Unveiling New Druggable Pockets in Influenza Non-Structural Protein 1: NS1-Host Interactions as Antiviral Targets for Flu.

Influenza viruses are responsible for significant morbidity and mortality worldwide in winter seasonal outbreaks and in flu pandemics. Influenza viruses have a high rate of evolution, requiring annual vaccine updates and severely diminishing the effectiveness of the available antivirals. Identifying novel viral targets and developing new effective antivirals is an urgent need. One of the most promising new targets for influenza antiviral therapy is non-structural protein 1 (NS1), a highly conserved protein exclusively expressed in virus-infected cells that mediates essential functions in virus replication and pathogenesis. Interaction of NS1 with the host proteins PI3K and TRIM25 is paramount for NS1's role in infection and pathogenesis by promoting viral replication through the inhibition of apoptosis and suppressing interferon production, respectively. We, therefore, conducted an analysis of the druggability of this viral protein by performing molecular dynamics simulations on full-length NS1 coupled with ligand pocket detection. We identified several druggable pockets that are partially conserved throughout most of the simulation time. Moreover, we found out that some of these druggable pockets co-localize with the most stable binding regions of the protein-protein interaction (PPI) sites of NS1 with PI3K and TRIM25, which suggests that these NS1 druggable pockets are promising new targets for antiviral development.

Designing optimized drug candidates with Generative Adversarial Network.

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine [Formula: see text] receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

Explainable deep drug-target representations for binding affinity prediction.

BACKGROUND: Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model's decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data. RESULTS: The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug-target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches. CONCLUSIONS: This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process.

Conformational Dynamics of the Soluble and Membrane-Bound Forms of Interleukin-1 Receptor Type-1: Insights into Linker Flexibility and Domain Orientation.

Interleukin-1 receptor type 1 (IL-1R1) is a key player in inflammation and immune responses. This receptor regulates IL-1 activity in two forms: as a membrane-bound form and as a soluble ectodomain. The details and differences between the conformational dynamics of the membrane-bound and the soluble IL-1R1 ectodomains (ECDs) remain largely elusive. Here, we study and compare the structural dynamics of the soluble and membrane-bound IL-1R1-ECDs using molecular dynamics (MD) simulations, focusing on the flexible interdomain linker of the ECD, as well as the spatial rearrangements between the Ig-like domains of the ECD. To explore the membrane-bound conformations, a full-length IL-1R1 structural model was developed and subjected to classical equilibrium MD. Comparative analysis of multiple MD trajectories of the soluble and the membrane-bound IL-1R1-ECDs reveals that (i) as somewhat expected, the extent of the visited "open-to-closed" transitional states differs significantly between the soluble and membrane-bound forms; (ii) the soluble form presents open-closed transitions, sampling a wider rotational motion between the Ig-like domains of the ECD, visiting closed and "twisted" conformations in higher extent, whereas the membrane-bound form is characterized by more conformationally restricted states; (iii) interestingly, the backbone dihedral angles of residues Glu202, Glu203 and Asn204, located in the flexible linker, display the highest variations during the transition between discrete conformational states detected in IL-1R1, thus appearing to work as the "central wheel of a clock's movement". The simulations and analyses presented in this contribution offer a deeper insight into the structure and dynamics of IL-1R1, which may be explored in a drug discovery setting.

The Therapeutic Prospects of Targeting IL-1R1 for the Modulation of Neuroinflammation in Central Nervous System Disorders.

The interleukin-1 receptor type 1 (IL-1R1) holds pivotal roles in the immune system, as it is positioned at the "epicenter" of the inflammatory signaling networks. Increased levels of the cytokine IL-1 are a recognized feature of the immune response in the central nervous system (CNS) during injury and disease, i.e., neuroinflammation. Despite IL-1/IL-1R1 signaling within the CNS having been the subject of several studies, the roles of IL-1R1 in the CNS cellular milieu still cause controversy. Without much doubt, however, the persistent activation of the IL-1/IL-1R1 signaling pathway is intimately linked with the pathogenesis of a plethora of CNS disease states, ranging from Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), all the way to schizophrenia and prion diseases. Importantly, a growing body of evidence is showing that blocking IL-1R1 signaling via pharmacological or genetic means in different experimental models of said CNS diseases leads to reduced neuroinflammation and delayed disease progression. The aim of this paper is to review the recent progress in the study of the biological roles of IL-1R1, as well as to highlight key aspects that render IL-1R1 a promising target for the development of novel disease-modifying treatments for multiple CNS indications.

