Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding

Thermodynamic consequences of stapling side-chains on a peptide ligand using a lactam-bridge: A theoretical study on anti-angiogenic peptides targeting VEGF

Peptides are promising therapeutic agents for various biological targets due to their high efficacy and low toxicity, and the design of peptide ligands with high binding affinity to the target of interest is of utmost importance in peptide-based drug design. Introducing a conformational constraint to a flexible peptide ligand using a side-chain lactam-bridge is a convenient and efficient method to improve its binding affinity to the target. However, in general, such a small structural modification to a flexible ligand made with the intent of lowering the configurational entropic penalty for binding may have unintended consequences in different components of the binding enthalpy and entropy, including the configurational entropy component, which are still not clearly understood. Toward probing this, we examine different components of the binding enthalpy and entropy as well as the underlying structure and dynamics, for a side-chain lactam-bridged peptide inhibitor and its flexible analog forming complexes with vascular endothelial growth factor (VEGF), using all-atom molecular dynamics simulations. It is found that introducing a side-chain lactam-bridge constraint into the flexible peptide analog led to a gain in configurational entropy change but losses in solvation entropy, solute internal energy, and solvation energy changes upon binding, pinpointing the opportunities and challenges in drug design. The present study features an interplay between configurational and solvation entropy changes, as well as the one between binding enthalpy and entropy, in ligand-target binding upon imposing a conformational constraint into a flexible ligand.

In vitro antimicrobial, anticancer evaluation, and in silico studies of mannopyranoside analogs against bacterial and fungal proteins: Acylation leads to improved antimicrobial activity

Carbohydrate analogs are an important, well-established class of clinically useful medicinal agents that exhibit potent antimicrobial activity. Thus, we explored the various therapeutic potential of methyl α-D-mannopyranoside (MαDM) analogs, including their ability to synthesize and assess their antibacterial, antifungal, and anticancer properties; additionally, molecular docking, molecular dynamics simulation, and ADMET analysis were performed. The structure of the synthesized MαDM analogs was ascertained by spectroscopic techniques and physicochemical and elemental analysis. In vitro antimicrobial activity was assessed and revealed significant inhibitory effects, particularly against gram-negative bacteria along with the prediction of activity spectra for substances (PASS). Concurrently, MαDM analogs showed good results against antifungal pathogens and exhibited promising anticancer effects in vitro, demonstrating dose-dependent cytotoxicity against Ehrlich ascites carcinoma (EAC) cancer cells while sparing normal cells from compound 5, with an IC50 of 4511.65 µg/mL according to the MTT colorimetric assay. A structure-activity relationship (SAR) study revealed that hexose combined with the acyl chains of decanoyl (C-10) and benzenesulfonyl (C6H5SO2-) had synergistic effects on the bacteria and fungi that were examined. Molecular docking was performed against the Escherichia coli (6KZV) and Candida albicans (1EAG) proteins to acquire insights into the molecular interactions underlying the observed biological activities. The docking results were further supported by 100 ns molecular dynamics simulations, which provided a dynamic view of the stability and flexibility of complexes involving MαDM and its targets. In addition, ADMET analysis was used to evaluate the toxicological and pharmacokinetic profiles. Owing to their promising drug-like properties, these MαDM analogs exhibit potential as prospective therapeutic candidates for future development.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Discovery of non-peptide GLP-1r natural agonists for enhancing coronary safety in type 2 diabetes patients

This study explores the computational discovery of non-peptide agonists targeting the Glucagon-Like Peptide-1 Receptor (GLP-1R) to enhance the safety of major coronary outcomes in individuals affected by Type 2 Diabetes. The objective is to identify novel compounds that can activate the GLP-1R pathway without the limitations associated with peptide agonists. Type 2 diabetes mellitus (T2DM) is associated with an increased risk of cardiovascular disease (CVD) and mortality, which is attributed to the accumulation of fat in organs, including the heart. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are frequently used to manage T2DM and could potentially offer cardiovascular benefits. Therefore, this study examines non-peptide agonists of GLP-1R to improve coronary safety in type 2 diabetes patients. After rigorous assessments, two standout candidates were identified, with natural compound 12 emerging as the most promising. This study represents a notable advancement in enhancing the management of coronary outcomes among individuals with type 2 diabetes. The computational methodology employed successfully pinpointed potential GLP-1R natural agonists, providing optimism for the development of safer and more effective therapeutic interventions. Although computational methodologies have provided crucial insights, realizing the full potential of these compounds requires extensive experimental investigations, crucial in advancing therapeutic strategies for this critical patient population.Communicated by Ramaswamy H. Sarma.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.

Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design

Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Novel Benzothiazole Derivatives Synthesis and its Analysis as Diuretic Agents

Benzothiazoles, an anticonvulsant, antiviral, antihypertensive, and cancer-fighting medication of the heterocyclic scaffold family, also acts as antibacterial and antiviral agents. There is much interest in this chemical’s production because of the strong and vital biological action it possesses. Substituted aromatic aldehydes were combined with 2-amino-benzothiazole-6-sulfonic acid amides, or Schiff base derivatives, to create Schiff base derivatives. Recrystallized, characterized, and tested for diuretic efficacy in vivo using online tools, m.p. (melting point), Rf, FTIR (Fourier transform infrared), 1H-NMR (proton nuclear magnetic resonance) data The molecular characteristics of all the substances created were estimated using Lipinski’s rule of 5, OSIRIS (software) molecular property explorer, Molsoft, and Autodock 4.0 docking software. Male Wistar rats were used to make all the compounds traditionally in order to test for diuretic activity. Neither the elemental nor the spectral information for the synthesized compounds disagreed. There were five different methods used to evaluate these compounds: Lipinski rule of five, Molsoft to determine molecular characteristics, PASS (prediction of activity spectra for substances) values to determine the diuretic effect, and OSIRIS software to determine toxicology. In order to investigate the diuretic effects of the selected drugs, docking analysis was used. Acetazolamide was shown to have a diuretic effect that was superior to that of compounds IIIb and IIIe, whereas 2-{(E)-[(3-hydroxyphenyl)methylidene]amino}-1,3-benzothiazole-6-sulfonamide (IIIb) was found to be the most promising potential.

In situ ligand restraints from quantum-mechanical methods

In macromolecular crystallographic structure refinement, ligands present challenges for the generation of geometric restraints due to their large chemical variability, their possible novel nature and their specific interaction with the binding pocket of the protein. Quantum-mechanical approaches are useful for providing accurate ligand geometries, but can be plagued by the number of minima in flexible molecules. In an effort to avoid these issues, the Quantum Mechanical Restraints (QMR) procedure optimizes the ligand geometry in situ, thus accounting for the influence of the macromolecule on the local energy minima of the ligand. The optimized ligand geometry is used to generate target values for geometric restraints during the crystallographic refinement. As demonstrated using a sample of >2330 ligand instances in >1700 protein-ligand models, QMR restraints generally result in lower deviations from the target stereochemistry compared with conventionally generated restraints. In particular, the QMR approach provides accurate torsion restraints for ligands and other entities.

A Distributional Model of Bound Ligand Conformational Strain: From Small Molecules up to Large Peptidic Macrocycles

The internal conformational strain incurred by ligands upon binding a target site has a critical impact on binding affinity, and expectations about the magnitude of ligand strain guide conformational search protocols. Estimates for bound ligand strain begin with modeled ligand atomic coordinates from X-ray co-crystal structures. By deriving low-energy conformational ensembles to fit X-ray diffraction data, calculated strain energies are substantially reduced compared with prior approaches. We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity. The modeled strain distribution closely matches calculated strain values from experimental data comprising over 3000 protein-ligand complexes. The distributional model has direct implications for conformational search protocols as well as for directions in molecular design.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models

A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.

Hypothesis-Driven, Structure-Based Design in Photopharmacology: The Case of eDHFR Inhibitors

Photopharmacology uses light to regulate the biological activity of drugs. This precise control is obtained through the incorporation of molecular photoswitches into bioactive molecules. A major challenge for photopharmacology is the rational design of photoswitchable drugs that show light-induced activation. Computer-aided drug design is an attractive approach toward more effective, targeted design. Herein, we critically evaluated different structure-based approaches for photopharmacology with Escherichia coli dihydrofolate reductase (eDHFR) as a case study. Through the iterative examination of our hypotheses, we progressively tuned the design of azobenzene-based, photoswitchable eDHFR inhibitors in five design–make–switch–test–analyze cycles. Targeting a hydrophobic subpocket of the enzyme and a specific salt bridge only with the thermally metastable cis-isomer emerged as the most promising design strategy. We identified three inhibitors that could be activated upon irradiation and reached potencies in the low-nanomolar range. Above all, this systematic study provided valuable insights for future endeavors toward rational photopharmacology.

