Conformational changes of small molecules binding to proteins

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.

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.

Photoswitchable Diazocine-Based Estrogen Receptor Agonists: Stabilization of the Active Form inside the Receptor

Photopharmacology is an emerging approach in drug design and pharmacological therapy. Light is used to switch a pharmacophore between a biologically inactive and an active isomer with high spatiotemporal resolution at the site of illness, thus potentially avoiding side effects in neighboring healthy tissue. The most frequently used strategy to design a photoswitchable drug is to replace a suitable functional group in a known bioactive molecule with azobenzene. Our strategy is different in that the photoswitch moiety is closer to the drug's scaffold. Docking studies reveal a very high structural similarity of natural 17β-estradiol and the E isomers of dihydroxy diazocines, but not their Z isomers, respectively. Seven dihydroxy diazocines were synthesized and subjected to a biological estrogen reporter gene assay. Four derivatives exhibit distinct estrogenic activity after irradiation with violet light, which can be shut off with green light. Most remarkably, the photogenerated, active E form of one of the active compounds isomerizes back to the inactive Z form with a half-life of merely several milliseconds in water, but nevertheless is active for more than 3 h in the presence of the estrogen receptor. The results suggest a significant local impact of the ligand-receptor complex toward back-isomerization. Thus, drugs that are active when bound but lose their activity immediately after leaving the receptor could be of great pharmacological value because they strongly increase target specificity. Moreover, the drugs are released into the environment in their inactive form. The latter argument is particularly important for drugs that act as endocrine disruptors.

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.

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.

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 .

Structural Effects of Amines in Enhancing Methanesulfonic Acid-Driven New Particle Formation

Atmospheric amines can enhance methanesulfonic acid (MSA)-driven new particle formation (NPF), but the mechanism is fundamentally different compared to that of the extensively studied sulfuric acid (SA)-driven process. Generally, the enhancing potentials of amines in SA-driven NPF follow the basicity, while this is not the case for MSA-driven NPF, where structural effects dominate, making MSA-driven NPF more prominent for methylamine (MA) compared to dimethylamine (DMA). Therefore, probing structural factors determining the enhancing potentials of amines on MSA-driven NPF is key to fully understanding the contribution of MSA to NPF. Here, we performed a comparative study on DMA and MA enhancing MSA-driven NPF by examining cluster formation using computational methods. The results indicate that DMA-MSA clusters are more stable than the corresponding MA-MSA clusters for cluster sizes up to (DMA)₂(MSA)₂, indicating that the basicity of amines dominates the initial cluster formation. The methyl groups of DMA were found to present significant steric hindrance beyond the (DMA)₂(MSA)₂ cluster and this adds to the lower hydrogen bonding capacity of DMA, making the cluster growth less favorable compared to MA. This study implies that several amines could synergistically enhance MSA-driven NPF by maximizing the advantage of different amines in different amine-MSA cluster growth stages.

Identification of Intrinsic Disorder in Complexes from the Protein Data Bank

Background: Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution, yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of sequence → structure → function paradigm. The Protein Data Bank (PDB) includes complexes containing disordered segments bound to globular proteins, but the molecular mechanisms of such binding interactions remain largely unknown. Results: In this study, we present the results of various disorder predictions on a nonredundant set of PDB complexes. In contrast to their structural appearances, many PDB proteins were predicted to be disordered when separated from their binding partners. These predicted-to-be-disordered proteins were observed to form structures depending upon various factors, including heterogroup binding, protein/DNA/RNA binding, disulfide bonds, and ion binding. Conclusions: This study collects many examples of disorder-to-order transition in IDP complex formation, thus revealing the unusual structure–function relationships of IDPs and providing an additional support for the newly proposed paradigm of the sequence → IDP/IDR ensemble → function.

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.

