PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties

Repurposing of FDA-approved drugs against oligomerization domain of dengue virus NS1 protein: a computational approach

Dengue fever is a serious health hazard on a global scale and its primary causative agent is the dengue virus (DENV). The non-structural protein 1 (NS1) of DENV plays a pivotal role in pathogenesis. It is associated with several autoimmune events, endothelial cell apoptosis, and vascular leakage, which increase mainly during the critical phase of infection. In this study, important residues of the oligomerization domain of NS1 protein were identified by literature searches. Virtual screening has been conducted using the entire dataset of the DrugBank database and the potential small-molecule inhibitors against the NS1 protein have been chosen on the basis of binding energy values. This is succeeded by molecular dynamics (MD) simulations of the shortlisted compounds, ultimately giving rise to five compounds. These five compounds were further subjected to RAMD simulations by applying a random direction force of specific magnitude on the ligand center of mass in order to push the ligand out of the protein-binding pocket, for the quantitative estimation of their binding energy values to determine the interaction strength between protein and ligand which prevents ligand unbinding from its binding site, ultimately leading to the selection of three major compounds, DB00826 (Natamycin), DB11274 (Dihydro-alphaergocryptine), and DB11275 (Epicriptine), with the DB11274 having a role against idiopathic Parkinson's disease, and thus may have possible important roles in the prevention of dengue-associated Parkinsonism. These compounds may act as prospective drugs against dengue, by preventing the oligomerization of the NS1 protein, thereby preventing disease progression and pathogenesis.

Protein interaction explorer (PIE): a comprehensive platform for navigating protein-protein interactions and ligand binding pockets

SUMMARY

Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein-protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts.

AVAILABILITY AND IMPLEMENTATION

PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer.

Functional analysis of the Aspergillus fumigatus kinome identifies a druggable DYRK kinase that regulates septal plugging

More than 10 million people suffer from lung diseases caused by the pathogenic fungus Aspergillus fumigatus. Azole antifungals represent first-line therapeutics for most of these infections but resistance is rising, therefore the identification of antifungal targets whose inhibition synergises with the azoles could improve therapeutic outcomes. Here, we generate a library of 111 genetically barcoded null mutants of Aspergillus fumigatus in genes encoding protein kinases, and show that loss of function of kinase YakA results in hypersensitivity to the azoles and reduced pathogenicity. YakA is an orthologue of Candida albicans Yak1, a TOR signalling pathway kinase involved in modulation of stress responsive transcriptional regulators. We show that YakA has been repurposed in A. fumigatus to regulate blocking of the septal pore upon exposure to stress. Loss of YakA function reduces the ability of A. fumigatus to penetrate solid media and to grow in mouse lung tissue. We also show that 1-ethoxycarbonyl-beta-carboline (1-ECBC), a compound previously shown to inhibit C. albicans Yak1, prevents stress-mediated septal spore blocking and synergises with the azoles to inhibit A. fumigatus growth.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Identification of potential inhibitors of casein kinase 2 alpha of Plasmodium falciparum with potent in vitro activity

Drug resistance to commercially available antimalarials is a major obstacle in malaria control and elimination, creating the need to find new antiparasitic compounds with novel mechanisms of action. The success of kinase inhibitors for oncological treatments has paved the way for the exploitation of protein kinases as drug targets in various diseases, including malaria. Casein kinases are ubiquitous serine/threonine kinases involved in a wide range of cellular processes such as mitotic checkpoint signaling, DNA damage response, and circadian rhythm. In Plasmodium, it is suggested that these protein kinases are essential for both asexual and sexual blood-stage parasites, reinforcing their potential as targets for multi-stage antimalarials. To identify new putative PfCK2α inhibitors, we utilized an in silico chemogenomic strategy involving virtual screening with docking simulations and quantitative structure-activity relationship predictions. Our investigation resulted in the discovery of a new quinazoline molecule (542), which exhibited potent activity against asexual blood stages and a high selectivity index (>100). Subsequently, we conducted chemical-genetic interaction analysis on yeasts with mutations in casein kinases. Our chemical-genetic interaction results are consistent with the hypothesis that 542 inhibits yeast Cka1, which has a hinge region with high similarity to PfCK2α. This finding is in agreement with our in silico results suggesting that 542 inhibits PfCK2α via hinge region interaction.

