Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking

Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target

The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.

Docking for Molecules That Bind in a Symmetric Stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of great current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures, the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural-network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study, SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Docking for molecules that bind in a symmetric stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of much current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed, and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

A generic binding pocket for small molecule IKs activators at the extracellular inter-subunit interface of KCNQ1 and KCNE1 channel complexes

The cardiac IKs ion channel comprises KCNQ1, calmodulin, and KCNE1 in a dodecameric complex which provides a repolarizing current reserve at higher heart rates and protects from arrhythmia syndromes that cause fainting and sudden death. Pharmacological activators of IKs are therefore of interest both scientifically and therapeutically for treatment of IKs loss-of-function disorders. One group of chemical activators are only active in the presence of the accessory KCNE1 subunit and here we investigate this phenomenon using molecular modeling techniques and mutagenesis scanning in mammalian cells. A generalized activator binding pocket is formed extracellularly by KCNE1, the domain-swapped S1 helices of one KCNQ1 subunit and the pore/turret region made up of two other KCNQ1 subunits. A few residues, including K41, A44 and Y46 in KCNE1, W323 in the KCNQ1 pore, and Y148 in the KCNQ1 S1 domain, appear critical for the binding of structurally diverse molecules, but in addition, molecular modeling studies suggest that induced fit by structurally different molecules underlies the generalized nature of the binding pocket. Activation of IKs is enhanced by stabilization of the KCNQ1-S1/KCNE1/pore complex, which ultimately slows deactivation of the current, and promotes outward current summation at higher pulse rates. Our results provide a mechanistic explanation of enhanced IKs currents by these activator compounds and provide a map for future design of more potent therapeutically useful molecules.

Potential Binding Sites of Pharmacological Chaperone NCGC00241607 on Mutant β-Glucocerebrosidase and Its Efficacy on Patient-Derived Cell Cultures in Gaucher and Parkinson's Disease

Mutations in the GBA1 gene, encoding the lysosomal enzyme glucocerebrosidase (GCase), cause Gaucher disease (GD) and are the most common genetic risk factor for Parkinson's disease (PD). Pharmacological chaperones (PCs) are being developed as an alternative treatment approach for GD and PD. To date, NCGC00241607 (NCGC607) is one of the most promising PCs. Using molecular docking and molecular dynamics simulation we identified and characterized six allosteric binding sites on the GCase surface suitable for PCs. Two sites were energetically more preferable for NCGC607 and located nearby to the active site of the enzyme. We evaluated the effects of NCGC607 treatment on GCase activity and protein levels, glycolipids concentration in cultured macrophages from GD (n = 9) and GBA-PD (n = 5) patients as well as in induced human pluripotent stem cells (iPSC)-derived dopaminergic (DA) neurons from GBA-PD patient. The results showed that NCGC607 treatment increased GCase activity (by 1.3-fold) and protein levels (by 1.5-fold), decreased glycolipids concentration (by 4.0-fold) in cultured macrophages derived from GD patients and also enhanced GCase activity (by 1.5-fold) in cultured macrophages derived from GBA-PD patients with N370S mutation (p < 0.05). In iPSC-derived DA neurons from GBA-PD patients with N370S mutation NCGC607 treatment increased GCase activity and protein levels by 1.1-fold and 1.7-fold (p < 0.05). Thus, our results showed that NCGC607 could bind to allosteric sites on the GCase surface and confirmed its efficacy on cultured macrophages from GD and GBA-PD patients as well as on iPSC-derived DA neurons from GBA-PD patients.

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.

Homology Modeling of Transporter Proteins

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors

Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures^1-4 and ultra-large libraries of virtual compounds^5,6. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries⁷, new approaches to compound screening are needed^8,9. Here we introduce a modular synthon-based approach-V-SYNTHES-to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold-synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (K_i) = 0.9 nM) and CB₂/CB₁ selectivity (50-200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors-S/Ace2, Tmprss2, Cathepsins L and K, and Mpro-to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

Frictional hyperspheres in hyperspace

We extend the formulation of the discrete element method, which is typically used to simulate granular media, to describe arbitrarily large numbers of spatial dimensions and the collisions of frictional hyperspheres in these simulations. These higher dimensional simulations require complex visualization techniques, which are also developed here. Under uniaxial compression, we find that the stiffness of a granular medium is independent of the dimension for dimensions greater than one. In the dense flow regime, we show that the compressibility and frictional properties of higher dimensional granular materials can be described by a common rheology, with the main distinction between dimensions being the packing fraction. Results from these simulations extend our understanding of the effects of dimensionality on the behavior of granular materials, and on elastic and frictional properties in higher dimensions.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Structure-Based Virtual Screening of Ultra-Large Library Yields Potent Antagonists for a Lipid GPCR

