Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein

Neuro-HIV-New insights into pathogenesis and emerging therapeutic targets

HIV-associated neurocognitive disorders (HAND) is a term describing a complex set of cognitive impairments accompanying HIV infection. Successful antiretroviral therapy (ART) reduces the most severe forms of HAND, but milder forms affect over 50% of people living with HIV (PLWH). Pathogenesis of HAND in the ART era remains unknown. A variety of pathogenic factors, such as persistent HIV replication in the brain reservoir, HIV proteins released from infected brain cells, HIV-induced neuroinflammation, and some components of ART, have been implicated in driving HAND pathogenesis in ART-treated individuals. Here, we propose another factor-impairment of cholesterol homeostasis and lipid rafts by HIV-1 protein Nef-as a possible contributor to HAND pathogenesis. These effects of Nef on cholesterol may also underlie the effects of other pathogenic factors that constitute the multifactorial nature of HAND pathogenesis. The proposed Nef- and cholesterol-focused mechanism may provide a long-sought unified explanation of HAND pathogenesis that takes into account all contributing factors. Evidence for the impairment by Nef of cellular cholesterol balance, potential effects of this impairment on brain cells, and opportunities to therapeutically target this element of HAND pathogenesis are discussed.

Computer-aided de novo design and optimization of novel potential inhibitors of HIV-1 Nef protein

Nef is a small accessory protein pivotal in the HIV-1 viral replication cycle. It is a multifunctional protein and its interactions with kinases in host cells have been well characterized through many in vitro and structural studies. Nef forms a homodimer to activate the kinases and subsequently the phosphorylation pathways. The disruption of its homodimerization represents a valuable approach in the search for novel classes of antiretroviral. However, this research avenue is still underdeveloped as just a few Nef inhibitors have been reported so far, with very limited structural information about their mechanism of action. To address this issue, we have employed an in silico structure-based drug design strategy that combines de novo ligand design with molecular docking and extensive molecular dynamics simulations. Since the Nef pocket involved in homodimerization has high lipophilicity, the initial de novo-designed structures displayed poor drug-likeness and solubility. Taking information from the hydration sites within the homodimerization pocket, structural modifications in the initial lead compound have been introduced to improve the solubility and drug-likeness, without affecting the binding profile. We propose lead compounds that can be the starting point for further optimizations to deliver long-awaited, rationally designed Nef inhibitors.

Targeted protein degradation as an antiviral approach

Targeted protein degradation (TPD) has emerged as a new modality in drug discovery. In this approach, small molecules are used to drive degradation of the target protein of interest. Whereas most direct-acting antivirals (DAAs) inhibit or derange the activity of their viral protein targets and have occupancy-driven pharmacology, small molecules with a TPD-based mechanism have event-driven pharmacology exerted through their ability to induce target degradation. These contrasting mechanisms can result in significant differences in drug efficacy and pharmacodynamics that may be useful in the development of new classes of antivirals. While now being widely pursued in cancer biology and autoimmune disease, TPD has not yet been widely applied as an antiviral strategy. Here, we briefly review TPD pharmacology along with the current status of tools available for developing small molecules that achieve antiviral activity through a TPD mechanism. We also highlight aspects of TPD that may be especially useful in the development of antivirals and that we hope will motivate pursuit of TPD-based antivirals by the antivirals research community.

Functional benefit of structural disorder for the replication of measles, Nipah and Hendra viruses

Measles, Nipah and Hendra viruses are severe human pathogens within the Paramyxoviridae family. Their non-segmented, single-stranded, negative-sense RNA genome is encapsidated by the nucleoprotein (N) within a helical nucleocapsid that is the substrate used by the viral RNA-dependent-RNA-polymerase (RpRd) for transcription and replication. The RpRd is a complex made of the large protein (L) and of the phosphoprotein (P), the latter serving as an obligate polymerase cofactor and as a chaperon for N. Both the N and P proteins are enriched in intrinsically disordered regions (IDRs), i.e. regions devoid of stable secondary and tertiary structure. N possesses a C-terminal IDR (NTAIL), while P consists of a large, intrinsically disordered N-terminal domain (NTD) and a C-terminal domain (CTD) encompassing alternating disordered and ordered regions. The V and W proteins, two non-structural proteins that are encoded by the P gene via a mechanism of co-transcriptional edition of the P mRNA, are prevalently disordered too, sharing with P the disordered NTD. They are key players in the evasion of the host antiviral response and were shown to phase separate and to form amyloid-like fibrils in vitro. In this review, we summarize the available information on IDRs within the N, P, V and W proteins from these three model paramyxoviruses and describe their molecular partnership. We discuss the functional benefit of disorder to virus replication in light of the critical role of IDRs in affording promiscuity, multifunctionality, fine regulation of interaction strength, scaffolding functions and in promoting liquid-liquid phase separation and fibrillation.