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants.

Proteome level analysis of drug-resistant Prevotella melaninogenica for the identification of novel therapeutic candidates

The management of infectious diseases has become more critical due to the development of novel pathogenic strains with enhanced resistance. Prevotella melaninogenica, a gram-negative bacterium, was found to be involved in various infections of the respiratory tract, aerodigestive tract, and gastrointestinal tract. The need to explore novel drug and vaccine targets against this pathogen was triggered by the emergence of antimicrobial resistance against reported antibiotics to combat P. melaninogenica infections. The study involves core genes acquired from 14 complete P. melaninogenica strain genome sequences, where promiscuous drug and vaccine candidates were explored by state-of-the-art subtractive proteomics and reverse vaccinology approaches. A stringent bioinformatics analysis enlisted 18 targets as novel, essential, and non-homologous to humans and having druggability potential. Moreover, the extracellular and outer membrane proteins were subjected to antigenicity, allergenicity, and physicochemical analysis for the identification of the candidate proteins to design multi-epitope vaccines. Two candidate proteins (ADK95685.1 and ADK97014.1) were selected as the best target for the designing of a vaccine construct. Lead B- and T-cell overlapped epitopes were joined to generate potential chimeric vaccine constructs in combination with adjuvants and linkers. Finally, a prioritized vaccine construct was found to have stable interactions with the human immune cell receptors as confirmed by molecular docking and MD simulation studies. The vaccine construct was found to have cloning and expression ability in the bacterial cloning system. Immune simulation ensured the elicitation of significant immune responses against the designed vaccine. In conclusion, our study reported novel drug and vaccine targets and designed a multi-epitope vaccine against the P. melaninogenica infection. Further experimental validation will help open new avenues in the treatment of this multi-drug-resistant pathogen.

Identification of novel Zika virus NS3 protease inhibitors with different inhibition modes by integrative experimental and computational approaches

Zika virus (ZIKV) infection is associated with severe neurological disorders and congenital malformation. Despite efforts to eradicate the disease, there is still neither vaccine nor approved drugs to treat ZIKV infection. The NS2B-NS3 protease is a validated drug target since it is essential to polyprotein virus maturation. In the present study, we describe an experimental screening of 2,320 compounds from the chemical library of the Muséum National d'Histoire Naturelle of Paris on ZIKV NS2B-NS3 protease. A total of 96 hits were identified with 90% or more of inhibitory activity at 10 μM. Amongst the most active compounds, five were analyzed for their inhibitory mechanisms by kinetics assays and computational approaches such as molecular docking. 2-(3-methoxyphenoxy) benzoic acid (compound 945) show characteristics of a competitive inhibition (Ki = 0.49 μM) that was corroborated by its molecular docking at the active site of the NS2B-NS3 protease. Taxifolin (compound 2292) behaves as an allosteric inhibitor whereas 3,8,9-trihydroxy-2-methyl-1H-phenalen-1-one (compound 128), harmol (compound 368) and anthrapurpurin (compound 1499) show uncompetitive inhibitions. These new NS2B-NS3 protease inhibitors are valuable hits to further hit-to-lead optimization.

Peptide ligands for the affinity purification of adeno-associated viruses from HEK 293 cell lysates

Adeno-associated viruses (AAVs) are the vector of choice for delivering gene therapies that can cure inherited and acquired diseases. Clinical research on various AAV serotypes significantly increased in recent years alongside regulatory approvals of AAV-based therapies. The current AAV purification platform hinges on the capture step, for which several affinity resins are commercially available. These adsorbents rely on protein ligands-typically camelid antibodies-that provide high binding capacity and selectivity, but suffer from low biochemical stability and high cost, and impose harsh elution conditions (pH < 3) that can harm the transduction activity of recovered AAVs. Addressing these challenges, this study introduces peptide ligands that selectively capture AAVs and release them under mild conditions (pH = 6.0). The peptide sequences were identified by screening a focused library and modeled in silico against AAV serotypes 2 and 9 (AAV2 and AAV9) to select candidate ligands that target homologous sites at the interface of the VP1-VP2 and VP2-VP3 virion proteins with mild binding strength (KD  ~ 10-5 -10- 6  M). Selected peptides were conjugated to Toyopearl resin and evaluated via binding studies against AAV2 and AAV9, demonstrating the ability to target both serotypes with values of dynamic binding capacity (DBC10%  > 1013 vp/mL of resin) and product yields (~50%-80%) on par with commercial adsorbents. The peptide-based adsorbents were finally utilized to purify AAV2 from a HEK 293 cell lysate, affording high recovery (50%-80%), 80- to 400-fold reduction of host cell proteins (HCPs), and high transduction activity (up to 80%) of the purified viruses.