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

Property Map Collective Variable as a Useful Tool for a Force Field Correction

The accuracy of biomolecular simulations depends on the accuracy of an empirical molecular mechanics potential known as a force field: a set of parameters and expressions to estimate the potential from atomic coordinates. Accurate parametrization of force fields for small organic molecules is a challenge due to their high diversity. One of the possible approaches is to apply a correction to the existing force fields. Here, we propose an approach to estimate the density functional theory (DFT)-derived force field correction which is calculated during the run of molecular dynamics without significantly affecting its speed. Using the formula known as a property map collective variable, we approximate the force field correction by a weighted average of this force field correction calculated only for a small series of reference structures. To validate this method, we used seven AMBER force fields, and we show how it is possible to convert one force field to behave like the other one. We also present the force field correction for the important anticancer drug Imatinib as a use case example. Our method appears to be suitable for adjusting the force field for general drug-like molecules. We provide a pipeline that generates the correction; this pipeline is available at https://pmcvff-correction.cerit-sc.cz/.

Calculation of Crystal-Solution Dissociation Constants

The calculation of dissociation constants is an important problem in molecular biophysics. For such a calculation, it is important to correctly calculate both terms of the binding free energy; that is, the enthalpy and entropy of binding. Both these terms can be computed using molecular dynamics simulations, but this approach is very computationally expensive, and entropy calculations are especially slow. We develop an alternative very fast method of calculating the binding entropy and dissociation constants. The main part of our approach is based on the evaluation of movement ranges of molecules in the bound state. Then, the range of molecular movements in the bound state (here, in molecular crystals) is used for the calculation of the binding entropies and, then (using, in addition, the experimentally measured sublimation enthalpies), the crystal-to-vapor dissociation constants. Previously, we considered the process of the reversible sublimation of small organic molecules from crystals to vapor. In this work, we extend our approach by considering the dissolution of molecules, in addition to their sublimation. Similar to the sublimation case, our method shows a good correlation with experimentally measured dissociation constants at the dissolution of crystals.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states

The intramolecular reorganization energy (ΔE_Reorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔE_Reorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔE_Reorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔE_Reorg is technically difficult. Typically, ΔE_Reorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔH_Reorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔH_Reorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔH_Reorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔH_Reorg values. Such counterintuitive larger ΔH_Reorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.

Ligand Strain Energy in Large Library Docking

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing the ranks of strained high-scoring decoy molecules. In a 40-target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules and another based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on precalculated population distributions, this approach is rapid, taking less than 0.04 s to evaluate a conformation on a standard core, making it pragmatic for precalculating strain in even ultralarge libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org.

New perspectives in cancer drug development: computational advances with an eye to design

Computational chemistry has come of age in drug discovery. Indeed, most pharmaceutical development programs rely on computer-based data and results at some point. Herein, we discuss recent applications of advanced simulation techniques to difficult challenges in drug discovery. These entail the characterization of allosteric mechanisms and the identification of allosteric sites or cryptic pockets determined by protein motions, which are not immediately evident in the experimental structure of the target; the study of ligand binding mechanisms and their kinetic profiles; and the evaluation of drug-target affinities. We analyze different approaches to tackle challenging and emerging biological targets. Finally, we discuss the possible perspectives of future application of computation in drug discovery.