Antiretroviral Drug Discovery Targeting the HIV-1 Nef Virulence Factor

While antiretroviral drugs have transformed the lives of HIV-infected individuals, chronic treatment is required to prevent rebound from viral reservoir cells. People living with HIV also are at higher risk for cardiovascular and neurocognitive complications, as well as cancer. Finding a cure for HIV-1 infection is therefore an essential goal of current AIDS research. This review is focused on the discovery of pharmacological inhibitors of the HIV-1 Nef accessory protein. Nef is well known to enhance HIV-1 infectivity and replication, and to promote immune escape of HIV-infected cells by preventing cell surface MHC-I display of HIV-1 antigens. Recent progress shows that Nef inhibitors not only suppress HIV-1 replication, but also restore sufficient MHC-I to the surface of infected cells to trigger a cytotoxic T lymphocyte response. Combining Nef inhibitors with latency reversal agents and therapeutic vaccines may provide a path to clearance of viral reservoirs.

Methods for the Discovery and Identification of Small Molecules Targeting Oxidative Stress-Related Protein–Protein Interactions: An Update

The oxidative stress response pathway is one of the hotspots of current pharmaceutical research. Many proteins involved in these pathways work through protein–protein interactions (PPIs). Hence, targeting PPI to develop drugs for an oxidative stress response is a promising strategy. In recent years, small molecules targeting protein–protein interactions (PPIs), which provide efficient methods for drug discovery, are being investigated by an increasing number of studies. However, unlike the enzyme–ligand binding mode, PPIs usually exhibit large and dynamic binding interfaces, which raise additional challenges for the discovery and optimization of small molecules and for the biochemical techniques used to screen compounds and study structure–activity relationships (SARs). Currently, multiple types of PPIs have been clustered into different classes, which make it difficult to design stationary methods for small molecules. Deficient experimental methods are plaguing medicinal chemists and are becoming a major challenge in the discovery of PPI inhibitors. In this review, we present current methods that are specifically used in the discovery and identification of small molecules that target oxidative stress-related PPIs, including proximity-based, affinity-based, competition-based, structure-guided, and function-based methods. Our aim is to introduce feasible methods and their characteristics that are implemented in the discovery of small molecules for different types of PPIs. For each of these methods, we highlight successful examples of PPI inhibitors associated with oxidative stress to illustrate the strategies and provide insights for further design.

Direct interaction between ABCA1 and HIV-1 Nef: Molecular modeling and virtual screening for inhibitors

HIV-1 infection impairs cellular cholesterol efflux by downmodulating the cholesterol transporter ABCA1, leading to metabolic co-morbidities like cardio-vascular disease. The main mechanism of this effect is impairment by the HIV-1 protein Nef of the ABCA1 interaction with the endoplasmic reticulum chaperone calnexin, which leads to a block in ABCA1 maturation followed by its degradation. However, ABCA1 is also downmodulated by Nef delivered with the extracellular vesicles, suggesting involvement of a direct Nef:ABCA1 interaction at the plasma membrane. Here, we present an optimized model of the Nef:ABCA1 interaction, which identifies interaction sites and provides an opportunity to perform a virtual screening for potential inhibitors. Interestingly, the predicted sites on Nef involved in the ABCA1 interaction overlap with those involved in the interaction with calnexin. The compounds previously shown to block Nef:calnexin interaction were among the top ranking ligands in docking simulations with ABCA1-interacting sites on Nef, suggesting the possibility that both interactions can be inhibited by the same chemical compounds. This study identifies a series of compounds for potential development as inhibitors of Nef-mediated co-morbidities of HIV infection.

Improving Docking Power for Short Peptides Using Random Forest

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

Synergy and allostery in ligand binding by HIV-1 Nef

The Nef protein of human and simian immunodeficiency viruses boosts viral pathogenicity through its interactions with host cell proteins. By combining the polyvalency of its large unstructured regions with the binding selectivity and strength of its folded core domain, Nef can associate with many different host cell proteins, thereby disrupting their functions. For example, the combination of a linear proline-rich motif and hydrophobic core domain surface allows Nef to bind tightly and specifically to SH3 domains of Src family kinases. We investigated whether the interplay between Nef's flexible regions and its core domain could allosterically influence ligand selection. We found that the flexible regions can associate with the core domain in different ways, producing distinct conformational states that alter the way in which Nef selects for SH3 domains and exposes some of its binding motifs. The ensuing crosstalk between ligands might promote functionally coherent Nef-bound protein ensembles by synergizing certain subsets of ligands while excluding others. We also combined proteomic and bioinformatics analyses to identify human proteins that select SH3 domains in the same way as Nef. We found that only 3% of clones from a whole-human fetal library displayed Nef-like SH3 selectivity. However, in most cases, this selectivity appears to be achieved by a canonical linear interaction rather than by a Nef-like ‘tertiary' interaction. Our analysis supports the contention that Nef's mode of hijacking SH3 domains is a virus-specific adaptation with no or very few cellular counterparts. Thus, the Nef tertiary binding surface is a promising virus-specific drug target.

