Pocketome: an encyclopedia of small-molecule binding sites in 4D

A computational analysis of the molecular mechanisms underlying the effects of ibuprofen and dibutyl phthalate on gene expression in fish

The impact of emerging pollutants such as ibuprofen and dibutyl phthalate on aquatic species is a growing concern and the need for proper assessment and evaluation of these toxicants is imperative. The objective of this study was to examine the toxicogenomic impacts of ibuprofen and dibutyl phthalate on Clarias gariepinus, a widely distributed African catfish species. Results showed that exposure to the test compounds caused significant changes in gene expression, including upregulation of growth hormone, interleukin, melatonin receptors, 17β-Hydroxysteroid Dehydrogenase, heat shock protein, doublesex, and mab-3 related transcription factor. On the other hand, expression of forkhead Box Protein L2 and cytochrome P450 was downregulated, revealing a potential to induce female to male sex reversal. The binding affinities and hydrophobic interactions of the test compounds with the reference genes were also studied, showing that ibuprofen had the lowest binding energy and the highest affinity for the docked genes. Both compounds revealed a mutual molecular interaction with amino acids residues within the catalytic cavity of the docked genes. These results provide new insights into the toxic effects of ibuprofen and dibutyl phthalate on Clarias gariepinus, contributing to a better understanding of the environmental impact of these pollutants.

A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Modeling

High-quality protein-ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However, experimentally determined complex structures are scarce compared with the vast chemical space. In this study, we addressed this issue by constructing the BindingNet data set via comparative complex structure modeling, which contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogues' structure-activity relationships. It can also be used for evaluating machine-learning-based scoring functions. Our results indicate that machine learning models trained on BindingNet could reduce the bias caused by buried solvent-accessible surface area, as we previously found for models trained on the PDBbind data set. We also discussed strategies to improve BindingNet and its potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches. The BindingNet complements PDBbind in constructing a sufficient and unbiased protein-ligand binding data set and is freely available at http://bindingnet.huanglab.org.cn.

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening

Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 μM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.

Synergy of Mutation-Induced Effects in Human Vitamin K Epoxide Reductase: Perspectives and Challenges for Allo-Network Modulator Design

The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme transforming vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents delivered by its redox partner through thiol-disulfide exchange reactions. The luminal loop (L-loop) is the principal mediator of hVKORC1 activation, and it is a region frequently harbouring numerous missense mutations. Four L-loop hVKORC1 mutants, suggested in vitro as either resistant (A41S, H68Y) or completely inactive (S52W, W59R), were studied in the oxidised state by numerical approaches (in silico). The DYNASOME and POCKETOME of each mutant were characterised and compared to the native protein, recently described as a modular protein composed of the structurally stable transmembrane domain (TMD) and the intrinsically disordered L-loop, exhibiting quasi-independent dynamics. The DYNASOME of mutants revealed that L-loop missense point mutations impact not only its folding and dynamics, but also those of the TMD, highlighting a strong mutation-specific interdependence between these domains. Another consequence of the mutation-induced effects manifests in the global changes (geometric, topological, and probabilistic) of the newly detected cryptic pockets and the alternation of the recognition properties of the L-loop with its redox protein. Based on our results, we postulate that (i) intra-protein allosteric regulation and (ii) the inherent allosteric regulation and cryptic pockets of each mutant depend on its DYNASOME; and (iii) the recognition of the redox protein by hVKORC1 (INTERACTOME) depend on their DYNASOME. This multifaceted description of proteins produces "omics" data sets, crucial for understanding the physiological processes of proteins and the pathologies caused by alteration of the protein properties at various "omics" levels. Additionally, such characterisation opens novel perspectives for the development of "allo-network drugs" essential for the treatment of blood disorders.