Current Status of Carbohydrates Information in the Protein Data Bank

Carbohydrates are well known for their physicochemical, biological, functional, and therapeutic characteristics. Unfortunately, their chemical nature imposes severe challenges for the structural elucidation of these phenomena, impairing not only the depth of our understanding of carbohydrates but also the development of new biotechnological and therapeutic applications based on these molecules. In the recent past, the amount of structural information, obtained mainly from X-ray crystallography, has increased progressively, as well as its quality. In this context, the current work presents a global analysis of the carbohydrate information available in the Protein Data Bank (PDB). From high quality structures, it is clear that most of the data are highly concentrated on a few sets of residue types, on their monosaccharidic forms, and connected by a small diversity of glycosidic linkages. The geometries of these linkages can be mostly associated with the types of linkages instead of residues, while the level of puckering distortion was characterized, quantified, and located in a pseudorotational equilibrium landscape, not only to local minima but also to transitional states. These qualitative and quantitative analyses offer a global picture of the carbohydrate structural content in the PDB, potentially supporting the building of new models for carbohydrate-related biological phenomena at the atomistic level, including new developments on force field parameters.

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.

What Next for Quantum Mechanics in Structure-Based Drug Discovery?

There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.

Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy

BACKGROUND

Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales.

OBJECTIVE

Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration.

METHODS

Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs.

RESULTS

This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed.

CONCLUSION

It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening

We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.

Binding of Pb-Melatonin and Pb-(Melatonin-metabolites) complexes with DMT1 and ZIP8: implications for lead detoxification

We have applied the docking methodology to characterize the binding modes of the divalent metal transporter 1 (DMT1) and the zinc transporter 8 (ZIP8) protein channels with: melatonin, some melatonin metabolites, and a few lead complexes of melatonin and its metabolites, in three different coordination modes (mono-coordinated, bi-coordinated and tri-coordinated). Our results show that bi-coordinated and tri-coordinated lead complexes prefer to bind inside the central region of ZIP8. Moreover, the interaction strength is larger compared with that of the free melatonin and melatonin metabolites. On the other hand, the binding modes with DMT1 of such complexes display lower binding energies, compared with the free melatonin and melatonin metabolites. Our results suggest that ZIP8 plays a major role in the translocation of Pb, bi or tri coordinated, when melatonin metabolites are present. Finally, we have characterized the binding modes responsible for the ZIP8 large affinities, found in bi-coordinated and tri-coordinated lead complexes. Our results show that such interactions are greater, because of an increase of the number of hydrogen bonds, the number and intensity of electrostatic interactions, and the interaction overlay degree in each binding mode. Our results give insight into the importance of the ZIP8 channel on lead transport and a possible elimination mechanism in lead detoxification processes. Graphical abstract .

The conformation of chloramphenicol in the ordered and disordered phases

The conformational behavior of chloramphenicol (CHL) in the solid, liquid and vapor phases is revisited here by means of FTIR spectroscopy and QM methods. In the crystalline phase, both the IR analysis and QM computations discard the conformer proposed by Acharya et al. (Acta Cryst., 1979, B35:1360-1363) and support the one proposed by Chatterjee et al. (J. Cryst. Mol. Struct., 1979, 9:295-304), characterized by an intramolecular OH⋯O hydrogen bond in which the primary hydroxyl group acts as hydrogen bond donor. The conformational behavior of CHL in the liquid and gas phases has been analyzed using QM calculations. The Self-Consistent Reaction Field (SCRF) method with the Onsager solvation model has been used for the initial optimizations in solution, and the lowest energy conformers have been refined using the Solvation Model based on Density (SMD). In solution environment the intramolecular OH⋯O hydrogen bond in CHL is reversed so that the secondary hydroxyl group acts as hydrogen bond donor. In addition, the dichloroacetamide group folds back further over the phenyl ring to form an intramolecular CCl⋯π halogen bond. Two different halogen bonds are actually observed (each one with a different chlorine atom) resulting in two different stable conformers, that can be detected by FTIR spectroscopy due to the conformational sensitivity of the CO group to the conformation of the dichloroacetyl group. Finally, the stability of the conformers with the polarity of the medium is also discussed.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Docking-based 3D-QSAR and pharmacophore studies on diarylpyrimidines as non-nucleoside inhibitors of HIV-1 reverse transcriptase