Cysteinyl leukotriene G protein-coupled receptors, CysLT1R and CysLT2R, regulate bronchoconstrictive and pro-inflammatory effects and play a key role in allergic disorders, cardiovascular diseases, and cancer. CysLT1R antagonists have been widely used to treat asthma disorders, while CysLT2R is a potential target against uveal melanoma. However, very few selective antagonist chemotypes for CysLT receptors are available, and the design of such ligands has proved to be challenging. To overcome this obstacle, we took advantage of recently solved crystal structures of CysLT receptors and an ultra-large Enamine REAL library, representing a chemical space of 680 M readily available compounds. Virtual ligand screening employed 4D docking models comprising crystal structures of CysLT1R and CysLT2R and their corresponding ligand-optimized models. Functional assessment of the candidate hits yielded discovery of five novel antagonist chemotypes with sub-micromolar potencies and the best Ki = 220 nM at CysLT1R. One of the hits showed inverse agonism at the L129Q constitutively active mutant of CysLT2R, with potential utility against uveal melanoma.

Integrating molecular modelling methods to advance influenza A virus drug discovery

Since the discovery of the anti-influenza drugs oseltamivir and zanamivir using computer-aided drug design methods, there have been significant applications of molecular modelling methodologies applied to influenza A virus drug discovery, such as molecular dynamics (MD) simulation, molecular docking, and virtual screening (VS). In this review, we provide a brief general introduction to molecular modelling in the context of drug discovery and then focus on the advances and impact of integrating these methods with specific reference to potential influenza A antiviral drug targets.

Conjugation of biphenyl groups with poly(ethylene glycol) to enhance inhibitory effects on the PD-1/PD-L1 immune checkpoint interaction

Monoclonal antibodies have been developed as anticancer agents to block immune checkpoint pathways associated with programmed cell death 1 (PD-1) and its ligand PD-L1. However, the high cost of antibodies has encouraged researchers to develop other inhibitor types. Here, biphenyl compounds were conjugated with poly(ethylene glycol) (PEG) to enhance the activity of small molecular inhibitors. Immunoassay results revealed the decrease in the inhibition activity following conjugation with linear PEG, suggesting that the PEG moiety reduced the interaction between the biphenyl structure and PD-L1. However, the inhibitory effect on PD-1/PD-L1 interaction was further enhanced by using branched PEG conjugates. The increase in the number of conjugated biphenyl compounds resulted in increased inhibitory activity. The highest IC50 value was 0.33 μM, which was about 5 times higher than that observed for a non-conjugated monovalent compound. The inhibitory activity was more than 20 times the activity reported for the starting compound. Considering the increase in the inhibition activity, this multivalent strategy can be useful in the design of new immune checkpoint inhibitors.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Preparation of Biphenyl-Conjugated Bromotyrosine for Inhibition of PD-1/PD-L1 Immune Checkpoint Interactions

Cancer immunotherapy has been revolutionized by the development of monoclonal antibodies (mAbs) that inhibit interactions between immune checkpoint molecules, such as programmed cell-death 1 (PD-1), and its ligand PD-L1. However, mAb-based drugs have some drawbacks, including poor tumor penetration and high production costs, which could potentially be overcome by small molecule drugs. BMS-8, one of the potent small molecule drugs, induces homodimerization of PD-L1, thereby inhibiting its binding to PD-1. Our assay system revealed that BMS-8 inhibited the PD-1/PD-L1 interaction with IC₅₀ of 7.2 μM. To improve the IC₅₀ value, we designed and synthesized a small molecule based on the molecular structure of BMS-8 by in silico simulation. As a result, we successfully prepared a biphenyl-conjugated bromotyrosine (X) with IC₅₀ of 1.5 μM, which was about five times improved from BMS-8. We further prepared amino acid conjugates of X (amino-X), to elucidate a correlation between the docking modes of the amino-Xs and IC₅₀ values. The results suggested that the displacement of amino-Xs from the BMS-8 in the pocket of PD-L1 homodimer correlated with IC₅₀ values. This observation provides us a further insight how to derivatize X for better inhibitory effect.