Developing a comprehensive solution aimed to disrupt LARS1/RagD protein-protein interaction

Aminoacyl-tRNA synthetases are crucial enzymes involved in protein synthesis and various cellular physiological reactions. Aside from their standard role in linking amino acids to the corresponding tRNAs, they also impact protein homeostasis by controlling the level of soluble amino acids within the cell. For instance, leucyl-tRNA synthetase (LARS1) acts as a leucine sensor for the mammalian target of rapamycin complex 1 (mTORC1), and may also function as a probable GTPase-activating protein (GAP) for the RagD subunit of the heteromeric activator of mTORC1. In turn, mTORC1 regulates cellular processes, such as protein synthesis, autophagy, and cell growth, and is implicated in various human diseases including cancer, obesity, diabetes, and neurodegeneration. Hence, inhibitors of mTORC1 or a deregulated mTORC1 pathway may offer potential cancer therapies. In this study, we investigated the structural requirements for preventing the sensing and signal transmission from LARS to mTORC1. Building upon recent studies on mTORC1 regulation activation by leucine, we lay the foundation for the development of chemotherapeutic agents against mTORC1 that can overcome resistance to rapamycin. Using a combination of in-silico approaches to develop and validate an alternative interaction model, discussing its benefits and advancements. Finally, we identified a set of compounds ready for testing to prevent LARS1/RagD protein-protein interactions. We establish a basis for creating chemotherapeutic drugs targeting mTORC1, which can conquer resistance to rapamycin. We utilize in-silico methods to generate and confirm an alternative interaction model, outlining its advantages and improvements, and pinpoint a group of novel substances that can prevent LARS1/RagD interactions.Communicated by Ramaswamy H. Sarma.

Expanding Training Data for Structure-Based Receptor-Ligand Binding Affinity Regression through Imputation of Missing Labels

The success of machine learning is, in part, due to a large volume of data available to train models. However, the amount of training data for structure-based molecular property prediction remains limited. The previously described CrossDocked2020 data set expanded the available training data for binding pose classification in a molecular docking setting but did not address expanding the amount of receptor-ligand binding affinity data. We present experiments demonstrating that imputing binding affinity labels for complexes without experimentally determined binding affinities is a viable approach to expanding training data for structure-based models of receptor-ligand binding affinity. In particular, we demonstrate that utilizing imputed labels from a convolutional neural network trained only on the affinity data present in CrossDocked2020 results in a small improvement in the binding affinity regression performance, despite the additional sources of noise that such imputed labels add to the training data. The code, data splits, and imputation labels utilized in this paper are freely available at https://github.com/francoep/ImputationPaper.

Effect of Colchicine Binding Site Inhibitors on the Tubulin Intersubunit Interaction

Microtubules are dynamic, non-covalent polymers consisting of α- and β-tubulin subunits that are involved in a wide range of intracellular processes. The polymerization and dynamics of microtubules are regulated by many factors, including small molecules that interact with different sites on the tubulin dimer. Colchicine binding site inhibitors (CBSIs) destabilize microtubules and inhibit tubulin polymerization, leading to cell cycle arrest. Because of their therapeutic potential, the molecular mechanism of CBSI function is an area of active research. Nevertheless, important details of this mechanism have yet to be resolved. In this study, we use atomistic molecular dynamics simulations to show that the binding of CBSIs to the tubulin heterodimer leads to the weakening of tubulin intersubunit interaction. Using atomistic molecular dynamics simulations and binding free energy calculations, we show that CBSIs act as protein-protein interaction inhibitors and destabilize interlinkage between α and β subunits, which is crucial for longitudinal contacts in the microtubule lattice. Our results offer new insight into the mechanisms of microtubule polymerization inhibition by colchicine and its analogs.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

Probing the orphan receptors: Tools and directions

The endogenous ligands activating a large fraction of the G Protein Coupled Receptor (GPCR) family members have yet to be identified. These receptors are commonly labeled as orphans (oGPCRs), and because of the absence of available pharmacological tools they are currently understudied. Nonetheless, genome wide association studies, together with research using animal models identified many physiological functions regulated by oGPCRs. Similarly, mutations in some oGPCRs have been associated with rare genetic disorders or with an increased risk of developing pathologies. The once underestimated pharmacological potential of targeting oGPCRs is increasingly being exploited by the development of novel tools to understand their biology and by drug discovery endeavors aimed at identifying new modulators of their activity. Here, we summarize recent advancements in the field of oGPCRs and future directions.

