A model binding site for testing scoring functions in molecular docking

The inhibitory effects of free and bound phenolics from Phyllanthus emblica Linn. on α-amylase: a comparison study

BACKGROUND

Phyllanthus emblica Linn. (PE) is rich in polyphenols, which can be categorized into free and bound phenolics (PEFP and PEBP). This study evaluated the inhibitory effect of PEFB and PEBP on α-amylase for the first time. The mechanism of the inhibition effect of PEFP and PEBP on α-amylase was investigated by enzyme inhibition kinetics, multispectral analysis, thermodynamics, and molecular docking.

RESULTS

Free and bound phenolics inhibited α-amylase activity effectively in a mixed type of inhibition. Fluorescence quenching and thermodynamic analyses showed that the binding of PEFP and PEBP to α-amylase occurred through a static quenching process (Kq = 6.94 × 10¹² and 5.74 × 10¹² L mol-1 s-1), which was accompanied by a redshift (λem from 343 to 347 nm), leading to a change in the microenvironment. This process was found to be a spontaneous exothermic reaction (ΔG < 0). Circular dichroism (CD) analysis confirms that the secondary structure of α-amylase was altered, in particular a decrease in α-helixes and an increase in random coils. Molecular docking studies showed that PEFP and PEBP interacted with α-amylase through hydrogen bonding and hydrophobic interactions.

CONCLUSION

The present study provides valuable insights into the mechanism of action of PEFP and PEBP on α-amylase, which will provide a theoretical basis for their possible use as novel natural α-amylase inhibitors. © 2024 Society of Chemical Industry.

Structure-based discovery of CFTR potentiators and inhibitors

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

Structure-based discovery of CFTR potentiators and inhibitors

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, while its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify novel CFTR modulators. We docked ~155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered novel mid-nanomolar potentiators as well as inhibitors that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

Nuclear-Electronic Orbital Quantum Mechanical/Molecular Mechanical Real-Time Dynamics

Simulating the nuclear-electronic quantum dynamics of large-scale molecular systems in the condensed phase is key for studying biologically and chemically important processes such as proton transfer and proton-coupled electron transfer reactions. Herein, the real-time nuclear-electronic orbital time-dependent density functional theory (RT-NEO-TDDFT) approach is combined with a hybrid quantum mechanical/molecular mechanical (QM/MM) strategy to enable the accurate description of coupled nuclear-electronic quantum dynamics in the presence of heterogeneous environments such as solvent or proteins. The densities of the electrons and quantum protons are propagated in real time, while the other nuclei are propagated classically on the instantaneous electron-proton vibronic surface. This approach is applied to phenol bound to lysozyme, intramolecular proton transfer in malonaldehyde, and nonequilibrium excited-state intramolecular proton transfer in o-hydroxybenzaldehyde. These examples illustrate that the RT-NEO-TDDFT framework, coupled with an atomistic representation of the environment, allows the simulation of condensed-phase systems that exhibit significant nuclear quantum effects.

Identification of Potential Aldose Reductase Inhibitors Using Convolutional Neural Network-Based in Silico Screening

Aldose reductase (ALR2) is a notable enzyme of the polyol pathway responsible for aggravating diabetic neuropathy complications. The first step begins when it catalyzes the reduction of glucose to sorbitol with NADPH as a coenzyme. Elevated concentrations of sorbitol damage the tissues, leading to complications like neuropathy. Though considerable effort has been pushed toward the successful discovery of potent inhibitors, its discovery still remains an elusive task. To this end, we present a 3D convolutional neural network (3D-CNN) based ALR2 inhibitor classification technique by dealing with snapshots of images captured from 3D chemical structures with multiple rotations as input data. The CNN-based architecture was trained on the 360 sets of image data along each axis and further prediction on the Maybridge library by each of the models. Subjecting the retrieved hits to molecular docking leads to the identification of the top 10 molecules with high binding affinity. The hits displayed a better blood-brain barrier penetration (BBB) score (90% with more than four scores) as compared to standard inhibitors (38%), reflecting the superior BBB penetrating efficiency of the hits. Followed by molecular docking, the biological evaluation spotlighted five compounds as promising ALR2 inhibitors and can be considered as a likely prospect for further structural optimization with medicinal chemistry efforts to improve their inhibition efficacy and consolidate them as new ALR2 antagonists in the future. In addition, the study also demonstrated the usefulness of scaffold analysis of the molecules as a method for investigating the significance of structurally diverse compounds in data-driven studies. For reproducibility and accessibility purposes, all of the source codes used in our study are publicly available.

