Insights into the product release mechanism of dengue virus NS3 helicase.

The non-structural protein 3 helicase (NS3h) is a multifunctional protein that is critical in RNA replication and other stages in the flavivirus life cycle. NS3h uses energy from ATP hydrolysis to translocate along single stranded nucleic acid and to unwind double stranded RNA. Here we present a detailed mechanistic analysis of the product release stage in the catalytic cycle of the dengue virus (DENV) NS3h. This study is based on a combined experimental and computational approach of product-inhibition studies and free energy calculations. Our results support a model in which the catalytic cycle of ATP hydrolysis proceeds through an ordered sequential mechanism that includes a ternary complex intermediate (NS3h-Pi-ADP), which evolves releasing the first product, phosphate (Pi), and subsequently ADP. Our results indicate that in the product release stage of the DENV NS3h a novel open-loop conformation plays an important role that may be conserved in NS3 proteins of other flaviviruses as well.

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking.

Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

Solvent effects on the NMR shieldings of stacked DNA base pairs.

Stacking effects are among the most important effects in DNA. We have recently studied their influence in fragments of DNA through the analysis of NMR magnetic shieldings, firstly in vacuo. As a continuation of this line of research we show here the influence of solvent effects on the shieldings through the application of both explicit and implicit models. We found that the explicit solvent model is more appropriate for consideration due to the results matching better in general with experiments, as well as providing clear knowledge of the electronic origin of the value of the shieldings. Our study is grounded on a recently developed theoretical model of our own, by which we are able to learn about the magnetic effects of given fragments of DNA molecules on selected base pairs. We use the shieldings of the atoms of a central base pair (guanine-cytosine) of a selected fragment of DNA molecules as descriptors of physical effects, like π-stacking and solvent effects. They can be taken separately and altogether. The effect of π-stacking is introduced through the addition of some pairs above and below of the central base pair, and now, the solvent effect is considered including a network of water molecules that consist of two solvation layers, which were fixed in the calculations performed in all fragments. We show that the solvent effects enhance the stacking effects on the magnetic shieldings of atoms that belong to the external N-H bonds. The net effect is of deshielding on both atoms. There is also a deshielding effect on the carbon atoms that belong to CO bonds, for which the oxygen atom has an explicit hydrogen bond (HB) with a solvent water molecule. Solvent effects are found to be no higher than a few percent of the total value of the shieldings (between 1% and 5%) for most atoms, although there are few for which such an effect can be higher. There is one nitrogen atom, the acceptor of the HB between guanine and cytosine, that is more highly shielded (around 15 ppm or 10%) when the explicit solvent is considered. In a similar manner, the most external nitrogen atom of cytosine and the hydrogen atom that is bonded to it are highly deshielded (around 10 ppm for nitrogen and around 3 ppm for hydrogen).

Bioinformatic analysis of RHO family of GTPases identifies RAC1 pharmacological inhibition as a new therapeutic strategy for hepatocellular carcinoma.

OBJECTIVE: The RHO family of GTPases, particularly RAC1, has been linked with hepatocarcinogenesis, suggesting that their inhibition might be a rational therapeutic approach. We aimed to identify and target deregulated RHO family members in human hepatocellular carcinoma (HCC). DESIGN: We studied expression deregulation, clinical prognosis and transcription programmes relevant to HCC using public datasets. The therapeutic potential of RAC1 inhibitors in HCC was study in vitro and in vivo. RNA-Seq analysis and their correlation with the three different HCC datasets were used to characterise the underlying mechanism on RAC1 inhibition. The therapeutic effect of RAC1 inhibition on liver fibrosis was evaluated. RESULTS: Among the RHO family of GTPases we observed that RAC1 is upregulated, correlates with poor patient survival, and is strongly linked with a prooncogenic transcriptional programme. From a panel of novel RAC1 inhibitors studied, 1D-142 was able to induce apoptosis and cell cycle arrest in HCC cells, displaying a stronger effect in highly proliferative cells. Partial rescue of the RAC1-related oncogenic transcriptional programme was obtained on RAC1 inhibition by 1D-142 in HCC. Most importantly, the RAC1 inhibitor 1D-142 strongly reduce tumour growth and intrahepatic metastasis in HCC mice models. Additionally, 1D-142 decreases hepatic stellate cell activation and exerts an anti-fibrotic effect in vivo. CONCLUSIONS: The bioinformatics analysis of the HCC datasets, allows identifying RAC1 as a new therapeutic target for HCC. The targeted inhibition of RAC1 by 1D-142 resulted in a potent antitumoural effect in highly proliferative HCC established in fibrotic livers.