1D2DSimScore: A novel method for comparing contacts in biomacromolecules and their complexes

The biologically relevant structures of proteins and nucleic acids and their complexes are dynamic. They include a combination of regions ranging from rigid structural segments to structural switches to regions that are almost always disordered, which interact with each other in various ways. Comparing conformational changes and variation in contacts between different conformational states is essential to understand the biological functions of proteins, nucleic acids, and their complexes. Here, we describe a new computational tool, 1D2DSimScore, for comparing contacts and contact interfaces in all kinds of macromolecules and macromolecular complexes, including proteins, nucleic acids, and other molecules. 1D2DSimScore can be used to compare structural features of macromolecular models between alternative structures obtained in a particular experiment or to score various predictions against a defined "ideal" reference structure. Comparisons at the level of contacts are particularly useful for flexible molecules, for which comparisons in 3D that require rigid-body superpositions are difficult, and in biological systems where the formation of specific inter-residue contacts is more relevant for the biological function than the maintenance of a specific global 3D structure. Similarity/dissimilarity scores calculated by 1D2DSimScore can be used to complement scores describing 3D structural similarity measures calculated by the existing tools.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome

Location and properties of ligand binding sites provide important information to uncover protein functions and to direct structure-based drug design approaches. However, as binding site detection depends on the three-dimensional (3D) structural data of proteins, functional analysis based on protein ligand binding sites is formidable for proteins without structural information. Recent developments in protein structure prediction and the 3D structures built by AlphaFold provide an unprecedented opportunity for analyzing ligand binding sites in human proteins. Here, we constructed the CavitySpace database, the first pocket library for all the proteins in the human proteome, using a widely-applied ligand binding site detection program CAVITY. Our analysis showed that known ligand binding sites could be well recovered. We grouped the predicted binding sites according to their similarity which can be used in protein function prediction and drug repurposing studies. Novel binding sites in highly reliable predicted structure regions provide new opportunities for drug discovery. Our CavitySpace is freely available and provides a valuable tool for drug discovery and protein function studies.

Discovery of a novel inhibitor against urokinase-type plasminogen activator, a potential enzyme with a role in atherosclerotic plaque instability

The buildup of lipids, cholesterol, and other substances in and on the walls of the arteries is known as atherosclerosis and deposition is known as atherosclerotic plaque. Urokinase-type plasminogen activator (uPA) has multiple roles in the atherosclerotic plaque formation and even work simultaneously in making the atherosclerotic plaque unstable. Extracellular matrix plays a major role in the plaque remodeling and rapture. In this study, we have accessed that a higher interaction was observed in the molecular interaction score for uPA with ZINC380065722 having a GOLD fitness score of about 67.60, which is much higher as compared to the known standard inhibitor UK 122 which has reported an interaction score of 59.14. Ser217 and Asp192 are found to be the key amino acid residues in almost all the interactions. Protein frustration analysis has shown that these amino acid residues play a crucial role in the retention of the active pocket conformation and any mutation of these two residues can causes serious decrease in the overall function of the protein. It was observed that the molecule ZINC380065722 remained bound to the protein till 100 ns of simulation time. The average SASA for the apo-uPA and uPA-ligand complex was found to be stable. The network of hydrogen bonds for the intramolecular protein secondary structure and with the solvent system for the apo-protein and the uPA-ligand complex was found to be consistent.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening and biological evaluation of novel inhibitors of mycobacterium Z-ring formation