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

Structure-based discovery of novel cruzain inhibitors with distinct trypanocidal activity profiles

Over 110 years after the first formal description of Chagas disease, the trypanocidal drugs thus far available have limited efficacy and several side effects. This encourages the search for novel treatments that inhibit T. cruzi targets. One of the most studied anti-T. cruzi targets is the cysteine protease cruzain; it is associated with metacyclogenesis, replication, and invasion of the host cells. We used computational techniques to identify novel molecular scaffolds that act as cruzain inhibitors. First, with a docking-based virtual screening, we identified compound 8, a competitive cruzain inhibitor with a K_i of 4.6 μM. Then, aided by molecular dynamics simulations, cheminformatics, and docking, we identified the analog compound 22 with a K_i of 27 μM. Surprisingly, despite sharing the same isoquinoline scaffold, compound 8 presented higher trypanocidal activity against the epimastigote forms, while compound 22, against the trypomastigotes and amastigotes. Taken together, compounds 8 and 22 represent a promising scaffold for further development of trypanocidal compounds as drug candidates for treating Chagas disease.

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has unusual anti-depressant and substance-withdrawal effects, and stabilizes the inward-open conformation. Unfortunately, ibogaine's promiscuity and cardiotoxicity limit the understanding of inward-open state ligands. We docked over 200 million small molecules against the inward-open state of the SERT. Thirty-six top-ranking compounds were synthesized, and thirteen inhibited; further structure-based optimization led to the selection of two potent (low nanomolar) inhibitors. These stabilized an outward-closed state of the SERT with little activity against common off-targets. A cryo-EM structure of one of these bound to the SERT confirmed the predicted geometry. In mouse behavioral assays, both compounds had anxiolytic- and anti-depressant-like activity, with potencies up to 200-fold better than fluoxetine (Prozac), and one substantially reversed morphine withdrawal effects.

Ligand binding free energy evaluation by Monte Carlo Recursion

The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

In Silico Targeting of Fascin Protein for Cancer Therapy: Benchmarking, Virtual Screening and Molecular Dynamics Approaches

Fascin is an actin-bundling protein overexpressed in various invasive metastatic carcinomas through promoting cell migration and invasion. Therefore, blocking Fascin binding sites is considered a vital target for antimetastatic drugs. This inspired us to find new Fascin binding site blockers. First, we built an active compound set by collecting reported small molecules binding to Fascin's binding site 2. Consequently, a high-quality decoys set was generated employing DEKOIS 2.0 protocol to be applied in conducting the benchmarking analysis against the selected Fascin structures. Four docking programs, MOE, AutoDock Vina, VinaXB, and PLANTS were evaluated in the benchmarking study. All tools indicated better-than-random performance reflected by their pROC-AUC values against the Fascin crystal structure (PDB: ID 6I18). Interestingly, PLANTS exhibited the best screening performance and recognized potent actives at early enrichment. Accordingly, PLANTS was utilized in the prospective virtual screening effort for repurposing FDA-approved drugs (DrugBank database) and natural products (NANPDB). Further assessment via molecular dynamics simulations for 100 ns endorsed Remdesivir (DrugBank) and NANPDB3 (NANPDB) as potential binders to Fascin binding site 2. In conclusion, this study delivers a model for implementing a customized DEKOIS 2.0 benchmark set to enhance the VS success rate against new potential targets for cancer therapies.

Hierarchical analysis of the target-based scoring function modification for the example of selected class A GPCRs

Computational methods, especially molecular docking-based calculations, have become indispensable in the modern drug discovery workflow. The constantly increasing chemical space requires fast, robust but most of all highly predictive methods to search for new bioactive agents. Thus, the scoring function (SF) is a useful and broadly applied energy-based element of docking software, allowing quick and effective evaluation of a ligand's propensity to bind to selected protein targets. Despite many spectacular successes of molecular docking applications in virtual screening (VS), the obtained results are often far from ideal, leading to incorrect selection of hit molecules and poor pose prediction. In our study we focused on docking calculation for the selected class A G-protein coupled receptors (GPCRs), with experimentally determined 3D structures and a sufficient set of known ligands with affinity values reported in the ChEMBL database. Our goal is to investigate how much the energy-based scoring function for this particular target class changes when changing from the default to the re-estimated weighting scheme on the specified energy terms in the SF definition. Additionally, we want to verify if indeed more accurate results are obtained when considering different levels of the biological hierarchy, namely: the whole class A GPCRs, sub-subfamilies, or just the individual proteins while applying default or specifically designed weighting coefficients. The performed calculation and evaluation factor values suggest a significant improvement of docking results for the designed SF definition. This individual approach improves the accuracy of binding affinity prediction and active compound recognition. The designed scoring function for classes, sub-subfamilies, or proteins leads to a significant improvement of molecular docking performance, especially at the level of individual proteins. Our results show that to increase the efficiency and predictive power of molecular docking calculations applied in classical VS, the strategy based on the individual approach for scoring function definition for selected proteins should be considered.