Diarylpyrimidines (DAPYs), a type of effective HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), have been considered as one of the most successful agents for treating AIDS. A number of structurally diverse DAPYs have been designed and synthesized in the past decade, and most of them exhibited potent anti-HIV-1 activities; however, the structure-activity relationships of recently reported DAPYs and their pharmacophore features that interacted with HIV-1 reverse transcriptase (RT) remain to be studied. In the present study, molecular docking studies were first performed on three novel classes of DAPYs to study their binding pattern in the HIV-1 RT. Based on the docking conformations of these DAPYs, 3D-QSAR models were constructed using CoMSIA and Topomer CoMFA methods, and pharmacophore models were also built using distance comparison technique. All selected DAPYs presented preferred U- or L-shaped conformations while being docked into the non-nucleoside inhibitor-binding pocket of the HIV-1 RT. The best CoMSIA model exhibited powerful predictivity, with satisfactory statistical parameters such as a q² of 0.572, an r² of 0.952, and an [Formula: see text] of 0.728. Contour maps of the best CoMSIA model were in accordance with those of the Topomer CoMFA model, giving the insight into the feature requirements of DAPYs for the anti-HIV-1 activity. Three potential pharmacophore models were constructed, and each of them was consisted of five hypothesis features. All results suggested that the aromatic ring on the left wing of DAPYs and the central pyrimidine ring contained key pharmacophore features for the anti-HIV-1 activity, and also indicated that the right wing of DAPYs had potential for further structural modification to improve activity. Eight novel DAPY molecules with potential anti-HIV-1 activities were designed on the basis of the obtained results. The findings in this study might provide important information for further design and development of novel HIV-1 NNRTIs.

Conformational Plasticity in Tyrosine Kinase Inhibitor-Kinase Interactions Revealed with Fluorescence Spectroscopy and Theoretical Calculations

To understand drug-protein dynamics, it is necessary to account for drug molecular flexibility and binding site plasticity. Herein, we exploit fluorescence from a tyrosine kinase inhibitor, AG1478, as a reporter of its conformation and binding site environment when complexed with its cognate kinase. Water-soluble kinases, aminoglycoside phosphotransferase APH(3')-Ia and mitogen-activated protein kinase 14 (MAPK14), were chosen for this study. On the basis of our prior work, the AG1478 conformation (planar or twisted) was inferred from the fluorescence excitation spectrum and the polarity of the AG1478-binding site was deduced from the fluorescence emission spectrum, while red-edge excitation shift (REES) probed the heterogeneity of the binding site (protein conformation and hydration) distributions in the protein conformational ensemble. In the AG1478-APH(3')-Ia complex, both twisted (or partially twisted) and planar AG1478 conformations were evidenced from emission wavelength-dependent excitation spectra. The binding site environment provided by APH(3')-Ia was moderately polar (λ_max = 480 nm) with evidence for considerable heterogeneity (REES = 34 nm). In contrast, in the AG1478-MAPK14 complex, AG1478 was in a predominantly planar conformation with a lower degree of conformational heterogeneity. The binding site environment provided by the MAPK14 protein was of relatively low polarity (λ_max = 430 nm) with a smaller degree of heterogeneity (REES = 11 nm). The results are compared with the available X-ray data and discussed in the context of our current understanding of tyrosine kinase inhibitor conformation and protein conformational ensembles.

How Diverse Are the Protein-Bound Conformations of Small-Molecule Drugs and Cofactors?

Knowledge of the bioactive conformations of small molecules or the ability to predict them with theoretical methods is of key importance to the design of bioactive compounds such as drugs, agrochemicals, and cosmetics. Using an elaborate cheminformatics pipeline, which also evaluates the support of individual atom coordinates by the measured electron density, we compiled a complete set ("Sperrylite Dataset") of high-quality structures of protein-bound ligand conformations from the PDB. The Sperrylite Dataset consists of a total of 10,936 high-quality structures of 4,548 unique ligands. Based on this dataset, we assessed the variability of the bioactive conformations of 91 small molecules-each represented by a minimum of ten structures-and found it to be largely independent of the number of rotatable bonds. Sixty-nine molecules had at least two distinct conformations (defined by an RMSD greater than 1 Å). For a representative subset of 17 approved drugs and cofactors we observed a clear trend for the formation of few clusters of highly similar conformers. Even for proteins that share a very low sequence identity, ligands were regularly found to adopt similar conformations. For cofactors, a clear trend for extended conformations was measured, although in few cases also coiled conformers were observed. The Sperrylite Dataset is available for download from http://www.zbh.uni-hamburg.de/sperrylite_dataset.