The major part of commercial prodrugs against Mycobacterium tuberculosis (Mtb) demonstrated a significant inhibitory effect on cell division and inhibition of bacterial growth in vitro. However, further implementation often failed to overcome the compensatory system of interchangeable cascades. This is the most common situation for the compounds, which hit the key enzymes activities involved in all basic stages of the cell cycle. We decided to find more compounds, which could affect a cytoskeleton complex playing important role in sensing the external signals, intracellular transport, and cell division. In general, the bacterial cytoskeleton is crucial for response to the environment and participates in cell-to-cell communication. In turn, filamentous temperature-sensitive Z (FtsZ) protein, a mycobacterial tubulin homolog, is essential for Z-ring formation and further bacteria cell division. We predicted the most preferable binding-sites and conducted a high-throughput virtual screening. Modeling results suggest that some compounds bind in a specific region on the surface Mtb FtsZ, which is absent in human, and other could hit GTPase activity of the FtsZ. Further in vitro studies confirmed that these novel molecules can efficiently bind to these pockets, demonstrating an effect on the polymerization state and kinetics mechanisms. The rescaling of the experiment on the cell line revealed that reported compounds are able to alter the polymerization level of the filamentous and, therefore, prevent mycobacteria reproduction.

A new microtubule-stabilizing agent shows potent antiviral effects against African swine fever virus with no cytotoxicity

African swine fever virus (ASFV) is the causal agent of a fatal disease of domestic swine for which no effective antiviral drugs are available. Recently, it has been shown that microtubule-targeting agents hamper the infection cycle of different viruses. In this study, we conducted in silico screening against the colchicine binding site (CBS) of tubulin and found three new compounds with anti-ASFV activity. The most promising antiviral compound (6b) reduced ASFV replication in a dose-dependent manner (IC50 = 19.5 μM) with no cellular (CC50 > 500 μM) and animal toxicity (up to 100 mg/kg). Results also revealed that compound 6b interfered with ASFV attachment, internalization and egress, with time-of-addition assays, showing that compound 6b has higher antiviral effects when added within 2-8 h post-infection. This compound significantly inhibited viral DNA replication and disrupted viral protein synthesis. Experiments with ASFV-infected porcine macrophages disclosed that antiviral effects of the compound 6b were similar to its effects in Vero cells. Tubulin polymerization assay and confocal microscopy demonstrated that compound 6b promoted tubulin polymerization, acting as a microtubule-stabilizing, rather than a destabilizing agent in cells. In conclusion, this work emphasizes the idea that microtubules can be targets for drug development against ASFV.

Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision

Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.

2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.

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.

Inverse Virtual Screening for the rapid re-evaluation of the presumed biological safe profile of natural products. The case of steviol from Stevia rebaudiana glycosides on farnesoid X receptor (FXR)

Nonnutritive sweeteners (NNSs) are widely employed as dietary substitutes for classical sugars thanks to their safety profile and low toxicity. In this study, a re-evaluation of the biological effects of steviol (1), the main metabolite from Stevia rebaudiana glycosides, was performed using the Inverse Virtual Screening (IVS) target fishing computational approach. Starting from well-known pharmacological properties of Stevia rebaudiana glycosides, this computational tool was employed for predicting the putative interacting targets of 1 and, afterwards, of its five synthetic ester derivatives 2-6, accounting a large panel of proteins involved in cancer and inflammation events. Applying this methodology, the farnesoid X receptor (FXR) was identified as the putative target partner of 1-6. The predicted ligand-protein interactions were corroborated by transactivation assays, specifically disclosing the agonistic activity of 1 and the antagonistic activities of 2-6 on FXR. The reported results highlight the feasibility of IVS as a fast and potent tool for predicting the interacting targets of query compounds, addressing the re-evaluation of their bioactivity. In light of the obtained results, the presumably safe profile of known compounds, such as the case of steviol (1), is critically discussed.