One Key and Multiple Locks: Substrate Binding in Structures of Tryptophan Dioxygenases and Hydroxylases

Since its discovery at the beginning of the past century, the essential nutrient l-Tryptophan (l-Trp) and its catabolic pathways have acquired an increasing interest in an ever wider scientific community for their pivotal roles in underlying many important physiological functions and associated pathological conditions. As a consequence, enzymes catalyzing rate limiting steps along l-Trp catabolic pathways - including IDO1, TDO, TPH1 and TPH2 - have turned to be interesting drug targets for the design and development of novel therapeutic agents for different disorders such as carcinoid syndrome, cancer and autoimmune diseases. This article provides a fresh comparative overview on the most recent advancements that crystallographic studies, biophysical and computational works have brought on structural aspects and molecular recognition patterns of these enzymes toward l-Trp. Finally, a conformational analysis of l-Trp is also discussed as part of the molecular recognition process governing the binding of a substrate to its cognate enzymes.

Ensemble completeness in conformer sampling: the case of small macrocycles

In this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ensembles derived for exhaustive molecular dynamics simulations applied to a dataset of 7 small macrocycles in two charge states and three solvents. Ensemble completeness is a prerequisite to allow for the selection of relevant diverse conformers for many applications in computational chemistry. We apply conformation maps using principal component analysis based on ring torsions. Our major finding critical for all applications of conformer ensembles in any computational study is that maps derived from MD with explicit solvent are significantly distinct between macrocycles, charge states and solvents, whereas the maps for post-optimized conformers using implicit solvent models from all generator algorithms are very similar independent of the solvent. We apply three metrics for the quantification of the relative covered ensemble space, namely cluster overlap, variance statistics, and a novel metric, Mahalanobis distance, showing that post-optimized MD ensembles cover a significantly larger conformational space than the generator ensembles, with the ranking PMM > BEST >> CONF. Furthermore, we find that the distributions of 3D polar surface areas are very similar for all macrocycles independent of charge state and solvent, except for the smaller and more strained compound 7, and that there is also no obvious correlation between 3D PSA and intramolecular hydrogen bond count distributions.

The Good, the Bad, and the Twisted Revisited: An Analysis of Ligand Geometry in Highly Resolved Protein-Ligand X-ray Structures

An analysis of the rotatable bond geometry of drug-like ligand models is reported for high-resolution (<1.1 Å) crystallographic protein-ligand complexes. In cases where the ligand fit to the electron density is very good, unusual torsional geometry is rare and, most often, though not exclusively, associated with strong polar, metal, or covalent ligand-protein interactions. It is rarely associated with a torsional strain of greater than 2 kcal mol^-1 by calculation. An unusual torsional geometry is more prevalent where the fit to electron density is not perfect. Multiple low-strain conformer bindings were observed in 21% of the set and, it is suggested, may also lie behind many of the 35% of single-occupancy cases, where a poor fit to the e-density was found. It is concluded that multiple conformer ligand binding is an under-recognized phenomenon in structure-based drug design and that there is a need for more robust crystallographic refinement methods to better handle such cases.

Theoretical Insight into the Library Screening Approach for Binding of Intermolecular G-Quadruplex RNA and Small Molecules through Docking and Molecular Dynamics Simulation Studies

The interactions of intermolecular G-quadruplex RNA and small molecules have been investigated by computational studies. Various anthraquinone, bisbenzimidazole, and carbazole-benzimidazole based ligands have shown a distinct preference to G-quadruplex structures as opposed to the corresponding duplex forms of DNA that were docked with telomeric G-quadruplex RNA. The comparative binding study of such ligands with G-quadruplex (G4) RNA showed higher binding affinities toward carbazole-benzimidazole ligands than those of the anthraquinone and bisbenzimidazole based ligands. A molecular dynamics simulation study was used to examine quadruplex-ligand interactions. Analysis of the binding free energy indicated the formation of the thermodynamically favorable RNA-ligand complex. The formation of several H-bonding interactions and the change of the solvent accessible surface area (SASA) also support the effective binding of the carbazole-benzimidazole ligands with G4 RNA structures. Thus, the library screening approach has assisted in getting a structure-activity relationship for the selected small molecules toward the G-quadruplex RNA binding, which can be applied in the targeting of G-quadruplex RNA medicated anticancer therapeutics.