Synthesis and structure-activity relationships of new chiral spiro-ß-lactams highly active against HIV-1 and Plasmodium.

The synthesis and antimicrobial activity of new spiro-ß-lactams is reported. The design of the new molecules was based on the structural modulation of two previously identified lead spiro-penicillanates with dual activity against HIV and Plasmodium. The spiro-ß-lactams synthesized were assayed for their in vitro activity against HIV-1, providing relevant structure-activity relationship information. Among the tested compounds, two spirocyclopentenyl-ß-lactams were identified as having remarkable nanomolar activity against HIV-1. Additionally, the same molecules showed promising antiplasmodial activity, inhibiting both the hepatic and blood stages of Plasmodium infection.

Spiro-ß-lactam BSS-730A Displays Potent Activity against HIV and Plasmodium.

The high burden of malaria and HIV/AIDS prevents economic and social progress in developing countries. A continuing need exists for development of novel drugs and treatment regimens for both diseases in order to address the tolerability and long-term safety concerns associated with current treatment options and the emergence of drug resistance. We describe new spiro-ß-lactam derivatives with potent (nM) activity against HIV and Plasmodium and no activity against bacteria and yeast. The best performing molecule of the series, BSS-730A, inhibited both HIV-1 and HIV-2 replication with an IC50 of 13 ± 9.59 nM and P. berghei hepatic infection with an IC50 of 0.55 ± 0.14 µM with a clear impact on parasite development. BSS-730A was also active against the erythrocytic stages of P. falciparum, with an estimated IC50 of 0.43 ± 0.04 µM. Time-of-addition studies showed that BSS-730A potentially affects all stages of the HIV replicative cycle, suggesting a complex mechanism of action. BSS-730A was active against multidrug-resistant HIV isolates, with a median 2.4-fold higher IC50 relative to control isolates. BSS-730A was equally active against R5 and X4 HIV isolates and displayed strong synergism with the entry inhibitor AMD3100. BSS-730A is a promising candidate for development as a potential therapeutic and/or prophylactic agent against HIV and Plasmodium.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

Predicting hydrophobic solvation by molecular simulation: 1. Testing united-atom alkane models.

We present a systematic test of the performance of three popular united-atom force fields-OPLS-UA, GROMOS and TraPPE-at predicting hydrophobic solvation, more precisely at describing the solvation of alkanes in alkanes. Gibbs free energies of solvation were calculated for 52 solute/solvent pairs from Molecular Dynamics simulations and thermodynamic integration making use of the IBERCIVIS volunteer computing platform. Our results show that all force fields yield good predictions when both solute and solvent are small linear or branched alkanes (up to pentane). However, as the size of the alkanes increases, all models tend to increasingly deviate from experimental data in a systematic fashion. Furthermore, our results confirm that specific interaction parameters for cyclic alkanes in the united-atom representation are required to account for the additional excluded volume within the ring. Overall, the TraPPE model performs best for all alkanes, but systematically underpredicts the magnitude of solvation free energies by about 6% (RMSD of 1.2 kJ/mol). Conversely, both GROMOS and OPLS-UA systematically overpredict solvation free energies (by ∼13% and 15%, respectively). The systematic trends suggest that all models can be improved by a slight adjustment of their Lennard-Jones parameters. © 2016 Wiley Periodicals, Inc.

A novel bis-furan scaffold for transthyretin stabilization and amyloid inhibition.