Multiscale Simulations of SARS-CoV-2 3CL Protease Inhibition with Aldehyde Derivatives. Role of Protein and Inhibitor Conformational Changes in the Reaction Mechanism

We here investigate the mechanism of SARS-CoV-2 3CL protease inhibition by one of the most promising families of inhibitors, those containing an aldehyde group as a warhead. These compounds are covalent inhibitors that inactivate the protease, forming a stable hemithioacetal complex. Inhibitor 11a is a potent inhibitor that has been already tested in vitro and in animals. Using a combination of classical and QM/MM simulations, we determined the binding mode of the inhibitor into the active site and the preferred rotameric state of the catalytic histidine. In the noncovalent complex, the aldehyde group is accommodated into the oxyanion hole formed by the NH main-chain groups of residues 143 to 145. In this pose, P1-P3 groups of the inhibitor mimic the interactions established by the natural peptide substrate. The reaction is initiated with the formation of the catalytic dyad ion pair after a proton transfer from Cys145 to His41. From this activated state, covalent inhibition proceeds with the nucleophilic attack of the deprotonated Sγ atom of Cys145 to the aldehyde carbon atom and a water-mediated proton transfer from the Nε atom of His41 to the aldehyde oxygen atom. Our proposed reaction transition-state structure is validated by comparison with X-ray data of recently reported inhibitors, while the activation free energy obtained from our simulations agrees with the experimentally derived value, supporting the validity of our findings. Our study stresses the interplay between the conformational dynamics of the inhibitor and the protein with the inhibition mechanism and the importance of including conformational diversity for accurate predictions about the inhibition of the main protease of SARS-CoV-2. The conclusions derived from our work can also be used to rationalize the behavior of other recently proposed inhibitor compounds, including aldehydes and ketones with high inhibitory potency.

Evolution of Novartis' Small Molecule Screening Deck Design

This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.

Redirecting cytotoxic T cells with chemically programmed antibodies

T-cell engaging bispecific antibodies (T-biAbs) mediate potent and selective cytotoxicity by combining specificities for target and effector cells in one molecule. Chemically programmed T-biAbs (cp-T-biAbs) are precisely assembled compositions of (i) small molecules that govern cancer cell surface targeting with high affinity and specificity and (ii) antibodies that recruit and activate T cells and equip the small molecule with confined biodistribution and longer circulatory half-life. Conceptually similar to cp-T-biAbs, switchable chimeric antigen receptor T cells (sCAR-Ts) can also be put under the control of small molecules by using a chemically programmed antibody as a bispecific adaptor molecule. As such, cp-T-biAbs and cp-sCAR-Ts can endow small molecules with the power of cancer immunotherapy. We here review the concept of chemically programmed antibodies for recruiting and activating T cells as a promising strategy for broadening the utility of small molecules in cancer therapy.

Synthesis, Optimization, Antifungal Activity, Selectivity, and CYP51 Binding of New 2-Aryl-3-azolyl-1-indolyl-propan-2-ols

A series of 2-aryl-3-azolyl-1-indolyl-propan-2-ols was designed as new analogs of fluconazole (FLC) by replacing one of its two triazole moieties by an indole scaffold. Two different chemical approaches were then developed. The first one, in seven steps, involved the synthesis of the key intermediate 1-(1H-benzotriazol-1-yl)methyl-1H-indole and the final opening of oxiranes by imidazole or 1H-1,2,4-triazole. The second route allowed access to the target compounds in only three steps, this time with the ring opening by indole and analogs. Twenty azole derivatives were tested against Candida albicans and other Candida species. The enantiomers of the best anti-Candida compound, 2-(2,4-dichlorophenyl)-3-(1H-indol-1-yl)-1-(1H-1,2,4-triazol-1-yl)-propan-2-ol (8g), were analyzed by X-ray diffraction to determine their absolute configuration. The (−)-8g enantiomer (Minimum inhibitory concentration (MIC) = IC₈₀ = 0.000256 µg/mL on C. albicans CA98001) was found with the S-absolute configuration. In contrast the (+)-8g enantiomer was found with the R-absolute configuration (MIC = 0.023 µg/mL on C. albicans CA98001). By comparison, the MIC value for FLC was determined as 0.020 µg/mL for the same clinical isolate. Additionally, molecular docking calculations and molecular dynamics simulations were carried out using a crystal structure of Candida albicans lanosterol 14α-demethylase (CaCYP51). The (−)-(S)-8g enantiomer aligned with the positioning of posaconazole within both the heme and access channel binding sites, which was consistent with its biological results. All target compounds have been also studied against human fetal lung fibroblast (MRC-5) cells. Finally, the selectivity of four compounds on a panel of human P450-dependent enzymes (CYP19, CYP17, CYP26A1, CYP11B1, and CYP11B2) was investigated.