Identification and experimental confirmation of novel cGMP efflux inhibitors by virtual ligand screening of vardenafil-analogues

BACKGROUND

Clinical studies have reported overexpression of PDE5 and elevation of intracellular cyclic GMP in various types of cancer cells. ABCC5 transports cGMP out of the cells with high affinity. PDE5 inhibitors prevent both cellular metabolism and cGMP efflux by inhibiting ABCC5 as well as PDE5. Increasing intracellular cGMP is hypothesized to promote apoptosis and growth restriction in tumor cells and also has potential for clinical use in treatment of cardiovascular disease and erectile dysfunction. Vardenafil is a potent inhibitor of both PDE5 and ABCC5-mediated cGMP cellular efflux. Nineteen novel vardenafil analogs that have been predicted as potent inhibitors by VLS were chosen for tests of their ability to inhibit ATP- dependent transport of cGMP by measuring the accumulation of cyclic GMP in inside-out vesicles.

AIM

In this study, we investigated the ability of nineteen new compounds to inhibit ABCC5- mediated cGMP transport. We also determined the Ki values of the six most potent compounds.

METHODS

Preparation of human erythrocyte inside out vesicles and transport assay.

RESULTS

Ki values for six of nineteen compounds that showed more than 50 % inhibition of cGMP transport in the screening test were determined and ranged from 1.1 to 23.1 μM. One compound was significantly more potent than the positive control, sildenafil.

CONCLUSION

Our findings show that computational screening correctly identified vardenafil-analogues that potently inhibit cGMP efflux-pumps from cytosol and could have substantial clinical potential in treatment of patients with diverse disorders.

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.

Synthesis, Antitumor Activity, and Docking Analysis of New Pyrido[3',2':4,5]furo(thieno)[3,2-d]pyrimidin-8-amines

Continuing our research in the field of new heterocyclic compounds, herein we report on the synthesis and antitumor activity of new amino derivatives of pyrido[3',2':4,5](furo)thieno[3,2-d]pyrimidines as well as of two new heterocyclic systems: furo[2-e]imidazo[1,2-c]pyrimidine and furo[2,3-e]pyrimido[1,2-c]pyrimidine. Thus, by refluxing the 8-chloro derivatives of pyrido[3',2':4,5]thieno(furo)[3,2-d]pyrimidines with various amines, the relevant pyrido[3',2':4,5]thieno(furo)[3,2-d]pyrimidin-8-amines were obtained. Further, the cyclization of some amines under the action of phosphorus oxychloride led to the formation of new heterorings: imidazo[1,2-c]pyrimidine and pyrimido[1,2-c]pyrimidine. The possible antitumor activity of the newly synthesized compounds was evaluated in vitro. The biological tests evidenced that some of them showed pronounced antitumor activity. A study of the structure-activity relationships revealed that the compound activity depended mostly on the nature of the amine fragments. A docking analysis was also performed for the most active compounds.

Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3

In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic.

Eurecon: Equidistant uniform rigid-body ensemble constructor

Conformational ensembles comprise one of the fundamental concepts in statistical bioinformatics and appear in a variety of applications, e.g. molecular docking, virtual screening, searching for pharmacophores, etc. High-throughput applications require billions of conformations to be considered, thus, one often uses the rigid-body representation of molecules or its fragments to cope with the computational cost. Of particular interest is generation of the near-native conformational ensembles, which consist of conformations structurally close to the biologically relevant ones. One possible way to compose such ensembles is to control the root mean square deviation (RMSD) between the original and the generated conformations. To the best of our knowledge there is no computational approach that guarantees that all the generated conformations have the desired RMSD with respect to the reference structure. In this study we presented a fast algorithm for the construction of rigid-body conformational ensembles, which possess two main properties: (i) each generated conformation has a fixed RMSD with respect to the original conformation, (ii) generated conformations are distributed uniformly over the sphere of axes corresponding to the rigid-body motions. The algorithm is very efficient, it does not require any standard RMSD computation between the conformations and has the O(N + M) complexity to generate the required rigid-body transforms, where N is the number of atoms in the system, and M is the size of the conformational ensemble. Eurecon is applicable to an arbitrary atomic system, thus, it could be used for molecular systems of various size and type. We demonstrated Eurecon application by generating near-native conformational ensembles for a ligand placed inside a binding site, a protein dimer embedded into a membrane, and a ribosomal complex. We implemented the developed algorithm in C++ and called it Eurecon, which stands for Equidistant Uniform Rigid-body Ensemble CONstructor. A user-friendly interface allows to define the desired RMSD value, the relative amplitudes for rotation and translation motions by means of the partition parameter, and the set of axes corresponding to the rigid-body motions. Eurecon is available as the SAMSON Element (https://samson-connect.net).