Identification of potential antivirals against SARS-CoV-2 using virtual screening method

SARS-CoV-2 has triggered a major epidemic among people around the world, and it is the newest in the sequence to become prevalent among other infectious diseases. The drug repurposing concept has been utilized effectively for numerous viral infections. Considering the situation and the urgency, the idea of drug repurposing for coronavirus infection (COVID-19) is also being studied. The molecular docking method was used for the screening of 29 antiviral drugs against primary protease proteins (MPP) of SARS-CoV-2, spike ecto-domain, spike receptor binding domain, Nsp9 RNA binding protein, and HR2 domain. Among these drugs, in terms of least binding energy, Indinavir, Sorivudine, Cidofovir, and Darunavir showed minimum docking scores with all the key proteins. For ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis, the ADMET properties of the top 4 drug candidates were retrieved through literature study. This analysis revealed that these drug candidates are well metabolized, distributed, and bioavailable, but have some undesirable effects. Furthermore, some approved structural analogues, such as Telbivudine, Tenofovir, Amprenavir, Fosamprenavir, etc., were predicted as similar drugs which may also be used for treating viral infections. We highly recommend these drug candidates as potential fighters against the deadly SARS-CoV-2 virus, and suggest in vivo trials for experimental validation of our findings.

Techniques and Strategies for Potential Protein Target Discovery and Active Pharmaceutical Molecule Screening in a Pandemic

Viruses remain a major challenge in the fierce fight against diseases. There have been many pandemics caused by various viruses throughout the world over the years. Recently, the global outbreak of COVID-19 has had a catastrophic impact on human health and the world economy. Antiviral drug treatment has become another essential means to overcome pandemics in addition to vaccine development. How to quickly find effective drugs that can control the development of a pandemic is a hot issue that still needs to be resolved in medical research today. To accelerate the development of drugs, it is necessary to target the key target proteins in the development of the pandemic, screen active molecules, and develop reliable methods for the identification and characterization of target proteins based on the active ingredients of drugs. This article discusses key target proteins and their biological mechanisms in the progression of COVID-19 and other major epidemics. We propose a model based on these foundations, which includes identifying potential core targets, screening potential active molecules of core targets, and verifying active molecules. This article summarizes the related innovative technologies and methods. We hope to provide a reference for the screening of drugs related to pandemics and the development of new drugs.

Lovastatin Inhibits HIV-1-Induced MHC-I Downregulation by Targeting Nef-AP-1 Complex Formation: A New Strategy to Boost Immune Eradication of HIV-1 Infected Cells

Current combined antiretroviral therapy (cART) mainly targets 3 of the 15 HIV proteins leaving many potential viral vulnerabilities unexploited. To purge the HIV-1 latent reservoir, various strategies including “shock and kill” have been developed. A key question is how to restore impaired immune surveillance. HIV-1 protein Nef has long been known to mediate the downregulation of cell-surface MHC-I and assist HIV-1 to evade the immune system. Through high throughput screening of Food and Drug Administration (FDA) approved drugs, we identified lovastatin, a statin drug, to significantly antagonize Nef to downregulate MHC-I, CD4, and SERINC5, and inhibit the intrinsic infectivity of virions. In addition, lovastatin boosted autologous CTLs to eradicate the infected cells and effectively inhibit the subsequent viral rebound in CD4⁺ T-lymphocytes isolated from HIV-1-infected individuals receiving suppressive cART. Furthermore, we found that lovastatin inhibits Nef-induced MHC-I downregulation by directly binding with Nef and disrupting the Nef–AP-1 complex. These results demonstrate that lovastatin is a promising agent for counteracting Nef-mediated downregulation of MHC-I, CD4, and SERINC5. Lovastatin could potentially be used in the clinic to enhance anti-HIV-1 immune surveillance.