Artificial intelligence in the early stages of drug discovery.

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles.

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

A fluorescence nanoscopy marker for corticotropin-releasing hormone type 1 receptor: computer design, synthesis, signaling effects, super-resolved fluorescence imaging, and in situ affinity constant in cells.

Class B G protein-coupled receptors (GPCRs) are involved in a variety of human pathophysiological states. These groups of membrane receptors are less studied than class A GPCRs due to the lack of structural information, delayed small molecule drug discovery, and scarce fluorescence detection tools available. The class B corticotropin-releasing hormone type 1 receptor (CRHR1) is a key player in the stress response whose dysregulation is critically involved in stress-related disorders: psychiatric conditions (i.e. depression, anxiety, and addictions), neuroendocrinological alterations, and neurodegenerative diseases. Here, we present a strategy to label GPCRs with a small fluorescent antagonist that permits the observation of the receptor in live cells through stochastic optical reconstruction microscopy (STORM) with 23 nm resolution. The marker, an aza-BODIPY derivative, was designed based on computational docking studies, then synthesized, and finally tested in biological cells. Experiments on hippocampal neurons demonstrate antagonist effects in similar concentrations as the well-established antagonist CP-376395. A quantitative analysis of two color STORM images enabled the determination of the binding affinity of the new marker in the cellular environment.

Discovery of novel dengue virus entry inhibitors via a structure-based approach.

Dengue is a mosquito-borne virus that has become a major public health concern worldwide in recent years. However, the current treatment for dengue disease is only supportive therapy, and no specific antivirals are available to control the infections. Therefore, the need for safe and effective antiviral drugs against this virus is of utmost importance. Entry of the dengue virus (DENV) into a host cell is mediated by its major envelope protein, E. The crystal structure of the E protein reveals a hydrophobic pocket occupied by the detergent n-octyl-ß-d-glucoside (ß-OG) lying at a hinge region between domains I and II, which is important for the low-pH-triggered conformational rearrangement required for fusion. Thus, the E protein is an attractive target for the development of antiviral agents. In this work, we performed prospective docking-based virtual screening to identify small molecules that likely bind to the ß-OG binding site. Twenty-three structurally different compounds were identified and two of them had an EC50 value in the low micromolar range. In particular, compound 2 (EC50=3.1µM) showed marked antiviral activity with a good therapeutic index. Molecular dynamics simulations were used in an attempt to characterize the interaction of 2 with protein E, thus paving the way for future ligand optimization endeavors. These studies highlight the possibility of using a new class of DENV inhibitors against dengue.

Investigating molecular dynamics-guided lead optimization of EGFR inhibitors.

The epidermal growth factor receptor (EGFR) is part of an extended family of proteins that together control aspects of cell growth and development, and thus a validated target for drug discovery. We explore in this work the suitability of a molecular dynamics-based end-point binding free energy protocol to estimate the relative affinities of a virtual combinatorial library designed around the EGFR model inhibitor 6{1} as a tool to guide chemical synthesis toward the most promising compounds. To investigate the validity of this approach, selected analogs including some with better and worse predicted affinities relative to 6{1} were synthesized, and their biological activity determined. To understand the binding determinants of the different analogs, hydrogen bonding and van der Waals contributions, and water molecule bridging in the EGFR-analog complexes were analyzed. The experimental validation was in good qualitative agreement with our theoretical calculations, while also a 6-dibromophenyl-substituted compound with enhanced inhibitory effect on EGFR compared to the reference ligand was obtained.

Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description.