Functional analysis of the Aspergillus fumigatus kinome reveals a DYRK kinase involved in septal plugging is a novel antifungal drug target

More than 10 million people suffer from lung diseases caused by the pathogenic fungus Aspergillus fumigatus. The azole class of antifungals represent first line therapeutics for most of these infections however resistance is rising. Identification of novel antifungal targets that, when inhibited, synergise with the azoles will aid the development of agents that can improve therapeutic outcomes and supress the emergence of resistance. As part of the A. fumigatus genome-wide knockout program (COFUN), we have completed the generation of a library that consists of 120 genetically barcoded null mutants in genes that encode the protein kinase cohort of A. fumigatus. We have employed a competitive fitness profiling approach (Bar-Seq), to identify targets which when deleted result in hypersensitivity to the azoles and fitness defects in a murine host. The most promising candidate from our screen is a previously uncharacterised DYRK kinase orthologous to Yak1 of Candida albicans, a TOR signalling pathway kinase involved in modulation of stress responsive transcriptional regulators. Here we show that the orthologue YakA has been repurposed in A. fumigatus to regulate blocking of the septal pore upon exposure to stress via phosphorylation of the Woronin body tethering protein Lah. Loss of YakA function reduces the ability of A. fumigatus to penetrate solid media and impacts growth in murine lung tissue. We also show that 1-ethoxycarbonyl-beta-carboline (1-ECBC), a compound previously shown to inhibit Yak1 in C. albicans prevents stress mediated septal spore blocking and synergises with the azoles to inhibit A. fumigatus growth.

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.

Identifying promising druggable binding sites and their flexibility to target the receptor-binding domain of SARS-CoV-2 spike protein

The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for viral infection. The interaction of its receptor-binding domain (RBD) with the human angiotensin-converting enzyme 2 (ACE2) protein is required for the virus to enter the host cell. We identified RBD binding sites to block its function with inhibitors by combining the protein structural flexibility with machine learning analysis. Molecular dynamics simulations were performed on unbound or ACE2-bound RBD conformations. Pockets estimation, tracking and druggability prediction were performed on a large sample of simulated RBD conformations. Recurrent druggable binding sites and their key residues were identified by clustering pockets based on their residue similarity. This protocol successfully identified three druggable sites and their key residues, aiming to target with inhibitors for preventing ACE2 interaction. One site features key residues for direct ACE2 interaction, highlighted using energetic computations, but can be affected by several mutations of the variants of concern. Two highly druggable sites, located between the spike protein monomers interface are promising. One weakly impacted by only one Omicron mutation, could contribute to stabilizing the spike protein in its closed state. The other, currently not affected by mutations, could avoid the activation of the spike protein trimer.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

Comparative Assessment of Three Medicinal Plants against Diabetes and Oxidative Stress Using Experimental and Computational Approaches

The hilly and rural areas' people of Bangladesh have a great history of putting into use numerous traditional medicinal plants to cure diseases. Therefore, with ethanol extract of Molineria capitulata (EEMC), methanol extract of Trichosanthes tricuspidata (METT), and methanol extract of Amorphophallus campanulatus (MEAC), we mandate evaluation of in vitro α-amylase inhibition, antioxidants, and molecular docking, and ADMET/T analysis. According to iodine starch methods, α-amylase inhibition was performed, and quantitative total phenolic and flavonoid content was determined by established methods, whereas DPPH free radical scavenging and reducing power assays were performed in previously established protocols, respectively. A comparative study among three plants (EEMC, METT, and MEAC) possessed a significant (p < 0.01) effect but EEMC showed the highest impact on enzyme inhibition. Plants in the measuring phenolic content METT and flavonoid measurement MEAC displayed most potent in the same way in the DPPH test was METT, and in reducing power capability MEAC has showed the highest effect between three extracts. Docking's study also reveals the compounds of METT (Cyclotricuspidoside A and Cyclotricuspidoside C) exhibit the superior score among all the compounds. This finding indicates that EEMC, METT, and MEAC substantially impact α-amylase inhibition along with antioxidants. In silico study also reveals the potency of these plants, but further in-depth, precise molecular studies are needed.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Druggable Pockets at the RNA Interface Region of Influenza A Virus NS1 Protein Are Conserved across Sequence Variants from Distinct Subtypes