Novel K-Ras G12C Switch-II Covalent Binders Destabilize Ras and Accelerate Nucleotide Exchange

The success of targeted covalent inhibitors in the global pharmaceutical industry has led to a resurgence of covalent drug discovery. However, covalent inhibitor design for flexible binding sites remains a difficult task due to a lack of methodological development. Here, we compared covalent docking to empirical electrophile screening against the highly dynamic target K-Ras^G12C. While the overall hit rate of both methods was comparable, we were able to rapidly progress a docking hit to a potent irreversible covalent binder that modifies the inactive, GDP-bound state of K-Ras^G12C. Hydrogen-deuterium exchange mass spectrometry was used to probe the protein dynamics of compound binding to the switch-II pocket and subsequent destabilization of the nucleotide-binding region. SOS-mediated nucleotide exchange assays showed that, contrary to prior switch-II pocket inhibitors, these new compounds appear to accelerate nucleotide exchange. This study highlights the efficiency of covalent docking as a tool for the discovery of chemically novel hits against challenging targets.

The anthelmintic praziquantel is a human serotoninergic G-protein-coupled receptor ligand

Schistosomiasis is a debilitating tropical disease caused by infection with parasitic blood flukes. Approximately 260 million people are infected worldwide, underscoring the clinical and socioeconomic impact of this chronic infection. Schistosomiasis is treated with the drug praziquantel (PZQ), which has proved the therapeutic mainstay for over three decades of clinical use. However, the molecular target(s) of PZQ remain undefined. Here we identify a molecular target for the antischistosomal eutomer — (R)-PZQ — which functions as a partial agonist of the human serotoninergic 5HT_2B receptor. (R)-PZQ modulation of serotoninergic signaling occurs over a concentration range sufficient to regulate vascular tone of the mesenteric blood vessels where the adult parasites reside within their host. These data establish (R)-PZQ as a G-protein-coupled receptor ligand and suggest that the efficacy of this clinically important anthelmintic is supported by a broad, cross species polypharmacology with PZQ modulating signaling events in both host and parasite.

Schistosomiasis is caused by infection with the flatworm Schistosoma, and praziquantel is the drug of choice for its treatment. Here, Chan and colleagues identify praziquantel as a ligand for the human serotoninergic 5-HT_2B G-protein-coupled receptor, and reveal a function for praziquantel as a regulator of vascular tone in treated hosts.

Receptor structure-based discovery of non-metabolite agonists for the succinate receptor GPR91

Objective

Besides functioning as an intracellular metabolite, succinate acts as a stress-induced extracellular signal through activation of GPR91 (SUCNR1) for which we lack suitable pharmacological tools.

Methods and results

Here we first determined that the cis conformation of the succinate backbone is preferred and that certain backbone modifications are allowed for GPR91 activation. Through receptor modeling over the X-ray structure of the closely related P2Y1 receptor, we discovered that the binding pocket is partly occupied by a segment of an extracellular loop and that succinate therefore binds in a very different mode than generally believed. Importantly, an empty side-pocket is identified next to the succinate binding site. All this information formed the basis for a substructure-based search query, which, combined with molecular docking, was used in virtual screening of the ZINC database to pick two serial mini-libraries of a total of only 245 compounds from which sub-micromolar, selective GPR91 agonists of unique structures were identified. The best compounds were backbone-modified succinate analogs in which an amide-linked hydrophobic moiety docked into the side-pocket next to succinate as shown by both loss- and gain-of-function mutagenesis. These compounds displayed GPR91-dependent activity in altering cytokine expression in human M2 macrophages similar to succinate, and importantly were devoid of any effect on the major intracellular target, succinate dehydrogenase.

Conclusions

These novel, synthetic non-metabolite GPR91 agonists will be valuable both as pharmacological tools to delineate the GPR91-mediated functions of succinate and as leads for the development of GPR91-targeted drugs to potentially treat low grade metabolic inflammation and diabetic complications such as retinopathy and nephropathy.

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The GPR91 binding site for succinate is identified with an adjacent empty pocket.

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The binding pocket structure is used to identify novel synthetic GPR91 agonists.

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The non-metabolite GPR91 ligands can be used as pharmacological tools and drug leads.

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Novel compounds demonstrate GPR91 control of cytokine expression in M2 macrophages.

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.