Systematic Evaluation of Counterpoise Correction in Density Functional Theory

A widespread belief persists that the Boys-Bernardi function counterpoise (CP) procedure "overcorrects" supramolecular interaction energies for the effects of basis-set superposition error. To the extent that this is true for correlated wave function methods, it is usually an artifact of low-quality basis sets. The question has not been considered systematically in the context of density functional theory, however, where basis-set convergence is generally less problematic. We present a systematic assessment of the CP procedure for a representative set of functionals and basis sets, considering both benchmark data sets of small dimers and larger supramolecular complexes. The latter include layered composite polymers with ∼150 atoms and ligand-protein models with ∼300 atoms. Provided that CP correction is used, we find that intermolecular interaction energies of nearly complete-basis quality can be obtained using only double-ζ basis sets. This is less expensive as compared to triple-ζ basis sets without CP correction. CP-corrected interaction energies are less sensitive to the presence of diffuse basis functions as compared to uncorrected energies, which is important because diffuse functions are expensive and often numerically problematic for large systems. Our results upend the conventional wisdom that CP "overcorrects" for basis-set incompleteness. In small basis sets, CP correction is mandatory in order to demonstrate that the results do not rest on error cancellation.

Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity

There is considerable interest in screening ultralarge chemical libraries for ligand discovery, both empirically and computationally1-4. Efforts have focused on readily synthesizable molecules, inevitably leaving many chemotypes unexplored. Here we investigate structure-based docking of a bespoke virtual library of tetrahydropyridines-a scaffold that is poorly sampled by a general billion-molecule virtual library but is well suited to many aminergic G-protein-coupled receptors. Using three inputs, each with diverse available derivatives, a one pot C-H alkenylation, electrocyclization and reduction provides the tetrahydropyridine core with up to six sites of derivatization5-7. Docking a virtual library of 75 million tetrahydropyridines against a model of the serotonin 5-HT2A receptor (5-HT2AR) led to the synthesis and testing of 17 initial molecules. Four of these molecules had low-micromolar activities against either the 5-HT2A or the 5-HT2B receptors. Structure-based optimization led to the 5-HT2AR agonists (R)-69 and (R)-70, with half-maximal effective concentration values of 41 nM and 110 nM, respectively, and unusual signalling kinetics that differ from psychedelic 5-HT2AR agonists. Cryo-electron microscopy structural analysis confirmed the predicted binding mode to 5-HT2AR. The favourable physical properties of these new agonists conferred high brain permeability, enabling mouse behavioural assays. Notably, neither had psychedelic activity, in contrast to classic 5-HT2AR agonists, whereas both had potent antidepressant activity in mouse models and had the same efficacy as antidepressants such as fluoxetine at as low as 1/40th of the dose. Prospects for using bespoke virtual libraries to sample pharmacologically relevant chemical space will be considered.

Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo

The sampling problem is one of the most widely studied topics in computational chemistry. While various methods exist for sampling along a set of reaction coordinates, many require system-dependent hyperparameters to achieve maximum efficiency. In this work, we present an alchemical variation of adaptive sequential Monte Carlo (SMC), an irreversible importance resampling method that is part of a well-studied class of methods that have been used in various applications but have been underexplored in computational biophysics. Afterward, we apply alchemical SMC on a variety of test cases, including torsional rotations of solvated ligands (butene and a terphenyl derivative), translational and rotational movements of protein-bound ligands, and protein side chain rotation coupled to the ligand degrees of freedom (T4-lysozyme, protein tyrosine phosphatase 1B, and transforming growth factor β). We find that alchemical SMC is an efficient way to explore targeted degrees of freedom and can be applied to a variety of systems using the same hyperparameters to achieve a similar performance. Alchemical SMC is a promising tool for preparatory exploration of systems where long-timescale sampling of the entire system can be traded off against short-timescale sampling of a particular set of degrees of freedom over a population of conformers.