Promiscuous Esterases Counterintuitively Are Less Flexible than Specific Ones

Understanding mechanisms of promiscuity is increasingly important from a fundamental and application point of view. As to enzyme structural dynamics, more promiscuous enzymes generally have been recognized to also be more flexible. However, examples for the opposite received much less attention. Here, we exploit comprehensive experimental information on the substrate promiscuity of 147 esterases tested against 96 esters together with computationally efficient rigidity analyses to understand the molecular origin of the observed promiscuity range. Unexpectedly, our data reveal that promiscuous esterases are significantly less flexible than specific ones, are significantly more thermostable, and have a significantly increased specific activity. These results may be reconciled with a model according to which structural flexibility in the case of specific esterases serves for conformational proofreading. Our results signify that an esterase sequence space can be screened by rigidity analyses for promiscuous esterases as starting points for further exploration in biotechnology and synthetic chemistry.

AUTOSTERE: Systematic Search for Scaffold Replacement Opportunities within Structural Databases

Medicinal chemists often bias toward working with scaffolds with which previously they have had direct experience and successes. In this way, it is often the case that scaffolds which have proven tractable within a research group are "reused" across multiple and sometimes unrelated drug targets. With this concept in mind, we designed a new computer algorithm AUTOSTERE which could systematically assess the opportunities to replace any part of any molecule within an entire database of known ligand structures with a target scaffold and automatically evaluate the potential designs in the context of the original ligand's protein environment. As such, it performs scaffold replacement on an unprecedented scale and suggests new target opportunities for preferred chemistries rather than the conventional reverse situation. The results of this approach for one scaffold, a substituted triazolinone, applied to a set of 10 426 ligand conformations extracted from the PDB are described. This led to the identification of ∼600 novel ligands incorporating the triazolinone scaffolds in complex with their predicted drug targets. From these, design examples are provided for HSP-90, cathepsin K, and TIE-2 kinase. A further study involved the searching for possible drug targets for unusual pyridopyrimidine cores. This process resulted in the identification of potential novel HIV reverse transcriptase inhibitors which were synthesized and shown to exhibit similar in vitro potencies to marketed compounds. Overall, the methodology described provides a powerful new approach to identify new target opportunities for scaffolds of provenance.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule-along with any bound ligand(s)-within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method-coupled with our XModeScore method for experimental tautomer/protomer state determination-to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

Conformational energies and equilibria of cyclic dinucleotides in vacuo and in solution: computational chemistry vs. NMR experiments

Performance of computational methods in modelling cyclic dinucleotides - an important and challenging class of compounds - has been evaluated by two different benchmarks: (1) gas-phase conformational energies and (2) qualitative agreement with NMR observations of the orientation of the χ-dihedral angle in solvent. In gas-phase benchmarks, where CCSD(T) and DLPNO-CCSD(T) methods have been used as the reference, most of the (dispersion corrected) density functional approximations are accurate enough to justify prioritizing computational cost and compatibility with other modelling options as the criterion of choice. NMR experiments of 3'3'-c-di-AMP, 3'3'-c-GAMP, and 3'3'-c-di-GMP show the overall prevalence of the anti-conformation of purine bases, but some population of syn-conformations is observed for guanines. Implicit solvation models combined with quantum-chemical methods struggle to reproduce this behaviour, probably due to a lack of dynamics and explicitly modelled solvent, leading to structures that are too compact. Molecular dynamics simulations overrepresent the syn-conformation of guanine due to the overestimation of an intramolecular hydrogen bond. Our combination of experimental and computational benchmarks provides "error bars" for modelling cyclic dinucleotides in solvent, where such information is generally difficult to obtain, and should help gauge the interpretability of studies dealing with binding of cyclic dinucleotides to important pharmaceutical targets. At the same time, the presented analysis calls for improvement in both implicit solvation models and force-field parameters.

Conformational Strain of Macrocyclic Peptides in Ligand-Receptor Complexes Based on Advanced Refinement of Bound-State Conformers

Macrocyclic peptides are an important modality in drug discovery, but molecular design is limited due to the complexity of their conformational landscape. To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures. Unexpectedly large strain energies were observed when the bound-state conformations were modeled with positional restraints. Instead, low-energy conformer ensembles were generated using xGen that fit experimental X-ray electron density maps and gave reasonable strain energy estimates. The ensembles featured significant conformational adjustments while still fitting the electron density as well or better than the original coordinates. Strain estimates suggest the interaction energy in protein-ligand complexes can offset a greater amount of strain for macrocyclic peptides than for small molecules and non-peptidic macrocycles. Across all molecular classes, the approximate upper bound on global strain energies had the same relationship with molecular size, and bound-state ensembles from xGen yielded favorable binding energy estimates.