Influenza A viruses still represent a major health issue, for both humans and animals. One of the main viral proteins of interest to target is the NS1 protein, which counters the host immune response and promotes viral replication. NS1 is a homodimer composed of a dimeric RNA-binding domain (RBD), which is structurally stable and conserved in sequence, and two effector domains that are tethered to the RBD by linker regions. This linker flexibility leads to NS1 polymorphism and can therefore exhibit different forms. Previously, we identified a putative drug-binding site, located in the RBD interface in a crystal structure of NS1. This pocket could be targeted to block RNA binding and inhibit NS1 activities. The objective of the present study is to confirm the presence of this druggable site, whatever the sequence variants, in order to develop a universal therapeutic compound that is insensitive to sequence variations and structural flexibility. Using a set of four NS1 full-length structures, we combined different bioinformatics approaches such as pocket tracking along molecular dynamics simulations, druggability prediction and classification. This protocol successfully confirmed a frequent large binding-site that is highly druggable and shared by different NS1 forms, which is promising for developing a robust NS1-targeted therapy.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Identification of potential phytochemicals from Citrus Limon against main protease of SARS-CoV-2: molecular docking, molecular dynamic simulations and quantum computations

The outbreak of coronavirus disease (COVID-19) caused by a novel RNA virus emerged at the end of 2019. Most of the patient's symptoms are mild to moderate, and influenza, acute respiratory distress syndrome (ARDS) and multi-organ failure are common. The disease is mild to moderate in most patients and is reported in many cases such as pneumonia, ARDS and multi-organ dysfunction. This study's objective is to evaluate 25 natural compounds from Citrus limon (CL) used by comprehensive molecular docking, density functional theory (DFT) and molecular dynamics analysis against SARS-CoV-2 main protease (M^pro). Among all the experimental compounds, diosmetin has shown the best docking values against the M^pro of SARS-CoV-2 compared to the standard antiviral drug. In DFT calculations, the order associated with biochemical reactivity is as follows: eriodictoyl > quercetin > spinacetin > diosmetin > luteolin > apigenin, whereas the regions of oxygen and hydrogen atoms from the selected isolated compounds are appropriate for electrophilic and nucleophilic attacks, respectively. Also, HOMO-LUMO and global descriptors values indicated a promising result of these compounds. Moreover, a molecular dynamics simulation study revealed the stable conformation and binding pattern in a stimulating environment of natural compounds CL. Considering molecular docking, simulation, and DFT analysis of the selected compounds, notably eriodictoyl, quercetin, and diosmetin showed good potential against SARS-CoV-2 M^pro. Our in silico study revealed promising antiviral activity, which may be considered a potential key factor or a therapeutic target for COVID-19.Communicated by Ramaswamy H. Sarma.

Knowledge-driven design and optimization of potent symmetric anticancer molecules: A case study on PKM2 activators

BACKGROUND

Pyruvate kinase M2 (PKM2) is preferentially expressed as a low-activity dimer over the active tetramer in proliferating tumor cells, resulting in metabolic reprogramming to achieve high energy requirements and nutrient uptake. This leads to a shift from the normal glycolytic pathway causing tumor cells to proliferate uncontrollably. This study utilizes knowledge-based drug discovery to determine the critical features from experimentally known PKM2 activators and design compounds that would significantly confer a stable structural and functional edge over the known compounds which are still at the preclinical stage.

METHODS

Conscientious molecular modeling studies were carried out and critical structural features were identified and validated from the knowledge of experimentally known PKM2 activators to confer high-binding affinities. A virtual library of 200 palindromic and non-palindromic activators was designed based on these identified critical features to target a distinct activator binding-site. This binding would favor specific dimer-dimer association and subsequent protein tetramerization. The resultant compounds strongly correlated with identified structural features and binding affinities which further strengthened our findings. The designed activators were then subjected to pharmacokinetic profiling and toxicity prediction, followed by free-binding energy calculations and MD simulations.