Structure-Based Design of a Chemical Probe Set for the 5-HT5A Serotonin Receptor

The 5-HT_5A receptor (5-HT_5AR), for which no selective agonists and a few antagonists exist, remains the least understood serotonin receptor. A single commercial antagonist, SB-699551, has been widely used to investigate the 5-HT_5AR function in neurological disorders, including pain, but this molecule has substantial liabilities as a chemical probe. Accordingly, we sought to develop an internally controlled probe set. Docking over 6 million molecules against a 5-HT_5AR homology model identified 5 mid-μM ligands, one of which was optimized to UCSF678, a 42 nM arrestin-biased partial agonist at the 5-HT_5AR with a more restricted off-target profile and decreased assay liabilities versus SB-699551. Site-directed mutagenesis supported the docked pose of UCSF678. Surprisingly, analogs of UCSF678 that lost the 5-HT_5AR activity revealed that 5-HT_5AR engagement is nonessential for alleviating pain, contrary to studies with less-selective ligands. UCSF678 and analogs constitute a selective probe set with which to study the function of the 5-HT_5AR.

In silico design and in vitro assessment of anti-Helicobacter pylori compounds as potential small-molecule arginase inhibitors

Related to a variety of gastrointestinal disorders ranging from gastric ulcer to gastric adenocarcinoma, the infection caused by the gram-negative bacteria Helicobacter pylori (H. pylori) poses as a great threat to human health; hence, the search for new treatments is a global priority. The H. pylori arginase (HPA) protein has been widely studied as one of the main virulence factors of this bacterium, being involved in the prevention of nitric oxide-mediated bacterial cell death, which is a central component of innate immunity. Given the growing need for the development of new drugs capable of combating the infection by H. pylori, the present work describes the search for new HPA inhibitors, using virtual screening techniques based on molecular docking followed by the evaluation of the proposed modes of interaction at the HPA active site. In vitro studies of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), followed by cytotoxicity activity in gastric adenocarcinoma and non-cancer cells, were performed. The results highlighted compounds 6, 11, and 13 as potential inhibitors of HPA; within these compounds, the results indicated 13 presented an improved activity toward H. pylori killing, with MIC and MBC both at 64 µg/mL. Moreover, compound 13 also presented a selectivity index of 8.3, thus being more selective for gastric adenocarcinoma cells compared to the commercial drug cisplatin. Overall, the present work demonstrates the search strategy based on in silico and in vitro techniques is able to support the rational design of new anti-H. pylori drugs.

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.

Energy penalties enhance flexible receptor docking in a model cavity

Protein flexibility remains a major challenge in library docking because of difficulties in sampling conformational ensembles with accurate probabilities. Here, we use the model cavity site of T4 lysozyme L99A to test flexible receptor docking with energy penalties from molecular dynamics (MD) simulations. Crystallography with larger and smaller ligands indicates that this cavity can adopt three major conformations: open, intermediate, and closed. Since smaller ligands typically bind better to the cavity site, we anticipate an energy penalty for the cavity opening. To estimate its magnitude, we calculate conformational preferences from MD simulations. We find that including a penalty term is essential for retrospective ligand enrichment; otherwise, high-energy states dominate the docking. We then prospectively docked a library of over 900,000 compounds for new molecules binding to each conformational state. Absent a penalty term, the open conformation dominated the docking results; inclusion of this term led to a balanced sampling of ligands against each state. High ranked molecules were experimentally tested by Tm upshift and X-ray crystallography. From 33 selected molecules, we identified 18 ligands and determined 13 crystal structures. Most interesting were those bound to the open cavity, where the buried site opens to bulk solvent. Here, highly unusual ligands for this cavity had been predicted, including large ligands with polar tails; these were confirmed both by binding and by crystallography. In docking, incorporating protein flexibility with thermodynamic weightings may thus access new ligand chemotypes. The MD approach to accessing and, crucially, weighting such alternative states may find general applicability.

An Experimental Toolbox for Structure-Based Hit Discovery for P. aeruginosa FabF, a Promising Target for Antibiotics

FabF (3-oxoacyl-[acyl-carrier-protein] synthase 2), which catalyses the rate limiting condensation reaction in the fatty acid synthesis II pathway, is an attractive target for new antibiotics. Here, we focus on FabF from P. aeruginosa (PaFabF) as antibiotics against this pathogen are urgently needed. To facilitate exploration of this target we have set up an experimental toolbox consisting of binding assays using bio-layer interferometry (BLI) as well as saturation transfer difference (STD) and WaterLOGSY NMR in addition to robust conditions for structure determination. The suitability of the toolbox to support structure-based design of FabF inhibitors was demonstrated through the validation of hits obtained from virtual screening. Screening a library of almost 5 million compounds resulted in 6 compounds for which binding into the malonyl-binding site of FabF was shown. For one of the hits, the crystal structure in complex with PaFabF was determined. Based on the obtained binding mode, analogues were designed and synthesised, but affinity could not be improved. This work has laid the foundation for structure-based exploration of PaFabF.

