Development of Protein-Protein Interaction Inhibitors for the Treatment of Infectious Diseases

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Molecular Determinants of Human Rhinovirus Infection, Assembly, and Conformational Stability at Capsid Protein Interfaces

Human rhinovirus (HRV), one of the most frequent human pathogens, is the major causative agent of common colds. HRVs also cause or exacerbate severe respiratory diseases, such as asthma or chronic obstructive pulmonary disease. Despite the biomedical and socioeconomic importance of this virus, no anti-HRV vaccines or drugs are available yet. Protein-protein interfaces in virus capsids have increasingly been recognized as promising virus-specific targets for the development of antiviral drugs. However, the specific structural elements and residues responsible for the biological functions of these extended capsid regions are largely unknown. In this study, we performed a thorough mutational analysis to determine which particular residues along the capsid interpentamer interfaces are relevant to HRV infection as well as the stage(s) in the viral cycle in which they are involved. The effect on the virion infectivity of the individual mutation to alanine of 32 interfacial residues that, together, removed most of the interpentamer interactions was analyzed. Then, a representative sample that included many of those 32 single mutants were tested for capsid and virion assembly as well as virion conformational stability. The results indicate that most of the interfacial residues, and the interactions they establish, are biologically relevant, largely because of their important roles in virion assembly and/or stability. The HRV interpentamer interface is revealed as an atypical protein-protein interface, in which infectivity-determining residues are distributed at a high density along the entire interface. Implications for a better understanding of the relationship between the molecular structure and function of HRV and the development of novel capsid interface-binding anti-HRV agents are discussed. IMPORTANCE The rising concern about the serious medical and socioeconomic consequences of respiratory infections by HRV has elicited a renewed interest in the development of anti-HRV drugs. The conversion into effective drugs of compounds identified via screening, as well as antiviral drug design, rely on the acquisition of fundamental knowledge about the targeted viral elements and their roles during specific steps of the infectious cycle. The results of this study provide a detailed view on structure-function relationships in a viral capsid protein-protein interface, a promising specific target for antiviral intervention. The high density and scattering of the interfacial residues found to be involved in HRV assembly and/or stability support the possibility that any compound designed to bind any particular site at the interface will inhibit infection by interfering with virion morphogenesis or stabilization of the functional virion conformation.

Design and Synthesis of Hepatitis B Virus (HBV) Capsid Assembly Modulators and Evaluation of Their Activity in Mammalian Cell Model

Capsid assembly modulators (CAMs) have emerged as a promising class of antiviral agents. We studied the effects of twenty-one newly designed and synthesized CAMs including heteroaryldihydropyrimidine compounds (HAPs), their analogs and standard compounds on hepatitis B virus (HBV) capsid assembly. Cytoplasmic expression of the HBV core (HBc) gene driven by the exogenously delivered recombinant alphavirus RNA replicon was used for high level production of the full-length HBc protein in mammalian cells. HBV capsid assembly was assessed by native agarose gel immunoblot analysis, electron microscopy and inhibition of virion secretion in HepG2.2.15 HBV producing cell line. Induced fit docking simulation was applied for modelling the structural relationships of the synthesized compounds and HBc. The most efficient were the HAP class compounds—dihydropyrimidine 5-carboxylic acid n-alkoxyalkyl esters, which induced the formation of incorrectly assembled capsid products and their accumulation within the cells. HBc product accumulation in the cells was not detected with the reference HAP compound Bay 41-4109, suggesting different modes of action. A significant antiviral effect and substantially reduced toxicity were revealed for two of the synthesized compounds. Two new HAP compounds revealed a significant antiviral effect and a favorable toxicity profile that allows these compounds to be considered promising leads and drug candidates for the treatment of HBV infection. The established alphavirus based HBc expression approach allows for the specific selection of capsid assembly modulators directly in the natural cell environment.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Inhibiting Type VI Secretion System Activity with a Biomimetic Peptide Designed To Target the Baseplate Wedge Complex

Human health is threatened by bacterial infections that are increasingly resistant to multiple drugs. A recently emerged strategy consists of disarming pathogenic bacteria by targeting and blocking their virulence factors. The type VI secretion system (T6SS) is a widespread secretion nanomachine encoded and employed by pathogenic strains to establish their virulence process during host invasion. Given the conservation of T6SS in several human bacterial pathogens, the discovery of an effective broad-spectrum T6SS virulence blocker represents an attractive target for development of antivirulence therapies. Here, we identified and validated a protein-protein interaction interface, TssK-TssG, as a key factor in the assembly of the T6SS baseplate (BP) complex in the pathogen enteroaggregative Escherichia coli (EAEC). In silico and biochemical studies revealed that the determinants of the interface are broadly conserved among pathogenic species, suggesting a role for this interface as a target for T6SS inhibition. Based on the high-resolution structure of the TssKFGE wedge complex, we rationally designed a biomimetic cyclic peptide (BCP) that blocks the assembly of the EAEC BP complex and inhibits the function of T6SS in bacterial cultures. Our BCP is the first compound completely designed from prior structural knowledge with anti-T6SS activity that can be used as a model to target human pathogens.