RESULTS

All the virtually designed activators comprising the identified critical features were observed to confer high-binding affinities ranging from -9.1 to -15.0 kcal/mol to the receptor protein. The designed activators also demonstrated optimum pharmacokinetic and toxicity profiles.

CONCLUSION

The best activators selected for MD simulations studies were conclusively observed to stabilize the required tetrameric conformation suggesting that these activators could potentially target PKM2 tetramerization that might restore the normal glycolytic pathway and suppress tumor progression.

Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

The druggable genome: Twenty years later

The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.

Structural Bioinformatic Survey of Protein-Small Molecule Interfaces Delineates the Role of Glycine in Surface Pocket Formation

With a structural bioinformatic approach, we have explored amino acid compositions at PISA defined interfaces between small molecules and proteins that are contained in an optimized subset of 11,351 PDB files. The use of a series of restrictions, to prevent redundancy and biases from interactions between amino acids with charged side chains and ions, yielded a final data set of 45,230 protein-small molecule interfaces. We have compared occurrences of natural amino acids in surface exposed regions and binding sites for all the proteins of our data set. From our structural bioinformatic survey, the most relevant signal arose from the unexpected Gly abundance at enzyme catalytic sites. This finding suggested that Gly must have a fundamental role in stabilizing concave protein surface moieties. Subsequently, we have tried to predict the effect of in silico Gly mutations in hen egg white lysozyme to optimize those conditions that can reshape the protein surface with the appearance of new pockets. Replacing amino acids having bulky side chains with Gly in specific protein regions seems a feasible way for designing proteins with additional surface pockets, which can alter protein surface dynamics, therefore, representing controllable switches for protein activity.

Inhibition of the hexamerization of SARS-CoV-2 endoribonuclease and modeling of RNA structures bound to the hexamer

Non-structural protein 15 (Nsp15) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) forms a homo hexamer and functions as an endoribonuclease. Here, we propose that Nsp15 activity may be inhibited by preventing its hexamerization through drug binding. We first explored the stable conformation of the Nsp15 monomer as the global free energy minimum conformation in the free energy landscape using a combination of parallel cascade selection molecular dynamics (PaCS-MD) and the Markov state model (MSM), and found that the Nsp15 monomer forms a more open conformation with larger druggable pockets on the surface. Targeting the pockets with high druggability scores, we conducted ligand docking and identified compounds that tightly bind to the Nsp15 monomer. The top poses with Nsp15 were subjected to binding free energy calculations by dissociation PaCS-MD and MSM (dPaCS-MD/MSM), indicating the stability of the complexes. One of the identified pockets, which is distinctively bound by inosine analogues, may be an alternative binding site to stabilize viral RNA binding and/or an alternative catalytic site. We constructed a stable RNA structure model bound to both UTP and alternative binding sites, providing a reasonable proposed model of the Nsp15/RNA complex.

Identification of a Potential mRNA-based Vaccine Candidate against the SARS-CoV-2 Spike Glycoprotein: A Reverse Vaccinology Approach

The emergence of the novel coronavirus (SARS-CoV-2) in December 2019 has generated a devastating global consequence which makes the development of a rapidly deployable, effective and safe vaccine candidate an imminent global health priority. The design of most vaccine candidates has been directed at the induction of antibody responses against the trimeric spike glycoprotein of SARS-CoV-2, a class I fusion protein that aids ACE2 (angiotensin-converting enzyme 2) receptor binding. A variety of formulations and vaccinology approaches are being pursued for targeting the spike glycoprotein, including simian and human replication-defective adenoviral vaccines, subunit protein vaccines, nucleic acid vaccines and whole-inactivated SARS-CoV-2. Here, we directed a reverse vaccinology approach towards the design of a nucleic acid (mRNA-based) vaccine candidate. The "YLQPRTFLL" peptide sequence (position 269-277) which was predicted to be a B cell epitope and likewise a strong binder of the HLA*A-0201 was selected for the design of the vaccine candidate, having satisfied series of antigenicity assessments. Through the codon optimization protocol, the nucleotide sequence for the vaccine candidate design was generated and targeted at the human toll-like receptor 7 (TLR7). Bioinformatics analyses showed that the sequence "UACCUGCAGCCGCGUACCUUCCUGCUG" exhibited a strong affinity and likewise was bound to a stable cavity in the TLR7 pocket. This study is therefore expected to contribute to the research efforts directed at securing definitive preventive measures against the SARS-CoV-2 infection.