A large-scale computational screen identifies strong potential inhibitors for disrupting SARS-CoV-2 S-protein and human ACE2 interaction

SARS-CoV-2 has infected millions of individuals across the globe and has killed over 2.7 million people. Even though vaccines against this virus have recently been introduced, the antibody generated in the process has been reported to decline quickly. This can reduce the efficacy of vaccines over time and can result in re-infections. Thus, drugs that are effective against COVID-19 can provide a second line of defence and can prevent occurrence of the severe form of the disease. The interaction between SARS-CoV2 S-protein and human ACE2 (hACE2) is essential for the infection of the virus. Thus, drugs that block this interaction could potentially inhibit SARS-CoV-2 infection into the host cells. To identify such drugs, we first analyzed the recently published crystal structure of S-protein-hACE2 complex and identified essential residues of both S-protein and hACE2 for this interaction. We used this knowledge to virtually dock a drug library containing 4115 drug molecules against S-protein for repurposing drugs that could inhibit binding of S-protein to hACE2. We identified several potential inhibitors based on their docking scores, pharmacological effects and ability to block residues of S protein required for interaction with hACE2. The top inhibitors included drugs used for the treatment of hepatitis C (velpatasvir, pibrentasvir) as well as several vitamin D derivatives. Several molecules obtained from our screen already have good experimental support in published literature. Thus, we believe that our results will facilitate the discovery of an effective drug against COVID-19. Communicated by Ramaswamy H. Sarma.

Facilitating SARS CoV-2 RNA-Dependent RNA polymerase (RdRp) drug discovery by the aid of HCV NS5B palm subdomain binders: In silico approaches and benchmarking

Corona Virus 2019 Disease (COVID-19) is a rapidly emerging pandemic caused by a newly discovered beta coronavirus, called Sever Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2). SARS CoV-2 is an enveloped, single stranded RNA virus that depends on RNA-dependent RNA polymerase (RdRp) to replicate. Therefore, SARS CoV-2 RdRp is considered as a promising target to cease virus replication. SARS CoV-2 polymerase shows high structural similarity to Hepatitis C Virus-1b genotype (HCV-1b) polymerase.

Arising from the high similarity between SARS CoV-2 RdRp and HCV NS5B, we utilized the reported small-molecule binders to the palm subdomain of HCV NS5B (genotype 1b) to generate a high-quality DEKOIS 2.0 benchmark set and conducted a benchmarking analysis against HCV NS5B. The three highly cited and publicly available docking tools AutoDock Vina, FRED and PLANTS were benchmarked. Based on the benchmarking results and analysis via pROC-Chemotype plot, PLANTS showed the best screening performance and can recognize potent binders at the early enrichment. Accordingly, we used PLANTS in a prospective virtual screening to repurpose both the FDA-approved drugs (DrugBank) and the HCV-NS5B palm subdomain binders (BindingDB) for SARS CoV-2 RdRp palm subdomain. Further assessment by molecular dynamics simulations for 50 ns recommended diosmin (from DrugBank) and compound 3 (from BindingDB) to be the best potential binders to SARS CoV-2 RdRp palm subdomain. The best predicted compounds are recommended to be biologically investigated against COVID-19. In conclusion, this work provides in-silico analysis to propose possible SARS CoV-2 RdRp palm subdomain binders recommended as a remedy for COVID-19. Up-to-our knowledge, this study is the first to propose binders at the palm subdomain of SARS CoV2 RdRp. Furthermore, this study delivers an example of how to make use of a high quality custom-made DEKOIS 2.0 benchmark set as a procedure to elevate the virtual screening success rate against a vital target of the rapidly emerging pandemic.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional

Quantum mechanical binding free energies based on thousands of full-protein DFT calculations are tractable, reproducible and converge well.

