A high-throughput virtual screening pipeline has been extended from single energetically minimized structure Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring to ensemble-average ...MM/GBSA rescoring. The correlation coefficient (R2) of calculated and experimental binding free energies for a series of antithrombin ligands has been improved from 0.36 to 0.69 when switching from the single-structure MM/GBSA rescoring to ensemble-average one. The electrostatic interactions in both solute and solvent are identified to play an important role in determining the binding free energy after the decomposition of the calculated binding free energy. The increasing negative charge of the compounds provides a more favorable electrostatic energy change but creates a higher penalty for the solvation free energy. Such a penalty is compensated by the electrostatic energy change, which results in a better binding affinity. A highly hydrophobic ligand is determined by the docking program to be a non-specific binder.
Our results have demonstrated that it is very important to keep a few top poses for rescoring, if the binding is non-specific or the binding mode is not well determined by the docking calculation.
The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the ...body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R2 = 0.94) and logPS (R2 = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. ...Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, “average” mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities (“domains”). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
When I began writing this article, it was just the beginning of COVID-19, when we were not yet social distancing. Everything has changed since then, but not a conviction I have disseminated for more ...than 25 years. More than ever, I maintain that formally addressing the critical visual component of research should be part of every researcher's education. How you visually represent your work not only communicates to others in your discipline. Crafting your visual presentations helps clarify your own thinking and, just as important, is a means of engaging the public. In these challenging times, when society is bombarded with complex information, it is more essential than ever to develop a more accessible and honest visual “language” for the public to understand and gather that information. Formal programs in teaching visual communication will help show the world, outside the research community, how to look at science, understand it, question it, and, hopefully, make smart decisions.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Predicting accurate protein–ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and ...state-of-the-art deep learning approaches. Despite the recent advances in the application of deep convolutional and graph neural network-based approaches, it remains unclear what the relative advantages of each approach are and how they compare with physics-based methodologies that have found more mainstream success in virtual screening pipelines. We present fusion models that combine features and inference from complementary representations to improve binding affinity prediction. This, to our knowledge, is the first comprehensive study that uses a common series of evaluations to directly compare the performance of three-dimensional (3D)-convolutional neural networks (3D-CNNs), spatial graph neural networks (SG-CNNs), and their fusion. We use temporal and structure-based splits to assess performance on novel protein targets. To test the practical applicability of our models, we examine their performance in cases that assume that the crystal structure is not available. In these cases, binding free energies are predicted using docking pose coordinates as the inputs to each model. In addition, we compare these deep learning approaches to predictions based on docking scores and molecular mechanic/generalized Born surface area (MM/GBSA) calculations. Our results show that the fusion models make more accurate predictions than their constituent neural network models as well as docking scoring and MM/GBSA rescoring, with the benefit of greater computational efficiency than the MM/GBSA method. Finally, we provide the code to reproduce our results and the parameter files of the trained models used in this work. The software is available as open source at https://github.com/llnl/fast. Model parameter files are available at ftp://gdo-bioinformatics.ucllnl.org/fast/pdbbind2016_model_checkpoints/.
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Analysis of rates of tunneling across self-assembled monolayers (SAMs) of n-alkanethiolates SC n (with n = number of carbon atoms) incorporated in junctions having structure AgTS-SAM//Ga2O3/EGaIn ...leads to a value for the injection tunnel current density J 0 (i.e., the current flowing through an ideal junction with n = 0) of 103.6±0.3 A·cm–2 (V = +0.5 V). This estimation of J 0 does not involve an extrapolation in length, because it was possible to measure current densities across SAMs over the range of lengths n = 1–18. This value of J 0 is estimated under the assumption that values of the geometrical contact area equal the values of the effective electrical contact area. Detailed experimental analysis, however, indicates that the roughness of the Ga2O3 layer, and that of the AgTS-SAM, determine values of the effective electrical contact area that are ∼10–4 the corresponding values of the geometrical contact area. Conversion of the values of geometrical contact area into the corresponding values of effective electrical contact area results in J 0(+0.5 V) = 107.6±0.8 A·cm–2, which is compatible with values reported for junctions using top-electrodes of evaporated Au, and graphene, and also comparable with values of J 0 estimated from tunneling through single molecules. For these EGaIn-based junctions, the value of the tunneling decay factor β (β = 0.75 ± 0.02 Å–1; β = 0.92 ± 0.02 nC–1) falls within the consensus range across different types of junctions (β = 0.73–0.89 Å–1; β = 0.9–1.1 nC–1). A comparison of the characteristics of conical Ga2O3/EGaIn tips with the characteristics of other top-electrodes suggests that the EGaIn-based electrodes provide a particularly attractive technology for physical-organic studies of charge transport across SAMs.
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Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico ...screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these ...capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. We review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.
•Expanding computing power allows for longer simulation times.•Machine learning techniques make these long simulations interpretable.•Kinetic properties of Alzheimer's disease proteins could lead to therapeutics.•Unsupervised learning has been used on multiple targets in Alzheimer's disease.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Summary
Background
A link between histone deacetylases (HDACs) and intestinal inflammation has been established. HDAC inhibitors that target gut‐selective inflammatory pathways represent a potential ...new therapeutic strategy in patients with refractory inflammatory bowel diseases (IBD).
Aims
To review the use of selective HDAC inhibitors to treat gut inflammation and to highlight potential improvements in selectivity/sensitivity by additional targeting of HDAC‐regulating microRNAs (miRNAs).
Methods
Original articles and reviews have been identified using PubMed search terms: ‘histone deacetylase’, ‘HDAC inhibitor’, ‘inflammatory bowel disease’, ‘gut inflammation,’ and ‘microRNA and HDAC’.
Results
The use of butyrate in distal colitis provided the first evidence that inhibition of HDACs decreases intestinal inflammation in IBD. HDAC inhibitors, such as valproic acid, vorinostat and givinostat, reduce inflammation and tissue damage in experimental murine colitis. Potential mechanisms of action for HDAC inhibitors include increased apoptosis, reduction of pro‐inflammatory cytokine release, regulation of transcription factors and modulation of HDAC‐regulatory miRNAs. HDAC2, HDAC3, HDAC6, HDAC9 and HDAC10 isoforms seem to be specifically involved in chronic intestinal inflammation, justifying the use of selective inhibitors as new therapeutic strategies in IBD. Controlling miRNAs for these isoforms can be identified.
Conclusions
The pro‐inflammatory influence of HDACs in the gut has been confirmed, but mostly in murine studies. Considerably more human data are required to permit development of selective HDAC inhibitors for IBD treatment. Inhibition of key HDAC isoforms in combination with modulation of HDAC‐regulatory miRNAs has potential as a novel therapeutic approach.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
We present a new approach to estimate the binding affinity from given three-dimensional poses of protein-ligand complexes. In this scheme, every protein-ligand atom pair makes an additive free-energy ...contribution. The sum of these pairwise contributions then gives the total binding free energy or the logarithm of the dissociation constant. The pairwise contribution is calculated by a function implemented via a neural network that takes the properties of the two atoms and their distance as input. The pairwise function is trained using a portion of the PDBbind 2018 data set. The model achieves good accuracy for affinity predictions when evaluated with PDBbind 2018 and with the CASF-2016 benchmark, comparing favorably to many scoring functions such as that of AutoDock Vina. The framework here may be extended to incorporate other factors to further improve its accuracy and power.
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