D3PM: a comprehensive database for protein motions ranging from residue to domain

Background

Knowledge of protein motions is significant to understand its functions. While currently available databases for protein motions are mostly focused on overall domain motions, little attention is paid on local residue motions. Albeit with relatively small scale, the local residue motions, especially those residues in binding pockets, may play crucial roles in protein functioning and ligands binding.

Results

A comprehensive protein motion database, namely D3PM, was constructed in this study to facilitate the analysis of protein motions. The protein motions in the D3PM range from overall structural changes of macromolecule to local flip motions of binding pocket residues. Currently, the D3PM has collected 7679 proteins with overall motions and 3513 proteins with pocket residue motions. The motion patterns are classified into 4 types of overall structural changes and 5 types of pocket residue motions. Impressively, we found that less than 15% of protein pairs have obvious overall conformational adaptations induced by ligand binding, while more than 50% of protein pairs have significant structural changes in ligand binding sites, indicating that ligand-induced conformational changes are drastic and mainly confined around ligand binding sites. Based on the residue preference in binding pocket, we classified amino acids into “pocketphilic” and “pocketphobic” residues, which should be helpful for pocket prediction and drug design.

Conclusion

D3PM is a comprehensive database about protein motions ranging from residue to domain, which should be useful for exploring diverse protein motions and for understanding protein function and drug design. The D3PM is available on www.d3pharma.com/D3PM/index.php.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04595-0.

Exploration of the Structural Asymmetry Induced by the Intrinsic Flexibility of HIV-2 Protease

HIV-2 protease (PR2) is a homodimer targeted by drugs in the treatment of HIV-2 infections. This dimer is often considered symmetric. However, exploration of crystallographic structures showed that the two chains of PR2 exhibit different conformations. This study presents the first analysis of the structural asymmetry of PR2 induced by its intrinsic flexibility. We followed the structural asymmetry of PR2 throughout a molecular dynamics (MD) simulation of 1 microsecond. To do so, we quantified the global and local structural asymmetries of 1001 structures extracted from the MD simulation using the root mean square deviation (RMSD) between the two chains in each structure. We then analyzed the links between global and local asymmetry and PR2 flexibility. Our results showed that the global asymmetry of PR2 evolves over time and that it is not explained by the asymmetry of only one region of PR2. We noted that the most flexible regions of PR2 are the most asymmetric regions, revealing that the structural asymmetry of a region is induced by its intrinsic flexibility. Using multivariate analysis methods, we identified six asymmetric profiles varying from structures exhibiting weak asymmetry to structures with extreme asymmetry in at least eight different regions. The analysis of transitions between the different profiles in the MD simulation showed that two consecutive structures often exhibit similar asymmetric profiles, revealing small deformations. To conclude, this study provides insights which help to better understand PR2’s structure, dynamics, and deformations.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

Facilitating Antiviral Drug Discovery Using Genetic and Evolutionary Knowledge

Over the course of human history, billions of people worldwide have been infected by various viruses. Despite rapid progress in the development of biomedical techniques, it is still a significant challenge to find promising new antiviral targets and drugs. In the past, antiviral drugs mainly targeted viral proteins when they were used as part of treatment strategies. Since the virus mutation rate is much faster than that of the host, such drugs feature drug resistance and narrow-spectrum antiviral problems. Therefore, the targeting of host molecules has gradually become an important area of research for the development of antiviral drugs. In recent years, rapid advances in high-throughput sequencing techniques have enabled numerous genetic studies (such as genome-wide association studies (GWAS), clustered regularly interspersed short palindromic repeats (CRISPR) screening, etc.) for human diseases, providing valuable genetic and evolutionary resources. Furthermore, it has been revealed that successful drug targets exhibit similar genetic and evolutionary features, which are of great value in identifying promising drug targets and discovering new drugs. Considering these developments, in this article the authors propose a host-targeted antiviral drug discovery strategy based on knowledge of genetics and evolution. We first comprehensively summarized the genetic, subcellular location, and evolutionary features of the human genes that have been successfully used as antiviral targets. Next, the summarized features were used to screen novel druggable antiviral targets and to find potential antiviral drugs, in an attempt to promote the discovery of new antiviral drugs.

Elucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease

The SARS-CoV-2 pandemic has rapidly spread across the globe, posing an urgent health concern. Many quests to computationally identify treatments against the virus rely on in silico small molecule docking to experimentally determined structures of viral proteins. One limit to these approaches is that protein dynamics are often unaccounted for, leading to overlooking transient, druggable conformational states. Using Gaussian accelerated molecular dynamics to enhance sampling of conformational space, we identified cryptic pockets within the SARS-CoV-2 main protease, including some within regions far from the active site. These simulations sampled comparable dynamics and pocket volumes to conventional brute force simulations carried out on two orders of magnitude greater timescales.

De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites

De novo molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.

Inhibitors of bacterial H2S biogenesis targeting antibiotic resistance and tolerance

Emergent resistance to all clinical antibiotics calls for the next generation of therapeutics. Here we report an effective antimicrobial strategy targeting the bacterial hydrogen sulfide (H2S)-mediated defense system. We identified cystathionine γ-lyase (CSE) as the primary generator of H2S in two major human pathogens, Staphylococcus aureus and Pseudomonas aeruginosa, and discovered small molecules that inhibit bacterial CSE. These inhibitors potentiate bactericidal antibiotics against both pathogens in vitro and in mouse models of infection. CSE inhibitors also suppress bacterial tolerance, disrupting biofilm formation and substantially reducing the number of persister bacteria that survive antibiotic treatment. Our results establish bacterial H2S as a multifunctional defense factor and CSE as a drug target for versatile antibiotic enhancers.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

Structural Basis for the C-Terminal Domain of Mycobacterium tuberculosis Ribosome Maturation Factor RimM to Bind Ribosomal Protein S19

Multidrug-resistant tuberculosis (TB) is a serious threat to public health, calling for the development of new anti-TB drugs. Chaperon protein RimM, involved in the assembly of ribosomal protein S19 into 30S ribosomal subunit during ribosome maturation, is a potential drug target for TB treatment. The C-terminal domain (CTD) of RimM is primarily responsible for binding S19. However, both the CTD structure of RimM from Mycobacterium tuberculosis (MtbRimMCTD) and the molecular mechanisms underlying MtbRimMCTD binding S19 remain elusive. Here, we report the solution structure, dynamics features of MtbRimMCTD, and its interaction with S19. MtbRimMCTD has a rigid hydrophobic core comprised of a relatively conservative six-strand β-barrel, tailed with a short α-helix and interspersed with flexible loops. Using several biophysical techniques including surface plasmon resonance (SPR) affinity assays, nuclear magnetic resonance (NMR) assays, and molecular docking, we established a structural model of the MtbRimMCTD-S19 complex and indicated that the β4-β5 loop and two nonconserved key residues (D105 and H129) significantly contributed to the unique pattern of MtbRimMCTD binding S19, which might be implicated in a form of orthogonality for species-dependent RimM-S19 interaction. Our study provides the structural basis for MtbRimMCTD binding S19 and is beneficial to the further exploration of MtbRimM as a potential target for the development of new anti-TB drugs.

Discovery of New Catalytic Topoisomerase II Inhibitors for Anticancer Therapeutics

Poison inhibitors of DNA topoisomerase II (TOP2) are clinically used drugs that cause cancer cell death by inducing DNA damage, which mechanism of action is also associated with serious side effects such as secondary malignancy and cardiotoxicity. In contrast, TOP2 catalytic inhibitors induce limited DNA damage, have low cytotoxicity, and are effective in suppressing cancer cell proliferation. They have been sought after to be prospective anticancer therapies. Herein the discovery of new TOP2 catalytic inhibitors is described. A new druggable pocket of TOP2 protein at its DNA binding domain was used as a docking site to virtually screen ~6 million molecules from the ZINC15 library. The lead compound, T60, was characterized to be a catalytic TOP2 inhibitor that binds TOP2 protein and disrupts TOP2 from interacting with DNA, resulting in no DNA cleavage. It has low cytotoxicity, but strongly inhibits cancer cell proliferation and xenograft growth. T60 also inhibits androgen receptor activity and prostate cancer cell growth. These results indicate that T60 is a promising candidate compound that can be further developed into new anticancer drugs.