Supporting SARS-CoV-2 Papain-Like Protease Drug Discovery: In silico Methods and Benchmarking

The coronavirus disease 19 (COVID-19) is a rapidly growing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its papain-like protease (SARS-CoV-2 PLpro) is a crucial target to halt virus replication. SARS-CoV PLpro and SARS-CoV-2 PLpro share an 82.9% sequence identity and a 100% sequence identity for the binding site reported to accommodate small molecules in SARS-CoV. The flexible key binding site residues Tyr269 and Gln270 for small-molecule recognition in SARS-CoV PLpro exist also in SARS-CoV-2 PLpro. This inspired us to use the reported small-molecule binders to SARS-CoV PLpro to generate a high-quality DEKOIS 2.0 benchmark set. Accordingly, we used them in a cross-benchmarking study against SARS-CoV-2 PLpro. As there is no SARS-CoV-2 PLpro structure complexed with a small-molecule ligand publicly available at the time of manuscript submission, we built a homology model based on the ligand-bound SARS-CoV structure for benchmarking and docking purposes. Three publicly available docking tools FRED, AutoDock Vina, and PLANTS were benchmarked. All showed better-than-random performances, with FRED performing best against the built model. Detailed performance analysis via pROC-Chemotype plots showed a strong enrichment of the most potent bioactives in the early docking ranks. Cross-benchmarking against the X-ray structure complexed with a peptide-like inhibitor confirmed that FRED is the best-performing tool. Furthermore, we performed cross-benchmarking against the newly introduced X-ray structure complexed with a small-molecule ligand. Interestingly, its benchmarking profile and chemotype enrichment were comparable to the built model. Accordingly, we used FRED in a prospective virtual screen of the DrugBank database. In conclusion, this study provides an example of how to harness a custom-made DEKOIS 2.0 benchmark set as an approach to enhance the virtual screening success rate against a vital target of the rapidly emerging pandemic.

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Computer-aided design of protein-ligand binding is important for the development of novel drugs. Here authors present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand binding interactions of small drug-like molecules.

Identification of entacapone as a chemical inhibitor of FTO mediating metabolic regulation through FOXO1

Recent studies have established the involvement of the fat mass and obesity-associated gene (FTO) in metabolic disorders such as obesity and diabetes. However, the precise molecular mechanism by which FTO regulates metabolism remains unknown. Here, we used a structure-based virtual screening of U.S. Food and Drug Administration-approved drugs to identify entacapone as a potential FTO inhibitor. Using structural and biochemical studies, we showed that entacapone directly bound to FTO and inhibited FTO activity in vitro. Furthermore, entacapone administration reduced body weight and lowered fasting blood glucose concentrations in diet-induced obese mice. We identified the transcription factor forkhead box protein O1 (FOXO1) mRNA as a direct substrate of FTO, and demonstrated that entacapone elicited its effects on gluconeogenesis in the liver and thermogenesis in adipose tissues in mice by acting on an FTO-FOXO1 regulatory axis.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Targeting the Class A Carbapenemase GES-5 via Virtual Screening

The worldwide spread of β-lactamases able to hydrolyze last resort carbapenems contributes to the antibiotic resistance problem and menaces the successful antimicrobial treatment of clinically relevant pathogens. Class A carbapenemases include members of the KPC and GES families. While drugs against KPC-type carbapenemases have recently been approved, for GES-type enzymes, no inhibitors have yet been introduced in therapy. Thus, GES carbapenemases represent important drug targets. Here, we present an in silico screening against the most prevalent GES carbapenemase, GES-5, using a lead-like compound library of commercially available compounds. The most promising candidates were selected for in vitro validation in biochemical assays against recombinant GES-5 leading to four derivatives active as high micromolar competitive inhibitors. For the best inhibitors, the ability to inhibit KPC-2 was also evaluated. The discovered inhibitors constitute promising starting points for hit to lead optimization.

The nuclear Overhauser Effect (NOE) as a tool to study macromolecular confinement: Elucidation and disentangling of crowding and encapsulation effects

We propose a methodology to capture short-lived but biophysically important contacts of biomacromolecules using the biomolecule-water nuclear Overhauser effect as an indirect microscope. Thus, instead of probing the direct correlation with the foreign biomolecule, we detect its presence by the disturbance it causes in the surrounding water. In addition, this information obtained is spatially resolved and can thus be attributed to specific sites. We extend this approach to the influence of more than one change in chemical environment and show a methodological way of resolution. This is achieved by taking double differences of corresponding σ_NOE/σ_ROE ratios of the systems studied and separating specific, unspecific, and intermediate influence. While applied to crowding and encapsulation in this study, this method is generally suitable for any combination of changes in chemical environment.