Structure-Based Discovery of New Antagonist and Biased Agonist Chemotypes for the Kappa Opioid Receptor

The ongoing epidemics of opioid overdose raises an urgent need for effective antiaddiction therapies and addiction-free painkillers. The κ-opioid receptor (KOR) has emerged as a promising target for both indications, raising demand for new chemotypes of KOR antagonists as well as G-protein-biased agonists. We employed the crystal structure of the KOR-JDTic complex and ligand-optimized structural templates to perform virtual screening of available compound libraries for new KOR ligands. The prospective virtual screening campaign yielded a high 32% hit rate, identifying novel fragment-like and lead-like chemotypes of KOR ligands. A round of optimization resulted in 11 new submicromolar KOR binders (best K_i = 90 nM). Functional assessment confirmed at least two compounds as potent KOR antagonists, while compound 81 was identified as a potent G_i biased agonist for KOR with minimal β-arrestin recruitment. These results support virtual screening as an effective tool for discovery of new lead chemotypes with therapeutically relevant functional profiles.

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

Recent Advancements in Docking Methodologies

Nowadays molecular docking has become an important methodology in CADD (Computer-Aided Drug Design)-assisted drug discovery process. It is an important computational tool widely used to predict binding mode, binding affinity and binding free energy of a protein-ligand complex. The important factors responsible for accurate results in docking studies are correct binding site prediction, use of suitable small-molecule databases, consistent docking pose, high dock score with good MD (Molecular Dynamics), clarity whether the compound is an inhibitor or agonist, etc. However, still there are several limitations which make it difficult to obtain accurate results from docking studies. In this chapter, the main focus is on recent advancements in various aspects of molecular docking such as ligand sampling, protein flexibility, scoring functions, fragment docking, post-processing, docking into homology models and protein-protein docking.

Smart, Innovative and Intelligent Technologies Used in Drug Designing

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2^N). A recursive approximation to the optimal solution scales as O(N²), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Establishment of correlation between in-silico and in-vitro test analysis against Leishmania HGPRT to inhibitors

Hypoxanthine Phosphoribosyltransferase (HGPRT; EC 2.4.2.8) is a central enzyme in the purine recycling pathway of all protozoan parasites. Protozoan parasites cannot synthesize purine bases (DNA/RNA) which is essential for survival as lack of de-novo pathway. Thus its good target for drug design and discovery as inhibition leads to cessation of replication. PRTase (transferase enzyme) has common PRTase type I folding pattern domain for its activities. Genomic studies revealed the sequence pattern and identified highly conserved residues that catalyzed the reaction in protozoan parasites. A recombinant protein has 24 kDa molecular mass (rLdHGPRT) was cloned, expressed and purified for testing of guanosine monophosphate (GMP) analogous compounds in-vitro by spectroscopically to the rLdHGPRT, lysates protein and MTT assay on Leishmania donovani. The predicted inhibitors of different libraries were screen into FlexX. The reported inhibitors were tested in-vitro. The 2'-deoxyguanosine 5'-diphosphate (DGD) (IC50 value 12.5 μM) is two times more effective when compared to guanosine-5'-diphosphate sodium (GD). Interestingly, LdHGPRT complex has shown stable after 24 ns in molecular dynamics simulation with interacting amino acids are Glu125, Ile127, Lys87 and Val186. QSAR studies revealed the correlation between predicted and experimental values has shown R2 0.998. Concludes that inversely proportional to their docked score with activities.

In Silico Investigation of the Neurotensin Receptor 1 Binding Site: Overlapping Binding Modes for Small Molecule Antagonists and the Endogenous Peptide Agonist

The neurotensin receptor 1 (NTSR1) belongs to the family of 7TM, G protein-coupled receptors, and is activated by the 13-amino-acid peptide neurotensin (NTS) that has been shown to play important roles in neurological disorders and the promotion of cancer cells. Recently, a high-resolution x-ray crystal structure of NTSR1 in complex with NTS8-13 has been determined, providing novel insights into peptide ligand recognition by 7TM receptors. SR48692, a potent and selective small molecule antagonist has previously been used extensively as a tool compound to study NTSR1 receptor signaling properties. To investigate the binding mode of SR48692 and other small molecule compounds to NTSR1, we applied an Automated Ligand-guided Backbone Ensemble Receptor Optimization protocol (ALiBERO), taking receptor flexibility and ligand knowledge into account. Structurally overlapping binding poses for SR48692 and NTS8-13 were observed, despite their distinct chemical nature and inverse pharmacological profiles. The optimized models showed significantly improved ligand recognition in a large-scale virtual screening assessment compared to the crystal structure. Our models provide new insights into small molecule ligand binding to NTSR1 and could facilitate the structure-based design of non-peptide ligands for the evaluation of the pharmacological potential of NTSR1 in neurological disorders and cancer.

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The 2013/2014 Community Structure-Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor-small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R(2) = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.