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

Proton Magnetic Resonance Spectroscopy Reveals Neuroprotection by Oral Minocycline in a Nonhuman Primate Model of Accelerated NeuroAIDS

Background: Despite the advent of highly active anti-retroviral therapy (HAART), HIV-associated neurocognitive disorders continue to be a significant problem. In efforts to understand and alleviate neurocognitive deficits associated with HIV, we used an accelerated simian immunodeficiency virus (SIV) macaque model of NeuroAIDS to test whether minocycline is neuroprotective against lentiviral-induced neuronal injury. Methodology/Principal Findings: Eleven rhesus macaques were infected with SIV, depleted of CD8+ lymphocytes, and studied until eight weeks post inoculation (wpi). Seven animals received daily minocycline orally beginning at 4 wpi. Neuronal integrity was monitored in vivo by proton magnetic resonance spectroscopy and post-mortem by immunohistochemistry for synaptophysin (SYN), microtubule-associated protein 2 (MAP2), and neuronal counts. Astrogliosis and microglial activation were quantified by measuring glial fibrillary acidic protein (GFAP) and ionized calcium binding adaptor molecule 1 (IBA-1), respectively. SIV infection followed by CD8+ cell depletion induced a progressive decline in neuronal integrity evidenced by declining N-acetylaspartate/creatine (NAA/Cr), which was arrested with minocycline treatment. The recovery of this ratio was due to increases in NAA, indicating neuronal recovery, and decreases in Cr, likely reflecting downregulation of glial cell activation. SYN, MAP2, and neuronal counts were found to be higher in minocycline-treated animals compared to untreated animals while GFAP and IBA-1 expression were decreased compared to controls. CSF and plasma viral loads were lower in MN-treated animals. Conclusions/Significance: In conclusion, oral minocycline alleviates neuronal damage induced by the AIDS virus

Structural Optimization and De Novo Design of Dengue Virus Entry Inhibitory Peptides

Viral fusogenic envelope proteins are important targets for the development of inhibitors of viral entry. We report an approach for the computational design of peptide inhibitors of the dengue 2 virus (DENV-2) envelope (E) protein using high-resolution structural data from a pre-entry dimeric form of the protein. By using predictive strategies together with computational optimization of binding “pseudoenergies”, we were able to design multiple peptide sequences that showed low micromolar viral entry inhibitory activity. The two most active peptides, DN57opt and 1OAN1, were designed to displace regions in the domain II hinge, and the first domain I/domain II beta sheet connection, respectively, and show fifty percent inhibitory concentrations of 8 and 7 µM respectively in a focus forming unit assay. The antiviral peptides were shown to interfere with virus:cell binding, interact directly with the E proteins and also cause changes to the viral surface using biolayer interferometry and cryo-electron microscopy, respectively. These peptides may be useful for characterization of intermediate states in the membrane fusion process, investigation of DENV receptor molecules, and as lead compounds for drug discovery

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking

Impact of the HIV-1 env Genetic Context outside HR1–HR2 on Resistance to the Fusion Inhibitor Enfuvirtide and Viral Infectivity in Clinical Isolates

Resistance mutations to the HIV-1 fusion inhibitor enfuvirtide emerge mainly within the drug's target region, HR1, and compensatory mutations have been described within HR2. The surrounding envelope (env) genetic context might also contribute to resistance, although to what extent and through which determinants remains elusive. To quantify the direct role of the env context in resistance to enfuvirtide and in viral infectivity, we compared enfuvirtide susceptibility and infectivity of recombinant viral pairs harboring the HR1–HR2 region or the full Env ectodomain of longitudinal env clones from 5 heavily treated patients failing enfuvirtide therapy. Prior to enfuvirtide treatment onset, no env carried known resistance mutations and full Env viruses were on average less susceptible than HR1–HR2 recombinants. All escape clones carried at least one of G36D, V38A, N42D and/or N43D/S in HR1, and accordingly, resistance increased 11- to 2800-fold relative to baseline. Resistance of full Env recombinant viruses was similar to resistance of their HR1–HR2 counterpart, indicating that HR1 and HR2 are the main contributors to resistance. Strictly X4 viruses were more resistant than strictly R5 viruses, while dual-tropic Envs featured similar resistance levels irrespective of the coreceptor expressed by the cell line used. Full Env recombinants from all patients gained infectivity under prolonged drug pressure; for HR1–HR2 viruses, infectivity remained steady for 3/5 patients, while for 2/5 patients, gains in infectivity paralleled those of the corresponding full Env recombinants, indicating that the env genetic context accounts mainly for infectivity adjustments. Phylogenetic analyses revealed that quasispecies selection is a step-wise process where selection of enfuvirtide resistance is a dominant factor early during therapy, while increased infectivity is the prominent driver under prolonged therapy

Molecular mechanisms of severe acute respiratory syndrome (SARS)

Severe acute respiratory syndrome (SARS) is a new infectious disease caused by a novel coronavirus that leads to deleterious pulmonary pathological features. Due to its high morbidity and mortality and widespread occurrence, SARS has evolved as an important respiratory disease which may be encountered everywhere in the world. The virus was identified as the causative agent of SARS due to the efforts of a WHO-led laboratory network. The potential mutability of the SARS-CoV genome may lead to new SARS outbreaks and several regions of the viral genomes open reading frames have been identified which may contribute to the severe virulence of the virus. With regard to the pathogenesis of SARS, several mechanisms involving both direct effects on target cells and indirect effects via the immune system may exist. Vaccination would offer the most attractive approach to prevent new epidemics of SARS, but the development of vaccines is difficult due to missing data on the role of immune system-virus interactions and the potential mutability of the virus. Even in a situation of no new infections, SARS remains a major health hazard, as new epidemics may arise. Therefore, further experimental and clinical research is required to control the disease

Simple Linear Model Provides Highly Accurate Genotypic Predictions of HIV-1 Drug Resistance

Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model out-performed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens. </jats:p