The design and synthesis of a novel bis-furan scaffold tailored for high efficiency at inhibiting transthyretin amyloid formation is reported. In vitro results show that the discovered compounds are more efficient inhibitors of amyloid formation than tafamidis, a drug currently used in the treatment of familial amyloid polyneuropathy (FAP), despite their lower molecular weight and lipophilicity. Moreover, ex vivo experiments with the strongest inhibitor in the series, conducted in human blood plasma from normal and FAP Val30Met-transthyretin carriers, disclose remarkable affinity and selectivity profiles. The promises and challenges facing further development of this compound are discussed under the light of increasing evidence implicating transthyretin stability as a key factor not only in transthyretin amyloidoses and several associated co-morbidities, but also in Alzheimer's disease.

A relational learning approach to Structure-Activity Relationships in drug design toxicity studies.

It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal--iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms.

Toward the discovery of functional transthyretin amyloid inhibitors: application of virtual screening methods.

Inhibition of amyloid fibril formation by stabilization of the native form of the protein transthyretin (TTR) is a viable approach for the treatment of familial amyloid polyneuropathy that has been gaining momentum in the field of amyloid research. The TTR stabilizer molecules discovered to date have shown efficacy at inhibiting fibrilization in vitro but display impairing issues of solubility, affinity for TTR in the blood plasma and/or adverse effects. In this study we present a benchmark of four protein- and ligand-based virtual screening (VS) methods for identifying novel TTR stabilizers: (i) two-dimensional (2D) similarity searches with chemical hashed, pharmacophore, and UNITY fingerprints, (ii) 3D searches based on shape, chemical, and electrostatic similarity, (iii) LigMatch, a new ligand-based method which uses multiple templates and combines 3D geometric hashing with a 2D preselection process, and (iv) molecular docking to consensus X-ray crystal structures of TTR. We illustrate the potential of the best-performing VS protocols to retrieve promising new leads by ranking a tailored library of 2.3 million commercially available compounds. Our predictions show that the top-scoring molecules possess distinctive features from the known TTR binders, holding better solubility, fraction of halogen atoms, and binding affinity profiles. To the best of our knowledge, this is the first attempt to rationalize the utilization of a large battery of in silico screening techniques toward the identification of a new generation of TTR amyloid inhibitors.

Potentially amyloidogenic conformational intermediates populate the unfolding landscape of transthyretin: insights from molecular dynamics simulations.

Protein aggregation into insoluble fibrillar structures known as amyloid characterizes several neurodegenerative diseases, including Alzheimer's, Huntington's and Creutzfeldt-Jakob. Transthyretin (TTR), a homotetrameric plasma protein, is known to be the causative agent of amyloid pathologies such as FAP (familial amyloid polyneuropathy), FAC (familial amyloid cardiomiopathy) and SSA (senile systemic amyloidosis). It is generally accepted that TTR tetramer dissociation and monomer partial unfolding precedes amyloid fibril formation. To explore the TTR unfolding landscape and to identify potential intermediate conformations with high tendency for amyloid formation, we have performed molecular dynamics unfolding simulations of WT-TTR and L55P-TTR, a highly amyloidogenic TTR variant. Our simulations in explicit water allow the identification of events that clearly discriminate the unfolding behavior of WT and L55P-TTR. Analysis of the simulation trajectories show that (i) the L55P monomers unfold earlier and to a larger extent than the WT; (ii) the single alpha-helix in the TTR monomer completely unfolds in most of the L55P simulations while remain folded in WT simulations; (iii) L55P forms, early in the simulations, aggregation-prone conformations characterized by full displacement of strands C and D from the main beta-sandwich core of the monomer; (iv) L55P shows, late in the simulations, severe loss of the H-bond network and consequent destabilization of the CBEF beta-sheet of the beta-sandwich; (v) WT forms aggregation-compatible conformations only late in the simulations and upon extensive unfolding of the monomer. These results clearly show that, in comparison with WT, L55P-TTR does present a much higher probability of forming transient conformations compatible with aggregation and amyloid formation.