Absolute Protein Binding Free Energy Simulations for Ligands with Multiple Poses, a Thermodynamic Path That Avoids Exhaustive Enumeration of the Poses

We propose a free energy calculation method for receptor-ligand binding, which have multiple binding poses that avoids exhaustive enumeration of the poses. For systems with multiple binding poses, the standard procedure is to enumerate orientations of the binding poses, restrain the ligand to each orientation, and then, calculate the binding free energies for each binding pose. In this study, we modify a part of the thermodynamic cycle in order to sample a broader conformational space of the ligand in the binding site. This modification leads to more accurate free energy calculation without performing separate free energy simulations for each binding pose. We applied our modification to simple model host-guest systems as a test, which have only two binding poses, by using a single decoupling method (SDM) in implicit solvent. The results showed that the binding free energies obtained from our method without knowing the two binding poses were in good agreement with the benchmark results obtained by explicit enumeration of the binding poses. Our method is applicable to other alchemical binding free energy calculation methods such as the double decoupling method (DDM) in explicit solvent. We performed a calculation for a protein-ligand system with explicit solvent using our modified thermodynamic path. The results of the free energy simulation along our modified path were in good agreement with the results of conventional DDM, which requires a separate binding free energy calculation for each of the binding poses of the example of phenol binding to T4 lysozyme in explicit solvent. © 2019 Wiley Periodicals, Inc.

Dielectric spectroscopy and time dependent Stokes shift: two faces of the same coin?

Different types of spectroscopy capture different aspects of dynamics and different ranges of intermolecular contributions.

The protein-water nuclear Overhauser effect (NOE) as an indirect microscope for molecular surface mapping of interaction patterns

In this computational study, the intermolecular solute-solvent Nuclear Overhauser Effect (NOE) of the model protein ubiquitin in different chemical environments (free, bound to a partner protein and encapsulated) is investigated. Short-ranged NOE observables such as the NOE/ROE ratio reveal hydration phenomena on absolute timescales such as fast hydration sites and slow water clefts. We demonstrate the ability of solute-solvent NOE differences measured of the same protein in different chemical environments to reveal hydration changes on the relative timescale. The resulting NOE/ROE-surface maps are shown to be a central key for analyzing biologically relevant chemical influences such as complexation and confinement: the presence of a complexing macromolecule or a confining surface wall modulates the water mobility in the vicinity of the probe protein, hence revealing which residues of said protein are proximate to the foreign interface and which are chemically unaffected. This way, hydration phenomena can serve to indirectly map the precise influence (position) of other molecules or interfaces onto the protein surface. This proposed one-protein many-solvents approach may offer experimental benefits over classical one-protein other-protein pseudo-intermolecular transient NOEs. Furthermore, combined influences such as complexation and confinement may exert non-additive influences on the protein compared to a reference state. We offer a mathematical method to disentangle the influence of these two different chemical environments.

Small-Molecule Intervention At The Dimerization Interface Of Survivin By Novel Rigidized Scaffolds

Introduction

Survivin is a nodal protein involved in several cellular pathways. It is a member of the IAP family and an integral component of the chromosomal passenger complex, where it binds to borealin and INCENP through its dimerization interface. By targeting survivin with a small molecule at its dimerization interface, inhibition of the proliferation of cancer cells has been suggested. With Abbott 8, a small-molecule dimerization inhibitor has been recently reported. The structure–activity relationship of this series of inhibitors implied that the middle pyridin-2(1H)-one ring did not tolerate modifications of any kind.

Methods

Based on the synthetic strategy of Abbott 8 using multicomponent reactions, we synthesized a series of small molecules bearing a novel rigidized core scaffold. This rigidization strategy was accomplished by integrating the pyridin-2(1H)-one and its 6-phenyl substituent into a tricyclic structure, linking position 5 of pyridin-2(1H)-one to the phenyl substituent by rings of different sizes. The new scaffolds were designed based on in silico molecular dynamics of survivin.

Results

Binding of these rigidized scaffolds to the recombinant L54M mutant of survivin was evaluated, revealing affinities in the low micromolar range.

Conclusion

This easily accessible, new class of survivin-dimerization modulators is an interesting starting point for further lead optimization.

Discovery of Novel Pim-1 Kinase Inhibitors with a Flexible-Receptor Docking Protocol

A flexible-receptor docking protocol was designed for treating binding-site side-chain flexibility by integrating essential aspects of "Conformational Selection" and "Induced Fit" in a hierarchical fashion. Assessed in a diverse set of pharmaceutically relevant targets, this protocol showed improved performance in reproducing binding poses and ligand enrichment studies compared to rigid-receptor docking. Moreover, it has also exhibited encouraging efficiency in prospective ligand discovery for Pim-1 kinase, which led to novel Pim-1 inhibitors with single-digit nanomolar potencies.