Computational Opportunities and Challenges in Finding Cyclic Peptide Modulators of Protein-Protein Interactions

Peptide cyclization can improve stability, conformational constraint, and compactness. However, apart from beta-turn structures, which are well incorporated into cyclic peptides (CPs), many primary peptide structures and functions are markedly altered by cyclization. Accordingly, to mimic linear peptide interfaces with cyclic peptides, it can be beneficial to screen combinatorial cyclic peptide libraries. Computational methods have been developed to screen CPs, but face a number of challenges. Here, we review methods to develop in silico computational libraries, and the potential for screening naturally occurring libraries of CPs. The simplest and most rapid computational pharmacophore methods that estimate peptide three-dimensional structures to be screened versus targets are relatively easy to implement, and while the constraint on structure imposed by cyclization makes them more effective than the same approaches with linear peptides, there are a large number of limiting assumptions. In contrast, full molecular dynamics simulations of cyclic peptide structures not only are costly to implement, but also require careful attention to interpretation, so that not only is the computation time rate limiting, but the interpretation time is also rate limiting due to the analysis of the typically complex underlying conformational space of CPs. A challenge for the field of computational cyclic peptide screening is to bridge this gap effectively. Natural compound libraries of short cyclic peptides, and short cyclized regions of proteins, encoded in the genomes of many organisms present a potential treasure trove of novel functionality which may be screened via combined computational and experimental screening approaches.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity

Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

INTerface Builder: A Fast Protein-Protein Interface Reconstruction Tool

INTerface Builder (INTBuilder) is a fast, easy-to-use program to compute protein-protein interfaces. It is designed to retrieve interfaces from molecular docking software outputs in an empirically determined linear complexity. INTBuilder directly reads the output formats of popular docking programs like ATTRACT, HEX, MAXDo, and ZDOCK, as well as a more generic format and Protein Data Bank (PDB) files. It identifies interacting surfaces at both residue and atom resolutions. INTerface Builder is an open source software written in C and freely available for noncommercial use (CeCILL license) at https://www.lcqb.upmc.fr/INTBuilder .

How order and disorder within paramyxoviral nucleoproteins and phosphoproteins orchestrate the molecular interplay of transcription and replication

In this review, we summarize computational and experimental data gathered so far showing that structural disorder is abundant within paramyxoviral nucleoproteins (N) and phosphoproteins (P). In particular, we focus on measles, Nipah, and Hendra viruses and highlight both commonalities and differences with respect to the closely related Sendai virus. The molecular mechanisms that control the disorder-to-order transition undergone by the intrinsically disordered C-terminal domain (NTAIL) of their N proteins upon binding to the C-terminal X domain (XD) of the homologous P proteins are described in detail. By having a significant residual disorder, NTAIL-XD complexes are illustrative examples of "fuzziness", whose possible functional significance is discussed. Finally, the relevance of N-P interactions as promising targets for innovative antiviral approaches is underscored, and the functional advantages of structural disorder for paramyxoviruses are pinpointed.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine

Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyze a TCM anti-HIV formula. Herb-target network analysis was carried out between 17 HIV-related proteins and SH formula as well as three control groups based on systematic docking. Inhibitory herbs were identified and active compounds enrichment was found to contribute to the therapeutic effectiveness of herbs. Our study demonstrates that computational analysis of TCMs at herb level can catch the rationale of TCM formulation and serve as guidance for novel TCM formula design.

CD4-gp120 interaction interface - a gateway for HIV-1 infection in human: molecular network, modeling and docking studies

The major causative agent for Acquired Immune Deficiency Syndrome (AIDS) is Human Immunodeficiency Virus-1 (HIV-1). HIV-1 is a predominant subtype of HIV which counts on human cellular mechanism virtually in every aspect of its life cycle. Binding of viral envelope glycoprotein-gp120 with human cell surface CD4 receptor triggers the early infection stage of HIV-1. This study focuses on the interaction interface between these two proteins that play a crucial role for viral infectivity. The CD4-gp120 interaction interface has been studied through a comprehensive protein-protein interaction network (PPIN) analysis and highlighted as a useful step towards identifying potential therapeutic drug targets against HIV-1 infection. We prioritized gp41, Nef and Tat proteins of HIV-1 as valuable drug targets at early stage of viral infection. Lack of crystal structure has made it difficult to understand the biological implication of these proteins during disease progression. Here, computational protein modeling techniques and molecular dynamics simulations were performed to generate three-dimensional models of these targets. Besides, molecular docking was initiated to determine the desirability of these target proteins for already available HIV-1 specific drugs which indicates the usefulness of these protein structures to identify an effective drug combination therapy against AIDS.

Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery

Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.