Large-Scale Analysis of Bioactive Ligand Conformational Strain Energy by Ab Initio Calculation

Ligand conformational strain energy (LCSE) plays an important role in virtual screening and lead optimization. While various studies have provided insights into LCSE for small-molecule ligands in the Protein Data Bank (PDB), conclusions are inconsistent mainly due to small datasets, poor quality control of crystal structures, and molecular mechanics (MM) or low-level quantum mechanics (QM) calculations. Here, we built a high-quality dataset (LigBoundConf) of 8145 ligand-bound conformations from PDB crystal structures and calculated LCSE at the M062X-D3/ma-TZVPP (SMD)//M062X-D3/def2-SVP(SMD) level for each case in the dataset. The mean/median LCSE is 4.6/3.7 kcal/mol for 6672 successfully calculated cases, which is significantly lower than the estimates based on molecular mechanics in many previous analyses. Especially, when removing ligands with nonaromatic ring(s) that are prone to have large LCSEs due to electron density overfitting, the mean/median LCSE was reduced to 3.3/2.5 kcal/mol. We further reveal that LCSE is correlated with several ligand properties, including formal atomic charge, molecular weight, number of rotatable bonds, and number of hydrogen-bond donors and acceptors. In addition, our results show that although summation of torsion strains is a good approximation of LCSE for most cases, for a small fraction (about 6%) of our dataset, it underestimates LCSEs if ligands could form nonlocal intramolecular interactions in the unbound state. Taken together, our work provides a comprehensive profile of LCSE for ligands in PDB, which could help ligand conformation generation, ligand docking pose evaluation, and lead optimization.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .

The role of NMR in leveraging dynamics and entropy in drug design

Nuclear magnetic resonance (NMR) spectroscopy has contributed to structure-based drug development (SBDD) in a unique way compared to the other biophysical methods. The potency of a ligand binding to a protein is dictated by the binding free energy, which is an intricate interplay between entropy and enthalpy. In addition to providing the atomic resolution structural information, NMR can help to identify protein-ligand interactions that potentially contribute to the enthalpic component of the free energy. NMR can also illuminate dynamic aspects of the interaction, which correspond to the entropic term of the free energy. The ability of NMR to access both terms in the free energy equation stems from the suite of experiments developed to shed light on various aspects that contribute to both entropy and enthalpy, deepening our understanding of the biological function of macromolecules and assisting to target them in physiological conditions. Here we provide a brief account of the contribution of NMR to SBDD, highlighting hallmark examples and discussing the challenges that demand further method development. In the era of integrated biology, the unique ability of NMR to directly ascertain structural and dynamical aspects of macromolecule and monitor changes in these properties upon engaging a ligand can be combined with computational and other structural and biophysical methods to provide a more complete picture of the energetics of drug engagement with the target. Such efforts can be used to engineer better drugs.

A Potential Inhibition Process of Ricin Protein with the flavonoids Quercetin and Epigallocatechin Gallate. A Quantum-Chemical and Molecular Docking Study

Castor bean (Ricinus Communis) oil has been reported as one of the most important bio-based fuels; however, high amounts of toxic solid residue are generated in the production. This toxicity is due to several molecules, ricin protein being the most studied compound. The inhibition of the ricin protein is essential for eliminating its toxicity. The objective of this study is to predict the possible inhibition process via the interactions between the ricin protein and the flavonoids quercetin (Q) and epigallocatechin gallate (EGCG). The molecular structures of the complexes formed between the ricin protein and flavonoids were studied using quantum-chemical and molecular docking calculations to analyze the type of interaction, active site of the protein, binding energies, and different conformations in the inhibition process. Different methodologies were applied for the molecular structure determination; the best approximation was obtained with B3LYP/6-31G (d,p) theoretical methodology. Mappings of electrostatic potential (MEP) and frontier molecular orbitals were used for the identification of the probable sites of interaction, which were confirmed by molecular docking. The adjustment and alignment of flavonoid groups before and after the interaction, and charge transfer parameters, showed that Q and EGCG act as electron donors inside of the active site in ricin.

MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment

For decades, the complicated energy surfaces found in macromolecular protein:ligand structures, which require large amounts of computational time and resources for energy state sampling, have been an inherent obstacle to fast, routine free energy estimation in industrial drug discovery efforts. Beginning in 2013, the Merz research group addressed this cost with the introduction of a novel sampling methodology termed “Movable Type” (MT). Using numerical integration methods, the MT method reduces the computational expense for energy state sampling by independently calculating each atomic partition function from an initial molecular conformation in order to estimate the molecular free energy using ensembles of the atomic partition functions. In this work, we report a software package, the DivCon Discovery Suite with the MovableType module from QuantumBio Inc., that performs this MT free energy estimation protocol in a fast, fully encapsulated manner. We discuss the computational procedures and improvements to the original work, and we detail the corresponding settings for this software package. Finally, we introduce two validation benchmarks to evaluate the overall robustness of the method against a broad range of protein:ligand structural cases. With these publicly available benchmarks, we show that the method can use a variety of input types and parameters and exhibits comparable predictability whether the method is presented with “expensive” X-ray structures or “inexpensively docked” theoretical models. We also explore some next steps for the method. The MovableType software is available at http://www.quantumbioinc.com/

ReSCoSS: a flexible quantum chemistry workflow identifying relevant solution conformers of drug-like molecules

Conformational equilibria are at the heart of drug design, yet their energetic description is often hampered by the insufficient accuracy of low-cost methods. Here we present a flexible and semi-automatic workflow based on quantum chemistry, ReSCoSS, designed to identify relevant conformers and predict their equilibria across different solvent environments in the Conductor-like Screening Model for Real Solvents (COSMO-RS) framework. We demonstrate the utility and accuracy of the workflow through conformational case studies on several drug-like molecules from literature where relevant conformations are known. We further show that including ReSCoSS conformers significantly improves COSMO-RS based predictions of physicochemical properties over single-conformation approaches. ReSCoSS has found broad adoption in the in-house drug discovery and development work streams and has contributed to establishing quantum-chemistry methods as a strategic pillar in ligand discovery.

Understanding the Contribution of Individual Amino Acid Residues in the Binding of Psychoactive Substances to Monoamine Transporters

The development of point-of-care detection methodologies for biologically relevant analytes that can facilitate rapid and appropriate treatment is at the forefront of current research efforts and interests. Among the various approaches, those exploiting host-guest chemistries where the optoelectronic signals of the chemical sensor can be modulated upon interaction with the target analyte are of particular interest. In aiding their rational development, judicious selection of peripheral functional groups anchored to core motifs with desired properties is critical. Herein, we report an in-depth investigation of the binding of three psychoactive substances, MDAI, mexedrone, and phenibut, to receptors of the monoamine transporters for dopamine, norepinephrine, and serotonin, particularly focusing on the role of individual amino acid residues. We first evaluated the conformational flexibility of the ligands by comparing their experimentally determined crystal structure geometries to those optimized by means of quantum as well as molecular mechanics, observing significant changes in the case of phenibut. Molecular docking studies were employed to identify preferential binding sites by means of calculated docking scores. In all cases, irrespective of the monoamine transporter, psychoactive substances exhibited preferred interaction with the S1 or central site of the proteins, in line with previous studies. However, we observed that experimental trends for their relative potency on the three transporters were only reproduced in the case of mexedrone. Subsequently, to further understand these findings and to pave the way for the rational development of superior chemical sensors for these substances, we computed the individual contributions of each nearest neighbor amino acid residue to the binding to the target analytes. Interestingly, these results are now in agreement with those experimental potency trends. In addition, these observations were in all cases associated with key intermolecular interactions with neighboring residues, such as tyrosine and aspartic acid, in the binding of the ligands to the monoamine transporter for dopamine. As a result, we believe this work will be of interest to those engaged in the rational development of chemical sensors for small molecule analytes as well as to those interested in the use of computational approaches to further understand protein-ligand interactions.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