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level

Background

Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces.

Results

To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets.

Conclusions

We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.

HIV-1 fusion inhibitors targeting the membrane-proximal external region of Env spikes

Combination antiretroviral therapy has transformed HIV-1 infection, once a fatal illness, into a manageable chronic condition. Drug resistance, severe side effects and treatment noncompliance bring challenges to combination antiretroviral therapy implementation in clinical settings and indicate the need for additional molecular targets. Here, we have identified several small-molecule fusion inhibitors, guided by a neutralizing antibody, against an extensively studied vaccine target-the membrane proximal external region (MPER) of the HIV-1 envelope spike. These compounds specifically inhibit the HIV-1 envelope-mediated membrane fusion by blocking CD4-induced conformational changes. An NMR structure of one compound complexed with a trimeric MPER construct reveals that the compound partially inserts into a hydrophobic pocket formed exclusively by the MPER residues, thereby stabilizing its prefusion conformation. These results suggest that the MPER is a potential therapeutic target for developing fusion inhibitors and that strategies employing an antibody-guided search for novel therapeutics may be applied to other human diseases.

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.

Recommendations for Improving Methods and Models for Aquatic Hazard Assessment of Ionizable Organic Chemicals

Ionizable organic chemicals (IOCs) such as organic acids and bases are an important substance class requiring aquatic hazard evaluation. Although the aquatic toxicity of IOCs is highly dependent on the water pH, many toxicity studies in the literature cannot be interpreted because pH was not reported or not kept constant during the experiment, calling for an adaptation and improvement of testing guidelines. The modulating influence of pH on toxicity is mainly caused by pH-dependent uptake and bioaccumulation of IOCs, which can be described by ion-trapping and toxicokinetic models. The internal effect concentrations of IOCs were found to be independent of the external pH because of organisms' and cells' ability to maintain a stable internal pH milieu. If the external pH is close to the internal pH, existing quantitative structure-activity relationships (QSARs) for neutral organics can be adapted by substituting the octanol-water partition coefficient by the ionization-corrected liposome-water distribution ratio as the hydrophobicity descriptor, demonstrated by modification of the target lipid model. Charged, zwitterionic and neutral species of an IOC can all contribute to observed toxicity, either through concentration-additive mixture effects or by interaction of different species, as is the case for uncoupling of mitochondrial respiration. For specifically acting IOCs, we recommend a 2-step screening procedure with ion-trapping/QSAR models used to predict the baseline toxicity, followed by adjustment using the toxic ratio derived from in vitro systems. Receptor- or plasma-binding models also show promise for elucidating IOC toxicity. The present review is intended to help demystify the ecotoxicity of IOCs and provide recommendations for their hazard and risk assessment. Environ Toxicol Chem 2020;39:269-286. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Druggable exosites of the human kino-pocketome

Small molecules binding at any of the multiple regulatory sites on the molecular surface of a protein kinase may stabilize or disrupt the corresponding interaction, leading to consequent modulation of the kinase cellular activity. As such, each of these sites represents a potential drug target. Even targeting sites outside the immediate ATP site, the so-called exosites, may cause desirable biological effects through an allosteric mechanism. Targeting exosites can alleviate adverse effects and toxicity that is common when ATP-site compounds bind promiscuously to many other types of kinases. In this study we have identified, catalogued, and annotated all potentially druggable exosites on the protein kinase domains within the existing structural human kinome. We then priority-ranked these exosites by those most amenable to drug design. In order to identify pockets that are either consistent across the kinome, or unique and specific to a particular structure, we have also implemented a normalized representation of all pockets, and displayed these graphically. Finally, we have built a database and designed a web-based interface for users interested in accessing the 3-dimensional representations of these pockets. We envision this information will assist drug discovery efforts searching for untargeted binding pockets in the human kinome.