Structural Impacts of Drug-Resistance Mutations Appearing in HIV-2 Protease

The use of antiretroviral drugs is accompanied by the emergence of HIV-2 resistances. Thus, it is important to elucidate the mechanisms of resistance to antiretroviral drugs. Here, we propose a structural analysis of 31 drug-resistant mutants of HIV-2 protease (PR2) that is an important target against HIV-2 infection. First, we modeled the structures of each mutant. We then located structural shifts putatively induced by mutations. Finally, we compared wild-type and mutant inhibitor-binding pockets and interfaces to explore the impacts of these induced structural deformations on these two regions. Our results showed that one mutation could induce large structural rearrangements in side-chain and backbone atoms of mutated residue, in its vicinity or further. Structural deformations observed in side-chain atoms are frequent and of greater magnitude, that confirms that to fight drug resistance, interactions with backbone atoms should be favored. We showed that these observed structural deformations modify the conformation, volume, and hydrophobicity of the binding pocket and the composition and size of the PR2 interface. These results suggest that resistance mutations could alter ligand binding by modifying pocket properties and PR2 stability by impacting its interface. Our results reinforce the understanding of the effects of mutations that occurred in PR2 and the different mechanisms of PR2 resistance.

Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict?

Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R² ≥ 0.71. and MAE ≤ 0.85), and the pIC₅₀ data set (n = 306, R² ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R² but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design.

Elucidation of cryptic and allosteric pockets within the SARS-CoV-2 protease

The SARS-CoV-2 pandemic has rapidly spread across the globe, posing an urgent health concern. Many quests to computationally identify treatments against the virus rely on in silico small molecule docking to experimentally determined structures of viral proteins. One limit to these approaches is that protein dynamics are often unaccounted for, leading to overlooking transient, druggable conformational states. Using Gaussian accelerated molecular dynamics to enhance sampling of conformational space, we identified cryptic pockets within the SARS-CoV-2 main protease, including some within regions far from the active site and assed their druggability. These pockets can aid in virtual screening efforts to identify a protease inhibitor for the treatment of COVID-19.

Molecular Dynamics Simulations of Influenza A Virus NS1 Reveal a Remarkably Stable RNA-Binding Domain Harboring Promising Druggable Pockets

The non-structural protein NS1 of influenza A viruses is considered to be the major antagonist of the interferon system and antiviral defenses of the cell. It could therefore represent a suitable target for novel antiviral strategies. As a first step towards the identification of small compounds targeting NS1, we here investigated the druggable potential of its RNA-binding domain since this domain is essential to the biological activities of NS1. We explored the flexibility of the full-length protein by running molecular dynamics simulations on one of its published crystal structures. While the RNA-binding domain structure was remarkably stable along the simulations, we identified a flexible site at the two extremities of the “groove” that is delimited by the antiparallel α-helices that make up its RNA-binding interface. This groove region is able to form potential binding pockets, which, in 60% of the conformations, meet the druggability criteria. We characterized these pockets and identified the residues that contribute to their druggability. All the residues involved in the druggable pockets are essential at the same time to the stability of the RNA-binding domain and to the biological activities of NS1. They are also strictly conserved across the large sequence diversity of NS1, emphasizing the robustness of this search towards the identification of broadly active NS1-targeting compounds.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

Structure-based view of the druggable genome

International efforts are underway to develop chemical probes for specific protein families, and a 'Target 2035' call to expand these efforts towards a comprehensive chemical coverage of the druggable human genome was recently announced. But what is the druggable genome? Here, we systematically review structures of proteins bound to drug-like ligands available from the Protein Data Bank (PDB) and use ligand desolvation upon binding as a druggability metric to draw a landscape of the human druggable genome. The vast majority of druggable protein families, including some highly populated and disease-associated families, are almost orphan of small-molecule ligands. We propose a list of 46 druggable domains representing 3440 human proteins that could be the focus of large chemical probe discovery efforts.