Computationally Guided Identification of Novel Mycobacterium tuberculosis GlmU Inhibitory Leads, Their Optimization, and in Vitro Validation

Mycobacterium tuberculosis (Mtb) infections are causing serious health concerns worldwide. Antituberculosis drug resistance threatens the current therapies and causes further need to develop effective antituberculosis therapy. GlmU represents an interesting target for developing novel Mtb drug candidates. It is a bifunctional acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis of UDP-N-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate (GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide and peptidoglycan that are constituents of the bacterial cell wall. In the current study, structure and ligand based computational models were developed and rationally applied to screen a drug-like compound repository of 20,000 compounds procured from ChemBridge DIVERSet database for the identification of probable inhibitors of Mtb GlmU. The in vitro evaluation of the in silico identified inhibitor candidates resulted in the identification of 15 inhibitory leads of this target. Literature search of these leads through SciFinder and their similarity analysis with the PubChem training data set (AID 1376) revealed the structural novelty of these hits with respect to Mtb GlmU. IC50 of the most potent identified inhibitory lead (5810599) was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD) simulation of this inhibitory lead (5810599) in complex with protein affirms the stability of the lead within the binding pocket and also emphasizes on the key interactive residues for further designing. Binding site analysis of the acetyltransferase pocket with respect to the identified structural moieties provides a thorough analysis for carrying out the lead optimization studies.

Anti-HIV Drug Discovery and Development: Current Innovations and Future Trends

The early effectiveness of combinatorial antiretroviral therapy (cART) in the treatment of HIV infection has been compromised to some extent by rapid development of multidrug-resistant HIV strains, poor bioavailability, and cumulative toxicities, and so there is a need for alternative strategies of antiretroviral drug discovery and additional therapeutic agents with novel action modes or targets. From this perspective, we first review current strategies of antiretroviral drug discovery and optimization, with the aid of selected examples from the recent literature. We highlight the development of phosphate ester-based prodrugs as a means to improve the aqueous solubility of HIV inhibitors, and the introduction of the substrate envelope hypothesis as a new approach for overcoming HIV drug resistance. Finally, we discuss future directions for research, including opportunities for exploitation of novel antiretroviral targets, and the strategy of activation of latent HIV reservoirs as a means to eradicate the virus.

State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors

Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach

Mycobacterium tuberculosis bacteria cause deadly infections in patients [Corrected]. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with IC₅₀ values ranging from 35.32 to 64.15 μM on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Structural Disorder within Paramyxoviral Nucleoproteins and Phosphoproteins in Their Free and Bound Forms: From Predictions to Experimental Assessment

We herein review available computational and experimental data pointing to the abundance of structural disorder within the nucleoprotein (N) and phosphoprotein (P) from three paramyxoviruses, namely the measles (MeV), Nipah (NiV) and Hendra (HeV) viruses. We provide a detailed molecular description of the mechanisms governing the disorder-to-order transition that the intrinsically disordered C-terminal domain (NTAIL) of their N proteins undergoes upon binding to the C-terminal X domain (PXD) of the homologous P proteins. We also show that NTAIL-PXD complexes are "fuzzy", i.e., they possess a significant residual disorder, and discuss the possible functional significance of this fuzziness. Finally, we emphasize the relevance of N-P interactions involving intrinsically disordered proteins as promising targets for new antiviral approaches, and end up summarizing the general functional advantages of disorder for viruses.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

HIV-1 Nef: a master manipulator of the membrane trafficking machinery mediating immune evasion

BACKGROUND

Many viral genomes encode a limited number of proteins, illustrating their innate efficiency in bypassing host immune surveillance. This concept of genomic efficiency is exemplified by the 9 kb RNA genome of human immunodeficiency virus 1 (HIV-1), encoding 15 proteins sub-divided according to function. The enzymatic group includes proteins such as the drug targets reverse transcriptase and protease. In contrast, the accessory proteins lack any known enzymatic or structural function, yet are essential for viral fitness and HIV-1 pathogenesis. Of these, the HIV-1 accessory protein Nef is a master manipulator of host cellular processes, ensuring efficient counterattack against the host immune response, as well as long-term evasion of immune surveillance. In particular, the ability of Nef to downmodulate major histocompatibility complex class I (MHC-I) is a key cellular event that enables HIV-1 to bypass the host's defenses by evading the adaptive immune response.

SCOPE OF REVIEW

In this article, we briefly review how various pathogenic viruses control cell-surface MHC-I, and then focus on the mechanisms and implications of HIV-1 Nef-mediated MHC-I downregulation via modulation of the host membrane trafficking machinery.

CONCLUSION

The extensive interaction network formed between Nef and numerous membrane trafficking regulators suggests that Nef's role in evading the immune surveillance system intersects multiple host membrane trafficking pathways.

SIGNIFICANCE

Nef's ability to evade the immune surveillance system is linked to AIDS pathogenesis. Thus, a complete understanding of the molecular pathways that are subverted by Nef in order to downregulate MHC-I will enhance our understanding of HIV-1's progression to AIDS.