Molecular Descriptors for Structure-Activity Applications: A Hands-On Approach

Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.

Impact of Molecular Descriptors on Computational Models

Molecular descriptors encode a wide variety of molecular information and have become the support of many contemporary chemoinformatic and bioinformatic applications. They grasp specific molecular features (e.g., geometry, shape, pharmacophores, or atomic properties) and directly affect computational models, in terms of outcome, performance, and applicability. This chapter aims to illustrate the impact of different molecular descriptors on the structural information captured and on the perceived chemical similarity among molecules. After introducing the fundamental concepts of molecular descriptor theory and application, a step-by-step retrospective virtual screening procedure guides users through the fundamental processing steps and discusses the impact of different types of molecular descriptors.

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.

N6-Substituted 5'-N-Methylcarbamoyl-4'-selenoadenosines as Potent and Selective A3 Adenosine Receptor Agonists with Unusual Sugar Puckering and Nucleobase Orientation

Potent and selective A₃ adenosine receptor (AR) agonists were identified by the replacement of 4'-oxo- or 4'-thionucleosides with bioisosteric selenium. Unlike previous agonists, 4'-seleno analogues preferred a glycosidic syn conformation and South sugar puckering, as shown in the X-ray crystal structure of 5'-N-methylcarbamoyl derivative 3p. Among the compounds tested, N⁶-3-iodobenzyl analogue 3d was found to be the most potent A₃AR full agonist (K_i = 0.57 nM), which was ≥800- and 1900-fold selective for A₁AR and A_2AAR, respectively. In the N⁶-cycloalkyl series, 2-Cl analogues generally exhibited better hA₃AR affinity than 2-H analogues, whereas 2-H > 2-Cl in the N⁶-3-halobenzyl series. N⁷ isomers 3t and 3u were much weaker in binding than corresponding N⁹ isomers, but compound 3t lacked A₃AR activation, appearing to be a weak antagonist. 2-Cl-N⁶-3-iodobenzyl analogue 3p inhibited chemoattractant-induced migration of microglia/monocytes without inducing cell death at ≤50 μM. This suggests the potential for the development of 4'-selenonucleoside A₃AR agonists as novel antistroke agents.

Dihedral Angle-Based Sampling of Natural Product Polyketide Conformations: Application to Permeability Prediction

Macrocycles pose challenges for computer-aided drug design due to their conformational complexity. One fundamental challenge is identifying all low-energy conformations of the macrocyclic ring, which is important for modeling target binding, passive membrane permeation, and other conformation-dependent properties. Macrocyclic polyketides are medically and biologically important natural products characterized by structural and functional diversity. Advances in synthetic biology and semisynthetic methods may enable creation of an even more diverse set of non-natural product polyketides for drug discovery and other applications. However, the conformational sampling of these flexible compounds remains demanding. We developed and optimized a dihedral angle-based macrocycle conformational sampling method for macrocycles of arbitrary structure, and here we apply it to diverse polyketide natural products. First, we evaluated its performance using a data set of 37 polyketides with available crystal structures, with 9-22 rotatable bonds in the macrocyclic ring. Our optimized protocol was able to reproduce the crystal structure of polyketides' aglycone backbone within 0.50 Å RMSD for 31 out of 37 polyketides. Consistent with prior structural studies, our analysis suggests that polyketides tend to have multiple distinct low-energy structures, including the bioactive (target-bound) conformation as well as others of unknown significance. For this reason, we also introduce a strategy to improve both efficiency and accuracy of the conformational search by utilizing torsional restraints derived from NMR vicinal proton couplings to restrict the conformational search. Finally, as a first application of the method, we made blinded predictions of the passive membrane permeability of a diverse set of polyketides, based on their predicted structures in low- and high-dielectric media.

Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding

There has been an explosion of structural information for pharmaceutical compounds bound to biological targets, but the conformations and dynamics of compounds free in solution are poorly characterized, if at all. Yet, knowledge of the unbound state is essential to understand the fundamentals of molecular recognition, including the much debated conformational intramolecular reorganization energy of a compound upon binding (ΔEReorg). Also, dependable observation of the unbound compounds is important for ligand-based drug discovery, e.g. with pharmacophore modelling. Here, these questions are addressed with long (⩾0.5μs) state-of-the-art molecular dynamics (MD) simulations of 26 compounds (including 7 approved drugs) unbound in explicit solvent. These compounds were selected to be chemically diverse, with a range of flexibility, and good quality bioactive X-ray structures. The MD-simulated free compounds are compared to their bioactive structure and conformers generated with ad hoc sampling in vacuo or with implicit generalized Born (GB) aqueous solvation models. The GB conformational models clearly depart from those obtained in explicit solvent, and suffer from conformational collapse almost as severe as in vacuo. Thus, the global energy minima in vacuo or with GB are not suitable representations of the unbound state, which can instead be extensively sampled by MD simulations. Many, but not all, MD-simulated compounds displayed some structural similarity to their bioactive structure, supporting the notion of conformational pre-organization for binding. The ligand-protein complexes were also simulated in explicit solvent, to estimate ΔEReorg as an enthalpic difference ΔHReorg between the intramolecular energies in the bound and unbound states. This fresh approach yielded ΔHReorg values⩽6kcal/mol for 18 out of 26 compounds. For three particularly polar compounds 15⩽ΔHReorg⩽20kcal/mol, supporting the notion that ΔHReorg can be substantial. Those large ΔHReorg values correspond to a redistribution of electrostatic interactions upon binding. Overall, the study illustrates how MD simulations offer a promising avenue to characterize the unbound state of medicinal compounds.

GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field

Molecular docking is an important component of computer-aided drug discovery. In this communication, we describe GeauxDock, a new docking approach that builds on the ideas of ligand homology modeling. GeauxDock features a descriptor-based scoring function integrating evolutionary constraints with physics-based energy terms, a mixed-resolution molecular representation of protein-ligand complexes, and an efficient Monte Carlo sampling protocol. To drive docking simulations toward experimental conformations, the scoring function was carefully optimized to produce a correlation between the total pseudoenergy and the native-likeness of binding poses. Indeed, benchmarking calculations demonstrate that GeauxDock has a strong capacity to identify near-native conformations across docking trajectories with the area under receiver operating characteristics of 0.85. By excluding closely related templates, we show that GeauxDock maintains its accuracy at lower levels of homology through the increased contribution from physics-based energy terms compensating for weak evolutionary constraints. GeauxDock is available at http://www.institute.loni.org/lasigma/package/dock/.

The conformational behaviour of naproxen and flurbiprofen in solution by NMR spectroscopy

The conformational equilibrium of common anti-inflammatory drugs has been studied experimentally in solution by NMR in weakly ordered PBLG phases.

Conformational behavior of flavin adenine dinucleotide: conserved stereochemistry in bound and free states

Metabolic enzymes utilize the cofactor flavin adenine dinucleotide (FAD) to catalyze essential biochemical reactions. Because these enzymes have been implicated in disease pathways, it will be necessary to target them via FAD-based structural analogues that can either activate/inhibit the enzymatic activity. To achieve this, it is important to explore the conformational space of FAD in the enzyme-bound and free states. Herein, we analyze X-ray crystallographic data of the enzyme-bound FAD conformations and sample conformations of the molecule in explicit water by molecular dynamics (MD) simulations. Enzyme-bound FAD conformations segregate into five distinct groups based on dihedral angle principal component analysis (PCA). A notable feature in the bound FADs is that the adenine base and isoalloxazine ring are oppositely oriented relative to the pyrophosphate axis characterized by near trans hypothetical dihedral angle "δV" values. Not surprisingly, MD simulations in water show final compact but not perfectly stacked ring structures in FAD. Simulation data did not reveal noticeable changes in overall conformational dynamics of the dinucleotide in reduced and oxidized forms and in the presence and/or absence of ions. During unfolding-folding dynamics, the riboflavin moiety is more flexible than the adenosine monophosphate group in the molecule. Conversely, the isoalloxazine ring is more stable than the variable adenine base. The pyrophosphate group depicts an unusually highly organized fluctuation illustrated by its dihedral angle distribution. Conformations sampled from enzymes and MD are quantified. The extent to which the protein shifts the distribution from the unbound state is discussed in terms of prevalent FAD shapes and dihedral angle population.