CavBench: A benchmark for protein cavity detection methods

Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.

Protein pocket detection via convex hull surface evolution and associated Reeb graph

Motivation

Protein pocket information is invaluable for drug target identification, agonist design, virtual screening and receptor-ligand binding analysis. A recent study indicates that about half holoproteins can simultaneously bind multiple interacting ligands in a large pocket containing structured sub-pockets. Although this hierarchical pocket and sub-pocket structure has a significant impact to multi-ligand synergistic interactions in the protein binding site, there is no method available for this analysis. This work introduces a computational tool based on differential geometry, algebraic topology and physics-based simulation to address this pressing issue.

Results

We propose to detect protein pockets by evolving the convex hull surface inwards until it touches the protein surface everywhere. The governing partial differential equations (PDEs) include the mean curvature flow combined with the eikonal equation commonly used in the fast marching algorithm in the Eulerian representation. The surface evolution induced Morse function and Reeb graph are utilized to characterize the hierarchical pocket and sub-pocket structure in controllable detail. The proposed method is validated on PDBbind refined sets of 4414 protein-ligand complexes. Extensive numerical tests indicate that the proposed method not only provides a unique description of pocket-sub-pocket relations, but also offers efficient estimations of pocket surface area, pocket volume and pocket depth.

Availability and implementation

Source code available at https://github.com/rdzhao/ProteinPocketDetection. Webserver available at http://weilab.math.msu.edu/PPD/.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (K_a, K_d, K_i, IC₅₀) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

Extended Multitarget Pharmacology of Anticancer Drugs

Multitarget pharmacology of small-molecule cancer drugs significantly contributes to their mechanism of action, side effects, and emergence of drug resistance and opens ways to repurpose, combine, or customize drug therapy. In most cases, the set of targets affected at therapeutic concentrations is not fully characterized and/or the interaction efficacy values are not accurately quantified. We collected information about multiple targets for each cancer drug along with their experimental effective concentrations or binding activities from multiple sources. All multitarget activity values for each drug then were used to build two proximity network pharmacology maps of anticancer drugs and targets of those drugs, respectively. Together with the network map, we showed that the majority of the cancer drugs had substantial multitarget pharmacology based on our current knowledge. In addition, most of the cancer drugs simultaneously affect macromolecular targets from different classes and types. The target subset can further be accentuated and personalized by patient sample-specific expression data. The network maps of cancer drugs and targets as well as all quantified activity data were integrated into a freely available database, CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drugnet.html). The identified multitarget pharmacology of cancer drugs is essential for improving the efficacy of individually prescribed drugs and drug combinations and minimization of adverse effects.

2-Aminothiazole Derivatives as Selective Allosteric Modulators of the Protein Kinase CK2. 1. Identification of an Allosteric Binding Site

CK2 is a ubiquitous Ser/Thr protein kinase involved in the control of various signaling pathways and is known to be constitutively active. In the present study, we identified aryl 2-aminothiazoles as a novel class of CK2 inhibitors, which displayed a non-ATP-competitive mode of action and stabilized an inactive conformation of CK2 in solution. Enzyme kinetics studies, STD NMR, circular dichroism spectroscopy, and native mass spectrometry experiments demonstrated that the compounds bind in an allosteric pocket outside the ATP-binding site. Our data, combined with molecular docking studies, strongly suggested that this new binding site was located at the interface between the αC helix and the flexible glycine-rich loop. A first hit optimization led to compound 7, exhibiting an IC₅₀ of 3.4 μM against purified CK2α in combination with a favorable selectivity profile. Thus, we identified a novel class of CK2 inhibitors targeting an allosteric pocket, offering great potential for further optimization into anticancer drugs.

