibitors since December 2001. We believe that this was an important factor that influenced the mutation patterns of HIV-1 protease and RT genes in Thailand

Melatonin regulates the aging mouse hippocampal homeostasis via the sirtuin1-FOXO1 pathway

Sirtuin1 (SIRT1) and forkhead box transcription factor O subfamily 1 (FOXO1) play vital roles in the maintenance of hippocampal neuronal homeostasis during aging. Our previous study showed that melatonin, a hormone mainly secreted by the pineal gland, restored the impaired memory of aged mice. Age-related neuronal energy deficits contribute to the pathogenesis of several neurodegenerative disorders. An attempt has been made to determine whether the effect of melatonin is mediated through the SIRT1-FOXO1 pathways. The present results showed that aged mice (22 months old) exhibited significantly downregulated SIRT1, FOXO1, and melatonin receptors MT1 and MT2 protein expression but upregulated tumor suppressor protein 53 (p53), acetyl-p53 protein (Ac-p53), mouse double minute 2 homolog (MDM2), Dickkopf-1 (DKK1) protein expression in mouse hippocampus com- pared with the young group. Melatonin treatment (10 mg/kg, daily in drinking water for 6 months) in aged mice significantly attenuated the age-induced downregulation of SIRT1, FOXO1, MT1 and MT2 protein expression and attenuated the age-induced increase in p53, ac-p53, MDM2, and DKK1 protein and mRNA expression. Mel- atonin decreased p53 and MDM2 expression, which led to a decrease in FOXO1 degradation. These present results suggest that melatonin may help the hippocampal neuronal homeostasis by increasing SIRT1, FOXO1 and mela- tonin receptors expression while decreasing DKK1 expression in the aging hippocampus. DKK1 can be induced by the accumulation of amyloid β (Aβ) which is the major hallmark of Alzheimer’s disease

Predicting Inactive Conformations of Protein Kinases Using Active Structures: Conformational Selection of Type-II Inhibitors

Protein kinases have been found to possess two characteristic conformations in their activation-loops: the active DFG-in conformation and the inactive DFG-out conformation. Recently, it has been very interesting to develop type-II inhibitors which target the DFG-out conformation and are more specific than the type-I inhibitors binding to the active DFG-in conformation. However, solving crystal structures of kinases with the DFG-out conformation remains a challenge, and this seriously hampers the application of the structure-based approaches in development of novel type-II inhibitors. To overcome this limitation, here we present a computational approach for predicting the DFG-out inactive conformation using the DFG-in active structures, and develop related conformational selection protocols for the uses of the predicted DFG-out models in the binding pose prediction and virtual screening of type-II ligands. With the DFG-out models, we predicted the binding poses for known type-II inhibitors, and the results were found in good agreement with the X-ray crystal structures. We also tested the abilities of the DFG-out models to recognize their specific type-II inhibitors by screening a database of small molecules. The AUC (area under curve) results indicated that the predicted DFG-out models were selective toward their specific type-II inhibitors. Therefore, the computational approach and protocols presented in this study are very promising for the structure-based design and screening of novel type-II kinase inhibitors

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space

An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction

Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics:

The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class