Structural Profiling of Bacterial Effectors Reveals Enrichment of Host-Interacting Domains and Motifs

Effector proteins are bacterial virulence factors secreted directly into host cells and, through extensive interactions with host proteins, rewire host signaling pathways to the advantage of the pathogen. Despite the crucial role of globular domains as mediators of protein-protein interactions (PPIs), previous structural studies of bacterial effectors are primarily focused on individual domains, rather than domain-mediated PPIs, which limits their ability to uncover systems-level molecular recognition principles governing host-bacteria interactions. Here, we took an interaction-centric approach and systematically examined the potential of structural components within bacterial proteins to engage in or target eukaryote-specific domain-domain interactions (DDIs). Our results indicate that: 1) effectors are about six times as likely as non-effectors to contain host-like domains that mediate DDIs exclusively in eukaryotes; 2) the average domain in effectors is about seven times as likely as that in non-effectors to co-occur with DDI partners in eukaryotes rather than in bacteria; and 3) effectors are about nine times as likely as non-effectors to contain bacteria-exclusive domains that target host domains mediating DDIs exclusively in eukaryotes. Moreover, in the absence of host-like domains or among pathogen proteins without domain assignment, effectors harbor a higher variety and density of short linear motifs targeting host domains that mediate DDIs exclusively in eukaryotes. Our study lends novel quantitative insight into the structural basis of effector-induced perturbation of host-endogenous PPIs and may aid in the design of selective inhibitors of host-pathogen interactions.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2

Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

Scanning Protein Surfaces with DNA-Encoded Libraries

Understanding the ligandability of a target protein, defined as the capability of a protein to bind drug-like compounds on any site, can give important stimuli to drug-development projects. For instance, inhibition of protein-protein interactions usually depends on the identification of protein surface binders. DNA-encoded chemical libraries (DELs) allow scanning of protein surfaces with large chemical space. Encoded library selection screens uncovered several protein-protein interaction inhibitors and compounds binding to the surface of G protein-coupled receptors (GPCRs) and kinases. The protein surface-binding chemotypes from DELs are predominantly chemically modified and cyclized peptides, and functional small-molecule peptidomimetics. Peptoid libraries and structural peptidomimetics have been less studied in the DEL field, hinting at hitherto less populated chemical space and suggesting alternative library designs. Roughly a third of bioactive molecules evolved from smaller, target-focused libraries. They showcase the potential of encoded libraries to identify more potent molecules from weak, for example, fragment-like, starting points.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Pyridobenzothiazolones Exert Potent Anti-Dengue Activity by Hampering Multiple Functions of NS5 Polymerase

Treatment of dengue virus (DENV) and other flavivirus infections is an unmet medical need. The highly conserved flaviviral NS5 RNA-dependent RNA polymerase (RdRp) is an attractive antiviral target that interacts with NS3 and viral RNA within the replication complex assembly. Biochemical and cell-based evidence indicate that targeting cavity B may lead to dual RdRp and NS5-NS3 interaction inhibitors. By ligand-based design around 1H-pyrido[2,1-b][1,3]benzothiazol-1-one (PBTZ) 1, we identified new potent and selective DENV inhibitors that exert dual inhibition of NS5 RdRp and NS3-NS5 interaction, likely through binding cavity B. Resistance studies with compound 4 generated sequence variants in the 3'-untranslated region of RNA while further biochemical experiments demonstrated its ability to block also RNA-NS5 interaction, required for correct RNA synthesis in cells. These findings shed light on the potential mechanism of action for this class of compounds, underlying how PBTZs are very promising lead candidates for further evaluation.

Combination of SAXS and Protein Painting Discloses the Three-Dimensional Organization of the Bacterial Cysteine Synthase Complex, a Potential Target for Enhancers of Antibiotic Action

The formation of multienzymatic complexes allows for the fine tuning of many aspects of enzymatic functions, such as efficiency, localization, stability, and moonlighting. Here, we investigated, in solution, the structure of bacterial cysteine synthase (CS) complex. CS is formed by serine acetyltransferase (CysE) and O-acetylserine sulfhydrylase isozyme A (CysK), the enzymes that catalyze the last two steps of cysteine biosynthesis in bacteria. CysK and CysE have been proposed as potential targets for antibiotics, since cysteine and related metabolites are intimately linked to protection of bacterial cells against redox damage and to antibiotic resistance. We applied a combined approach of small-angle X-ray scattering (SAXS) spectroscopy and protein painting to obtain a model for the solution structure of CS. Protein painting allowed the identification of protein–protein interaction hotspots that were then used as constrains to model the CS quaternary assembly inside the SAXS envelope. We demonstrate that the active site entrance of CysK is involved in complex formation, as suggested by site-directed mutagenesis and functional studies. Furthermore, complex formation involves a conformational change in one CysK subunit that is likely transmitted through the dimer interface to the other subunit, with a regulatory effect. Finally, SAXS data indicate that only one active site of CysK is involved in direct interaction with CysE and unambiguously unveil the quaternary arrangement of CS.

Dengue drug discovery: Progress, challenges and outlook

In the context of the only available vaccine (DENGVAXIA) that was marketed in several countries, but poses higher risks to unexposed individuals, the development of antivirals for dengue virus (DENV), whilst challenging, would bring significant benefits to public health. Here recent progress in the field of DENV drug discovery made in academic laboratories and industry is reviewed. Characteristics of an ideal DENV antiviral molecule, given the specific immunopathology provoked by this acute viral infection, are described. New chemical classes identified from biochemical, biophysical and phenotypic screens that target viral (especially NS4B) and host proteins, offer promising opportunities for further development. In particular, new methodologies ("omics") can accelerate the discovery of much awaited flavivirus specific inhibitors. Challenges and opportunities in lead identification activities as well as the path to clinical development of dengue drugs are discussed. To galvanize DENV drug discovery, collaborative public-public partnerships and open-access resources will greatly benefit both the DENV research community and DENV patients.