An Augmented Pocketome: Detection and Analysis of Small-Molecule Binding Pockets in Proteins of Known 3D Structure

Protein-ligand interactions form the basis of most cellular events. Identifying ligand binding pockets in proteins will greatly facilitate rationalizing and predicting protein function. Ligand binding sites are unknown for many proteins of known three-dimensional (3D) structure, creating a gap in our understanding of protein structure-function relationships. To bridge this gap, we detect pockets in proteins of known 3D structures, using computational techniques. This augmented pocketome (PocketDB) consists of 249,096 pockets, which is about seven times larger than what is currently known. We deduce possible ligand associations for about 46% of the newly identified pockets. The augmented pocketome, when subjected to clustering based on similarities among pockets, yielded 2,161 site types, which are associated with 1,037 ligand types, together providing fold-site-type-ligand-type associations. The PocketDB resource facilitates a structure-based function annotation, delineation of the structural basis of ligand recognition, and provides functional clues for domains of unknown functions, allosteric proteins, and druggable pockets.

New Binding Sites, New Opportunities for GPCR Drug Discovery

Many central biological events rely on protein-ligand interactions. The identification and characterization of protein-binding sites for ligands are crucial for the understanding of functions of both endogenous ligands and synthetic drug molecules. G protein-coupled receptors (GPCRs) typically detect extracellular signal molecules on the cell surface and transfer these chemical signals across the membrane, inducing downstream cellular responses via G proteins or β-arrestin. GPCRs mediate many central physiological processes, making them important targets for modern drug discovery. Here, we focus on the most recent breakthroughs in finding new binding sites and binding modes of GPCRs and their potentials for the development of new medicines.

Aminergic GPCR-Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data

The aminergic family of G protein-coupled receptors (GPCRs) plays an important role in various diseases and represents a major drug discovery target class. Structure determination of all major aminergic subfamilies has enabled structure-based ligand design for these receptors. Site-directed mutagenesis data provides an invaluable complementary source of information for elucidating the structural determinants of binding of different ligand chemotypes. The current study provides a comparative analysis of 6692 mutation data points on 34 aminergic GPCR subtypes, covering the chemical space of 540 unique ligands from mutagenesis experiments and information from experimentally determined structures of 52 distinct aminergic receptor-ligand complexes. The integrated analysis enables detailed investigation of structural receptor-ligand interactions and assessment of the transferability of combined binding mode and mutation data across ligand chemotypes and receptor subtypes. An overview is provided of the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.

GPCR homology model template selection benchmarking: Global versus local similarity measures

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development. GPCR ligand interaction studies often have a starting point with either crystal structures or comparative models. The majority of GPCR do not have experimentally-characterized 3-dimensional structures, so comparative modeling, also called homology modeling, is a good structure-based starting point. Comparative modeling is a widely used method for generating models of proteins with unknown structures by analogy to crystallized proteins that are expected to exhibit structural conservation. Traditionally, comparative modeling template selection is based on global sequence identity and shared function. However high sequence identity localized to the ligand binding pocket may produce better models to examine protein-ligand interactions. This in silico benchmark study examined the performance of a global versus local similarity measure applied to comparative modeling template selection for 6 previously crystallized, class A GCPR (CXCR4, FFAR1, NOP, P2Y12, OPRK, and M1) with the long-term goal of optimizing GPCR ligand identification efforts. Comparative models were generated from templates selected using both global and local similarity measures. Similarity to reference crystal structures was reflected in RMSD values between atom positions throughout the structure or localized to the ligand binding pocket. Overall, models deviated from the reference crystal structure to a similar degree regardless of whether the template was selected using a global or local similarity measure. Ligand docking simulations were performed to assess relative performance in predicting protein-ligand complex structures and interaction networks. Calculated RMSD values between ligand poses from docking simulations and crystal structures indicate that models based on locally selected templates give docked poses that better mimic crystallographic ligand positions than those based on globally-selected templates in five of the six benchmark cases. However, protein model refinement strategies in advance of ligand docking applications are clearly essential as the average RMSD between crystallographic poses and poses docked into local template models was 9.7 Å and typically less than half of the ligand interaction sites are shared between the docked and crystallographic poses. These data support the utilization of local similarity measures to guide template selection in protocols using comparative models to investigate ligand-receptor interactions.