Protein-ligand cocrystal structures: we can do better

There is a large body of evidence that many protein-ligand cocrystal structures contain poorly refined ligand geometries. These errors result in bound structures that have nonideal bond lengths and angles, are strained, contain improbable conformations, and have bad protein-ligand contacts. Many of these problems can be greatly reduced with better refinement models.

A comparative study of the molecular structure, lipophilicity, solubility, acidity, absorption and polar surface area of coumarinic anticoagulants and direct thrombin inhibitors

The methods of computational chemistry have been used to elucidate the molecular properties of coumarinic anticoagulants (acenocoumarol, phenprocoumon, warfarin and tecarfarin) and direct thrombin inhibitors (melagatran, dabigatran and their prodrug forms).

Conformational properties of cholic acid, a lead compound at the crossroads of bile acid inspired drug discovery

DFT and NMR spectroscopy studies unveil three major minima conformations of cholic acid that may affect its biological properties.

Applying conformational selection theory to improve crossdocking efficiency in 3-phosphoinositide dependent protein kinase-1

The emerging picture of biomolecular recognition is that of conformational selection followed by induced-fit. Conformational selection theory states that binding partners exist in various conformations in solution, with binding involving a "selection" between complementary conformers. In this study, we devise a docking protocol that mimics conformational selection in protein-ligand binding and demonstrate that it significantly enhances crossdocking accuracy over Glide's flexible docking protocol, which is widely used in the pharmaceutical industry. Our protocol uses a pregenerated conformational ensemble to simulate ligand flexibility. The ensemble was generated by thorough conformational sampling coupled with conformer minimization. The generated conformers were then rigidly docked in the active site of the protein along with a postdocking minimization step that allows limited induced fit effects to be modeled for the ligand. We illustrate the improved performance of our protocol through crossdocking of 31 ligands to cocomplexed proteins of the kinase 3-phosphoinositide dependent protein kinase-1 extracted from the crystal structures 1H1W (ATP bound), 1OKY (staurosporine bound) and 3QD0 (bound to a potent inhibitor). Consistent with conformational selection theory, the performance of our protocol was the best for crossdocking to the cognate protein bound to the natural ligand, ATP.

Quantification of free ligand conformational preferences by NMR and their relationship to the bioactive conformation

Accurate unbound solution 3D-structures of ligands provide unique opportunities for medicinal chemistry and, in particular, a context to understand binding thermodynamics and kinetics. Previous methods of deriving these 3D-structures have had neither the accuracy nor resolution needed for drug design and have not yet realized their potential. Here, we describe and apply a NMR methodology to the aminoglycoside streptomycin that can accurately quantify accessible 3D-space and rank the occupancy of observed conformers to a resolution that enables medicinal chemistry understanding and design. Importantly, it is based upon conventional small molecule NMR techniques and can be performed in physiologically-relevant solvents. The methodology uses multiple datasets, an order of magnitude more experimental data than previous NMR approaches and a dynamic model during refinement, is independent of computational chemistry and avoids the problem of virtual conformations. The refined set of solution 3D-shapes for streptomycin can be grouped into two major families, of which the most populated is almost identical to the 30S ribosomal subunit bioactive shape. We therefore propose that accurate unbound ligand solution conformations may, in some cases, provide a subsidiary route to bioactive shape without crystallography. This experimental technique opens up new opportunities for drug design and more so when complemented with protein co-crystal structures, SAR data and pharmacophore modeling.

Assessment and challenges of ligand docking into comparative models of G-protein coupled receptors

The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10³ fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.