Structure-based virtual screening is currently an established tool in drug lead discovery projects. Although in the last years the field saw an impressive progress in terms of algorithm development, computational performance, and retrospective and prospective applications in ligand identification, there are still long-standing challenges where further improvement is needed. In this review, we consider the conceptual frame, state-of-the-art and recent developments of three critical "structural" issues in structure-based drug lead discovery: the use of homology modeling to accurately model the binding site when no experimental structures are available, the necessity of accounting for the dynamics of intrinsically flexible systems as proteins, and the importance of considering active site water molecules in lead identification and optimization campaigns.

Isoform selectivity of adenylyl cyclase inhibitors: characterization of known and novel compounds.

Nine membrane-bound adenylyl cyclase (AC) isoforms catalyze the production of the second messenger cyclic AMP (cAMP) in response to various stimuli. Reduction of AC activity has well documented benefits, including benefits for heart disease and pain. These roles have inspired development of isoform-selective AC inhibitors, a lack of which currently limits exploration of functions and/or treatment of dysfunctions involving AC/cAMP signaling. However, inhibitors described as AC5- or AC1-selective have not been screened against the full panel of AC isoforms. We have measured pharmacological inhibitor profiles for all transmembrane AC isoforms. We found that 9-(tetrahydro-2-furanyl)-9H-purin-6-amine (SQ22,536), 2-amino-7-(furanyl)-7,8-dihydro-5(6H)-quinazolinone (NKY80), and adenine 9-ß-d-arabinofuranoside (Ara-A), described as supposedly AC5-selective, do not discriminate between AC5 and AC6, whereas the putative AC1-selective inhibitor 5-[[2-(6-amino-9H-purin-9-yl)ethyl]amino]-1-pentanol (NB001) does not directly target AC1 to reduce cAMP levels. A structure-based virtual screen targeting the ATP binding site of AC was used to identify novel chemical structures that show some preference for AC1 or AC2. Mutation of the AC2 forskolin binding pocket does not interfere with inhibition by SQ22,536 or the novel AC2 inhibitor, suggesting binding to the catalytic site. Thus, we show that compounds lacking the adenine chemical signature and targeting the ATP binding site can potentially be used to develop AC isoform-specific inhibitors, and discuss the need to reinterpret literature using AC5/6-selective molecules SQ22,536, NKY80, and Ara-A.

How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

X-aptamers: a bead-based selection method for random incorporation of druglike moieties onto next-generation aptamers for enhanced binding.

By combining pseudorandom bead-based aptamer libraries with conjugation chemistry, we have created next-generation aptamers, X-aptamers (XAs). Several X-ligands can be added in a directed or random fashion to the aptamers to further enhance their binding affinities for the target proteins. Here we describe the addition of a drug (N-acetyl-2,3-dehydro-2-deoxyneuraminic acid), demonstrated to bind to CD44-HABD, to a complete monothioate backbone-substituted aptamer to increase its binding affinity for the target protein by up to 23-fold, while increasing the drug's level of binding 1-million fold.

Ligand and decoy sets for docking to G protein-coupled receptors.

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.

Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point.

Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.

Guided structure-based ligand identification and design via artificial intelligence modeling.

INTRODUCTION: The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identification of ligands for validated therapeutic targets, as well as the de novo design of ligands through generative models. AREAS COVERED: Herein, the authors review the contributions between the 2019 to present period regarding the application of AI methods to structure-based virtual screening (SBVS) which encompasses mainly molecular docking applications - binding pose prediction and binary classification for ligand or hit identification-, as well as de novo drug design driven by machine learning (ML) generative models, and the validation of AI models in structure-based screening. Studies are reviewed in terms of their main objective, used databases, implemented methodology, input and output, and key results . EXPERT OPINION: More profound analyses regarding the validity and applicability of AI methods in DD have begun to appear. In the near future, we expect to see more structure-based generative models- which are scarce in comparison to ligand-based generative models-, the implementation of standard guidelines for validating the generated structures, and more analyses regarding the validation of AI methods in structure-based DD.

Quantum mechanical binding free energy calculation for phosphopeptide inhibitors of the Lck SH2 domain.