Computational approaches to developing short cyclic peptide modulators of protein-protein interactions

Cyclic peptides are a promising class of bioactive molecules potentially capable of modulating "difficult" targets, such as protein-protein interactions. Cyclic peptides have long been used as therapeutics derived from natural product derivatives, but remain an underexplored class of compounds from the perspective of rational drug design, possibly due to the known weaknesses of peptide drugs in general. While cyclic peptides are non"druglike" by the accepted empirical rules, their unique structure may lend itself to both membrane permeability and proteolytic resistance-the main barriers to oral delivery. The constrained shape of cyclic peptides also lends itself better to virtual screening approaches, and new tools and successes in this area have been recently noted. An increasing number of strategies are available, both to generate and screen cyclic peptide libraries, and best practises and current successes are described within. This chapter will describe various computational strategies for virtual screening cyclic peptides, along with known implementations and applications. We will explore the generation and screening of diverse combinatorial virtual libraries, incorporating a range of cyclization strategies and structural modifications. More advanced approaches covered include evolutionary algorithms designed to aid in screening large structural libraries, machine learning approaches, and harnessing bioinformatics resources to bias cyclic peptide virtual libraries towards known bioactive structures.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Small molecule inhibitors of the HIV-1 virulence factor, Nef

Although antiretroviral therapy has revolutionized the clinical management of AIDS, life-long treatment is required because these drugs do not eradicate HIV- infected cells. Chronic antiretroviral therapy may not protect AIDS patients from cognitive impairment, raising important quality of life issues. Because of the rise of HIV strains resistant to current drugs and uncertain vaccine prospects, an urgent need exists for the discovery and development of new therapeutic approaches. This review is focused on one such approach, which involves targeting HIV-1 Nef, a viral accessory protein essential for AIDS pathogenesis.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/

Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments, as the cytoplasm or the cellular organelles. We show that combining coarse-grain molecular cross-docking simulations and binding site predictions based on evolutionary sequence analysis is a viable route to identify true interacting partners for hundreds of proteins with a variate set of protein structures and interfaces. Also, we realize a large-scale analysis of protein binding promiscuity and provide a numerical characterization of partner competition and level of interaction strength for about 28000 false-partner interactions. Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community.

Development and validation of a high-content bimolecular fluorescence complementation assay for small-molecule inhibitors of HIV-1 Nef dimerization

Nef is a human immunodeficiency virus 1 (HIV-1) accessory factor essential for viral pathogenesis and AIDS progression. Many Nef functions require dimerization, and small molecules that block Nef dimerization may represent antiretroviral drug leads. Here we describe a cell-based assay for Nef dimerization inhibitors based on bimolecular fluorescence complementation (BiFC). Nef was fused to nonfluorescent, complementary fragments of yellow fluorescent protein (YFP) and coexpressed in the same cell population. Dimerization of Nef resulted in juxtaposition of the YFP fragments and reconstitution of the fluorophore. For automation, the Nef-YFP fusion proteins plus a monomeric red fluorescent protein (mRFP) reporter were expressed from a single vector, separated by picornavirus "2A" linker peptides to permit equivalent translation of all three proteins. Validation studies revealed a critical role for gating on the mRFP-positive subpopulation of transfected cells, as well as use of the mRFP signal to normalize the Nef-BiFC signal. Nef-BiFC/mRFP ratios resulting from cells expressing wild-type versus dimerization-defective Nef were very clearly separated, with Z factors consistently in the 0.6 to 0.7 range. A fully automated pilot screen of the National Cancer Institute Diversity Set III identified several hit compounds that reproducibly blocked Nef dimerization in the low micromolar range. This BiFC-based assay has the potential to identify cell-active small molecules that directly interfere with Nef dimerization and function.

Discovery of a diaminoquinoxaline benzenesulfonamide antagonist of HIV-1 Nef function using a yeast-based phenotypic screen

BACKGROUND

HIV-1 Nef is a viral accessory protein critical for AIDS progression. Nef lacks intrinsic catalytic activity and binds multiple host cell signaling proteins, including Hck and other Src-family tyrosine kinases. Nef binding induces constitutive Hck activation that may contribute to HIV pathogenesis by promoting viral infectivity, replication and downregulation of cell-surface MHC-I molecules. In this study, we developed a yeast-based phenotypic screen to identify small molecules that inhibit the Nef-Hck complex.

RESULTS

Nef-Hck interaction was faithfully reconstituted in yeast cells, resulting in kinase activation and growth arrest. Yeast cells expressing the Nef-Hck complex were used to screen a library of small heterocyclic compounds for their ability to rescue growth inhibition. The screen identified a dihydrobenzo-1,4-dioxin-substituted analog of 2-quinoxalinyl-3-aminobenzene-sulfonamide (DQBS) as a potent inhibitor of Nef-dependent HIV-1 replication and MHC-I downregulation in T-cells. Docking studies predicted direct binding of DQBS to Nef which was confirmed in differential scanning fluorimetry assays with recombinant purified Nef protein. DQBS also potently inhibited the replication of HIV-1 NL4-3 chimeras expressing Nef alleles representative of all M-group HIV-1 clades.