Evaluation of 3-Dimensionality in Approved and Experimental Drug Space

The 3-dimensional (3D) structure of therapeutics and other bioactive molecules is an important factor in determining the strength and selectivity of their protein-ligand interactions. Previous efforts have considered the strain introduced and tolerated through conformational changes induced upon protein binding. Herein, we present an analysis of 3-dimentionality for energy-minimized structures from the DrugBank and ligands bound to proteins identified in the Protein Data Bank (PDB). This analysis reveals that the majority of molecules found in both the DrugBank and the PDB tend toward linearity and planarity, with few molecules having highly 3D conformations. Decidedly 3D geometries have been historically difficult to achieve, likely due to the synthetic challenge of making 3D organic molecules, and other considerations, such as adherence to the 'rule-of-five'. This has resulted in the dominance of planar and/or linear topologies of the molecules described here. Strategies to address the generally flat nature of these data sets are explored, including the use of 3D organic fragments and inorganic scaffolds as a means of accessing privileged 3D space. This work highlights the potential utility of libraries with greater 3D topological diversity so that the importance of molecular shape to biological behavior can be more fully understood in drug discovery campaigns.

Selective host-guest chemistry, self-assembly and conformational preferences of m-xylene macrocycles probed by ion-mobility spectrometry mass spectrometry

We demonstrated ion-mobility spectrometry mass spectrometry (IMS-MS) as a powerful tool for interrogating and preserving selective chemistry including non-covalent and host-guest complexes of m-xylene macrocycles formed in solution. The technique readily revealed the unique favorability of a thiourea-containing macrocycle MXT to Zn²⁺ to form a dimer complex with the cation in an off-axis sandwich structure having the Zn-S bonds in a tetrahedral coordination environment. Replacing thiourea with urea generates MXU which formed high-order oligomerization with weak binding interactions to neutral DMSO guests detected at every oligomer size. The self-assembly pathway observed for this macrocycle is consistent with the crystalline assembly. Further transformation of urea into squaramide produces MXS, a rare receptor for probing sulfate in solution. Tight complexes were observed for both monomeric and dimeric of MXS in which HSO₄^- bound stronger than SO₄^2- to the host. The position of HSO₄^- at the binding cavity is a 180° inversion of the reported crystallographic SO₄^2-. The MXS dimer formed a prism-like shape with HSO₄^- exhibiting strong contacts with the 8 amine protons of two MXS macrocycles. By eliminating intermolecular interferences, we detected the low energy structures of MXS with collisional cross section (CCS) matching cis-trans and cis-cis squaramides-amines, both were not observed in crystallization trials. The experiments collectively unravel multiple facets of macrocycle chemistry including conformational flexibility, self-assembly and ligand binding; all in one analysis. Our findings illustrate an inexpensive and widely applicable approach to investigate weak but important interactions that define the shape and binding of macrocycles.

Synthesis, biochemical, pharmacological characterization and in silico profile modelling of highly potent opioid orvinol and thevinol derivatives

Morphine and its derivatives play inevitably important role in the μ-opioid receptor (MOR) targeted antinociception. A structure-activity relationship study is presented for novel and known orvinol and thevinol derivatives with varying 3-O, 6-O, 17-N and 20-alkyl substitutions starting from agonists, antagonists and partial agonists. In vitro competition binding experiments with [³H]DAMGO showed low subnanomolar affinity to MOR. Generally, 6-O-demethylation increased the affinity toward MOR and decreased the efficacy changing the pharmacological profile in some cases. In vivo tests in osteoarthritis inflammation model showed significant antiallodynic effects of thevinol derivatives while orvinol derivatives did not. The pharmacological character was modelled by computational docking to both active and inactive state models of MOR. Docking energy difference for the two states separates agonists and antagonists well while partial agonists overlapped with them. An interaction pattern of the ligands, involving the interacting receptor atoms, showed more efficient separation of the pharmacological profiles. In rats, thevinol derivatives showed antiallodynic effect in vivo. The orvinol derivatives, except for 6-O-desmethyl-dihydroetorfin (2c), did not show antiallodynic effect.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Conformational analysis of macrocycles: comparing general and specialized methods

Abstract

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray_ppw), and (v) to the X-ray_ppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-ray_ppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-ray_ppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-ray_ppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-020-00277-2) contains supplementary material, which is available to authorized users.