The accurate and efficient calculation of binding free energies is essential in computational biophysics. We present a linear-scaling quantum mechanical (QM)-based end-point method termed MM/QM-COSMO to calculate binding free energies in biomolecular systems, with an improved description of entropic changes. Molecular dynamics trajectories are re-evaluated using a semiempirical Hamiltonian and a continuum solvent model; translational and rotational entropies are calculated using configurational integrals, and internal entropy is calculated using the harmonic oscillator approximation. As an application, we studied the binding of a series of phosphotyrosine tetrapeptides to the human Lck SH2 domain, a key component in intracellular signal transduction, modulation of which can have therapeutic relevance in the treatment of cancer, osteoporosis, and autoimmune diseases. Calculations with molecular mechanics Poisson-Boltzmann, and generalized Born surface area methods showed large discrepancies with experimental data stemming from the enthalpic component, in agreement with an earlier report. The empirical force field-based solvent interaction energy scoring function yielded improved results, with average unsigned error of 3.6 kcal/mol, and a better ligand ranking. The MM/QM-COSMO method exhibited the best agreement both for absolute (average unsigned error = 0.7 kcal/mol) and relative binding free energy calculations. These results show the feasibility and promise of a full QM-based end-point method with an adequate balance of accuracy and computational efficiency.

Computational and experimental studies of the interaction between phospho-peptides and the C-terminal domain of BRCA1.

The C-terminal domain of BRCA1(BRCT) is involved in the DNA repair pathway by recognizing the pSXXF motif in interacting proteins. It has been reported that short peptides containing this motif bind to BRCA1(BRCT) in the micromolar range with high specificity. In this work, the binding of pSXXF peptides has been studied computationally and experimentally in order to characterize their interaction with BRCA1(BRCT). Elucidation of the contacts that drive the protein-ligand interaction is critical for the development of high affinity small-molecule BRCA1 inhibitors. Molecular dynamics (MD) simulations revealed the key role of threonine at the peptide P+2 position in providing structural rigidity to the ligand in the bound state. The mutation at P+1 had minor effects. Peptide extension at the N-terminal position with the naphthyl amino acid exhibited a modest increase in binding affinity, what could be explained by the dispersion interaction of the naphthyl side-chain with a hydrophobic patch. Three in silico end-point methods were considered for the calculation of binding free energy. The Molecular Mechanics Poisson-Boltzmann Surface Area and the Solvated Interaction Energy methods gave reasonable agreement with experimental data, exhibiting a Pearlman predictive index of 0.71 and 0.78, respectively. The MM-quantum mechanics-surface area method yielded improved results, which was characterized by a Pearlman index of 0.78. The correlation coefficients were 0.59, 0.61 and 0.69, respectively. The ability to apply a QM level of theory within an end-point binding free energy protocol may provide a way for a consistent improvement of accuracy in computer-aided drug design.

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds.

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

Evaluation of Silicone Fluids and Resins as CO2 Thickeners for Enhanced Oil Recovery Using a Computational and Experimental Approach.

CO2 thickeners have the potential to be a game changer for enhanced oil recovery, carbon capture utilization and storage, and hydraulic fracturing. Thickener design is challenging due to polymers' low solubility in supercritical CO2 (scCO2) and the difficulty of substantially increasing the viscosity of CO2. In this contribution, we present a framework to design CO2 soluble thickeners, combining calculations using a quantum mechanical solvation model with direct laboratory viscosity testing. The conductor-like polarizable continuum model for solvation free-energy calculations was used to determine functional silicone and silsesquioxane solubilities in scCO2. This method allowed for a fast and efficient identification of CO2-soluble compounds, revealing silsesquioxanes as more CO2-philic than linear polydimethylsiloxane (PDMS), the most efficient non-fluorinated thickener know to date. The rolling ball apparatus was used to measure the viscosity of scCO2 with both PDMS and silicone resins with added silica nanoparticles. Methyl silicone resins were found to be stable and fast to disperse in scCO2 while having a significant thickening effect. They have a larger effect on the solution viscosity than higher-molecular-weight PDMS and are able to thicken CO2 even at high temperatures. Silicone resins are thus shown to be promising scCO2 thickeners, exhibiting enhanced solubility and good rheological properties, while also having a moderate cost and being easily commercially attainable.