CONCLUSIONS

Our findings demonstrate the utility of a yeast-based growth reversion assay for the identification of small molecule Nef antagonists. Inhibitors of Nef function discovered with this assay, such as DQBS, may complement the activity of current antiretroviral therapies by enabling immune recognition of HIV-infected cells through the rescue of cell surface MHC-I.

Design of inhibitors using a combinatorial library for HIV-Nef and human SH3 domain interaction

The HIV-1 Nef protein has the ability to down regulate important molecules at the immune synapse. These include class I and class II (Human Leukocyte Antigen) HLA on the Antigen Presenting Cells (APC). The receptors in these molecules consist of SH-3 domain and their interaction with the HIV-1 Nef is critical. Therefore, it is important to inhibit this HIV-Nef and human SH3 domain interaction. Thus, we used a combinatorial library to screen for molecules to inhibit this interaction. The exercise identified a group of top ranking compounds for further consideration.

Contributions of HIV-1 Nef to immune dysregulation in HIV-infected patients: a therapeutic target?

INTRODUCTION

HIV accessory protein Nef is a factor responsible for many of the viral pathogenic effects. Progression to AIDS is dramatically delayed and in some well-documented cases completely abolished on infection with naturally occurring HIV strains lacking intact nef sequences in their genomes. The topic of this review is the contribution of Nef to the immune pathology as a possible target in HIV-infected patients.

AREAS COVERED

An overview of known Nef functions accounting for its role in pathogenesis is presented, emphasizing interactions with dendritic cells and macrophages, and Nef-induced exosome secretion, all involved in immune dysregulation during the course of HIV infection. Current approaches to Nef inhibition by different classes of compounds are reviewed.

EXPERT OPINION

Blocking Nef for therapeutic purposes is a challenging endeavor mainly due to intrinsic properties of this HIV accessory protein. Nef has multiple interfaces to interact with host proteins and lacks a catalytic domain. Potential benefits arising from the development of successful inhibitors could however prove beneficial for reducing gradual deterioration of immune system in chronically infected patients in absence of functional cure.

A Biochemical/Biophysical Assay Dyad for HTS-Compatible Triaging of Inhibitors of the HIV-1 Nef/Hck SH3 Interaction

The current treatment regimens for HIV include over 20 anti-retrovirals. However, adverse drug effects and the emergence of drug resistance necessitates the continued improvement of the existing drug classes as well as the development of novel drugs that target as yet therapeutically unexploited viral and cellular pathways. Here we demonstrate a strategy for the discovery of protein-protein interaction inhibitors of the viral pathogenicity factor HIV-1 Nef and its interaction with the host factor SH3. A combination of a time-resolved fluorescence resonance energy resonance energy transfer-based assay and a label-free resonant waveguide grating-based assay was optimized for high-throughput screening formats.

Effector kinase coupling enables high-throughput screens for direct HIV-1 Nef antagonists with antiretroviral activity

HIV-1 Nef, a critical AIDS progression factor, represents an important target protein for antiretroviral drug discovery. Because Nef lacks intrinsic enzymatic activity, we developed an assay that couples Nef to the activation of Hck, a Src family member and Nef effector protein. Using this assay, we screened a large, diverse chemical library and identified small molecules that block Nef-dependent Hck activity with low micromolar potency. Of these, a diphenylpyrazolo compound demonstrated submicromolar potency in HIV-1 replication assays against a broad range of primary Nef variants. This compound binds directly to Nef via a pocket formed by the Nef dimerization interface and disrupts Nef dimerization in cells. Coupling of nonenzymatic viral accessory factors to host cell effector proteins amenable to high-throughput screening may represent a general strategy for the discovery of new antimicrobial agents.