Exploring the strength of a hydrogen bond as a function of steric environment using 1,2-azaborine ligands and engineered T4 lysozyme receptors

A congeneric series of 1,2-azaborine ligands was used to study the relationship between the steric demand of the ligand and hydrogen bonding strength in the context of ligand-protein binding using engineered T4 lysozymes as the model biomacromolecules. The hydrogen bonding strength values were extracted from experimentally accessible binding free energies using the Double Mutant Cycle analysis. With the increasing steric demand, the NH…102Q hydrogen bonding interaction is weakened; however, this weakening of the hydrogen bonding interaction occurs in discrete steps rather than in an incremental fashion.

Hydration dynamics of proteins in reverse micelles probed by 1H-NOESY/1H-ROESY NMR and 17O-nuclear quadrupole resonance (NQR)

This study is based on extensive MD simulations of a protein in a reverse micelle to mimic the effect of confinement on biomolecules. This permits the calculation of measurable quantities appearing in NQR and Nuclear Overhauser-NMR despite the high computational effort. We address the long-standing debate about the intermolecular NOE showing that absolute quantities derived from NOESY and ROESY spectra do indeed contain considerable long-range contributions, while ratios thereof are effectively short-ranged due to almost perfect compensation effects. Based on NQR relaxation times, we predict strong rotational retardation of interstitial water between the protein and the surfactant surface. The computed NOE to ROE ratio correlates fairly with experimental results. The solvation dynamics mapped onto the protein surface reflects the spatial heterogeneity of a cell-like system with slow water dynamics in proximity to the cell wall and almost bulk-like behaviour in the water core.

Identification of a potential allosteric site of Golgi α-mannosidase II using computer-aided drug design

Golgi α-mannosidase II (GMII) is a glycoside hydrolase playing a crucial role in the N-glycosylation pathway. In various tumour cell lines, the distribution of N-linked sugars on the cell surface is modified and correlates with the progression of tumour metastasis. GMII therefore is a possible molecular target for anticancer agents. Here, we describe the identification of a non-competitive GMII inhibitor using computer-aided drug design methods including identification of a possible allosteric binding site, pharmacophore search and virtual screening.

Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways

The T4 lysozyme L99A mutant is often used as a model system to study small-molecule binding to proteins, but pathways for ligand entry and exit from the buried binding site and the associated protein conformational changes have not been fully resolved. Here, molecular dynamics simulations were employed to model benzene exit from its binding cavity using the weighted ensemble (WE) approach to enhance sampling of low-probability unbinding trajectories. Independent WE simulations revealed four pathways for benzene exit, which correspond to transient tunnels spontaneously formed in previous simulations of apo T4 lysozyme. Thus, benzene unbinding occurs through multiple pathways partially created by intrinsic protein structural fluctuations. Motions of several α-helices and side chains were involved in ligand escape from metastable microstates. WE simulations also provided preliminary estimates of rate constants for each exit pathway. These results complement previous works and provide a semiquantitative characterization of pathway heterogeneity for binding of small molecules to proteins.

Ultra-large library docking for discovering new chemotypes

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

In silico identification and experimental validation of hits active against KPC-2 β-lactamase

Bacterial resistance has become a worldwide concern, particularly after the emergence of resistant strains overproducing carbapenemases. Among these, the KPC-2 carbapenemase represents a significant clinical challenge, being characterized by a broad substrate spectrum that includes aminothiazoleoxime and cephalosporins such as cefotaxime. Moreover, strains harboring KPC-type β-lactamases are often reported as resistant to available β-lactamase inhibitors (clavulanic acid, tazobactam and sulbactam). Therefore, the identification of novel non β-lactam KPC-2 inhibitors is strongly necessary to maintain treatment options. This study explored novel, non-covalent inhibitors active against KPC-2, as putative hit candidates. We performed a structure-based in silico screening of commercially available compounds for non-β-lactam KPC-2 inhibitors. Thirty-two commercially available high-scoring, fragment-like hits were selected for in vitro validation and their activity and mechanism of action vs the target was experimentally evaluated using recombinant KPC-2. N-(3-(1H-tetrazol-5-yl)phenyl)-3-fluorobenzamide (11a), in light of its ligand efficiency (LE = 0.28 kcal/mol/non-hydrogen atom) and chemistry, was selected as hit to be directed to chemical optimization to improve potency vs the enzyme and explore structural requirement for inhibition in KPC-2 binding site. Further, the compounds were evaluated against clinical strains overexpressing KPC-2 and the most promising compound reduced the MIC of the β-lactam antibiotic meropenem by four-fold.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.