Marked variability in the extent of protein disorder within and between viral families

Intrinsically disordered regions in eukaryotic proteomes contain key signaling and regulatory modules and mediate interactions with many proteins. Many viral proteomes encode disordered proteins and modulate host factors through the use of short linear motifs (SLiMs) embedded within disordered regions. However, the degree of viral protein disorder across different viruses is not well understood, so we set out to establish the constraints acting on viruses, in terms of their use of disordered protein regions. We surveyed predicted disorder across 2,278 available viral genomes in 41 families, and correlated the extent of disorder with genome size and other factors. Protein disorder varies strikingly between viral families (from 2.9% to 23.1% of residues), and also within families. However, this substantial variation did not follow the established trend among their hosts, with increasing disorder seen across eubacterial, archaebacterial, protists, and multicellular eukaryotes. For example, among large mammalian viruses, poxviruses and herpesviruses showed markedly differing disorder (5.6% and 17.9%, respectively). Viral families with smaller genome sizes have more disorder within each of five main viral types (ssDNA, dsDNA, ssRNA+, dsRNA, retroviruses), except for negative single-stranded RNA viruses, where disorder increased with genome size. However, surveying over all viruses, which compares tiny and enormous viruses over a much bigger range of genome sizes, there is no strong association of genome size with protein disorder. We conclude that there is extensive variation in the disorder content of viral proteomes. While a proportion of this may relate to base composition, to extent of gene overlap, and to genome size within viral types, there remain important additional family and virus-specific effects. Differing disorder strategies are likely to impact on how different viruses modulate host factors, and on how rapidly viruses can evolve novel instances of SLiMs subverting host functions, such as innate and acquired immunity.

Identification of a novel binding site between HIV type 1 Nef C-terminal flexible loop and AP2 required for Nef-mediated CD4 downregulation

HIV-1 Nef is an accessory protein necessary for HIV-1 virulence and rapid AIDS development. Nef promotes viral replication and infection by connecting CD4 and several other cell surface receptors to the clathrin adaptor protein AP2, resulting in the internalization and degradation of the receptors interacting with Nef. We investigated how Nef can mediate constitutive receptor endocytosis through the interaction of the dileucine motif in its C-terminal flexible loop (C-loop) with AP2, whereas AP2 binding of the transmembrane receptors usually results in an equilibrated (recycled) endocytosis. Our results indicated that in addition to the dileucine motif, there is a second motif in the Nef C-loop involved in the Nef-AP2 interaction. Nef-mediated CD4 downregulation was impaired when the residue in the hydrophobic region in the Nef C-loop (LL165HPMSLHGM173) was mutated to a basic residue K/R or an acidic residue E/D or to the rigid residue P, or when M168L170, L170H171, or G172M173 was mutated to AA. A pull-down assay indicated that AP2 was not coprecipitated with Nef mutants that did not downregulate CD4. Molecular modeling of the Nef C-terminal flexible loop in complex with AP2 suggests that M168L170 occupies a pocket in the AP2 σ2 subunit. Our data suggest a new model in the Nef-AP2 interaction in which the hydrophobic region in the Nef C-loop with the dileucine (L164L165) motif and M168L170 motif binds to AP2(σ2), while the acidic motif E174 and D175 binds to AP2(α), which explains how Nef through the flexible loop connects CD4 to AP2 for constitutive CD4 downregulation.

High-throughput and in silico screenings in drug discovery

BACKGROUND

In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones.

OBJECTIVE

Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process.

CONCLUSION

Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.

The holistic integration of virtual screening in drug discovery

During the past decade, virtual screening (VS) has come of age. In this review, we document the evolution and maturation of VS from a rather exotic, stand-alone method toward a versatile hit and lead identification technology. VS campaigns have become fully integrated into drug discovery campaigns, evenly matched and complementary to high-throughput screening (HTS) methods. Here, we propose a novel classification of VS applications to help to monitor the advances in VS and to support future improvement of computational hit and lead identification methods. Several relevant VS studies from recent publications, in both academic and industrial settings, were selected to demonstrate the progress in this area. Furthermore, we identify challenges that lie ahead for the development of integrated VS campaigns.

A phenotypic assay to identify Chikungunya virus inhibitors targeting the nonstructural protein nsP2

Chikungunya virus (CHIKV) is a mosquito-transmitted pathogen responsible for an acute infection of abrupt onset, characterized by high fever, polyarthralgia, myalgia, headaches, chills, and rash. In 2006, CHIKV was responsible for an epidemic outbreak of unprecedented magnitude in the Indian Ocean, stressing the need for therapeutic approaches. Since then, we have acquired a better understanding of CHIKV biology, but we are still missing active molecules against this reemerging pathogen. We recently reported that the nonstructural nsP2 protein of CHIKV induces a transcriptional shutoff that allows the virus to block cellular antiviral response. This was demonstrated using various luciferase-based reporter gene assays, including a trans-reporter system where Gal4 DNA binding domain is fused to Fos transcription factor. Here, we turned this assay into a high-throughput screening system to identify small molecules targeting nsP2-mediated shutoff. Among 3040 molecules tested, we identified one natural compound that partially blocks nsP2 activity and inhibits CHIKV replication in vitro. This proof of concept suggests that similar functional assays could be developed to target other viral proteins mediating a cellular shutoff and identify innovative therapeutic molecules.