Screening a fragment cocktail library using ultrafiltration

Ultrafiltration provides a generic method to discover ligands for protein drug targets with millimolar to micromolar K(d), the typical range of fragment-based drug discovery. This method was tailored to a 96-well format, and cocktails of fragment-sized molecules, with molecular masses between 150 and 300 Da, were screened against medical structural genomics target proteins. The validity of the method was confirmed through competitive binding assays in the presence of ligands known to bind the target proteins.     

Multivariate Modulation of the Zr MOF UiO-66 for Defect-Controlled Combination Anticancer Drug Delivery

Metal–organic frameworks (MOFs) are emerging as leading candidates for nanoscale drug delivery, as a consequence of their high drug capacities, ease of functionality, and the ability to carefully engineer key physical properties. Despite many anticancer treatment regimens consisting of a cocktail of different drugs, examples of delivery of multiple drugs from one MOF are rare, potentially hampered by difficulties in postsynthetic loading of more than one cargo molecule. Herein, we report a new strategy, multivariate modulation, which allows incorporation of up to three drugs in the Zr MOF UiO-66 by defect-loading. The drugs are added to one-pot solvothermal synthesis and are distributed throughout the MOF at defect sites by coordination to the metal clusters. This tight binding comes with retention of crystallinity and porosity, allowing a fourth drug to be postsynthetically loaded into the MOFs to yield nanoparticles loaded with cocktails of drugs that show enhancements in selective anticancer cytotoxicity against MCF-7 breast cancer cells in vitro. We believe that multivariate modulation is a significant advance in the application of MOFs in biomedicine, and anticipate the protocol will also be adopted in other areas of MOF chemistry, to easily produce defective MOFs with arrays of highly functionalised pores for potential application in gas separations and catalysis.     

Generating 3D molecules conditional on receptor binding sites with deep generative models

The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein–ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein–ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. We evaluate the properties of the generated molecules and demonstrate that they change significantly when conditioned on mutated receptors. We also explore the latent space learned by our generative model using sampling and interpolation techniques. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning.

We generate 3D molecules conditioned on receptor binding sites by training a deep generative model on protein–ligand complexes. Our model uses the conditional receptor information to make chemically relevant changes to the generated molecules.     

Multivariate Modulation of the Zr MOF UiO‐66 for Defect‐Controlled Combination Anticancer Drug Delivery

Metal–organic frameworks (MOFs) are emerging as leading candidates for nanoscale drug delivery, as a consequence of their high drug capacities, ease of functionality, and the ability to carefully engineer key physical properties. Despite many anticancer treatment regimens consisting of a cocktail of different drugs, examples of delivery of multiple drugs from one MOF are rare, potentially hampered by difficulties in postsynthetic loading of more than one cargo molecule. Herein, we report a new strategy, multivariate modulation, which allows incorporation of up to three drugs in the Zr MOF UiO‐66 by defect‐loading. The drugs are added to one‐pot solvothermal synthesis and are distributed throughout the MOF at defect sites by coordination to the metal clusters. This tight binding comes with retention of crystallinity and porosity, allowing a fourth drug to be postsynthetically loaded into the MOFs to yield nanoparticles loaded with cocktails of drugs that show enhancements in selective anticancer cytotoxicity against MCF‐7 breast cancer cells in vitro. We believe that multivariate modulation is a significant advance in the application of MOFs in biomedicine, and anticipate the protocol will also be adopted in other areas of MOF chemistry, to easily produce defective MOFs with arrays of highly functionalised pores for potential application in gas separations and catalysis.     

Distillation of used liquid scintillation cocktail and the possibility of re-utilization of the recovered solvent

Liquid(toluene and dioxane)scintillation cocktail containing [methyl-3H]thymidine as a radioactive material was distilled by a rotary evaporator after storage for 0 to 12 months. The solvent obtained by distillation was then purified by several treatments, and the 3H-concentration in the solvent after each treatment was determined accurately. The 3H-concentration in the rotary evaporator-distillate of the cocktails reached about 0.2% of that of the original scintillation cocktails when they were stored for 12 months. But these radioactive materials could be removed completely from each solvent by several refining treatments: washing with water followed by dehydration with molecular sieves in case of toluene scintillation cocktail, and desiccation by adding excess amount of KOH in case of dioxane scintillation cocktail. It was concluded that the distillation followed by several simple refining treatments was useful to regenerate the refined solvent available for the preparation of liquid scintillation cocktail and also as a preliminary step to dispose of the waste scintillation cocktail.     

Adsorption of water on sodium chloride surfaces: electrostatics – guided ab initio studies

Water adsorption is studied on medium-sized clusters of sodium chloride representing (100) and (110) surfaces at the ab initio level. Topographical features of molecular electrostatic potential (MESP) have been employed for predicting the potent sites for binding of one to four water molecules on these surfaces. Such guess geometries are initially optimized using an electrostatics-based model, electrostatic potential for intermolecular complexation (EPIC) and further at the Hartree–Fock and B3LYP/6-31G(d, p) levels. The corresponding interaction energies are examined for assessing co-operative binding effects. The geometry and interaction energy of four water molecules adsorbed on NaCl(100) clearly brings out the co-operative binding among the water molecules. Further, water binding to (110) surface is stronger than that with (100) surface. This is also in confirmation with the electrostatic properties of (110) surface. Many-body decomposition analysis brings out the stronger interaction between NaCl clusters with water molecules vis-a-vis water–water interaction.     

Structure-based de novo drug design using 3D deep generative models

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users'' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.     

High-throughput screening of inhibitory potential of nine cytochrome P450 enzymes in vitro using liquid chromatography/tandem mass spectrometry

The early detection of potential drug-drug interactions is an important issue of drug discovery that has led to the development of high-throughput screening (HTS) methods for potential drug interactions. We developed a HTS method for potential interactions of inhibitory drugs for nine human P450 enzymes using cocktail incubation and tandem mass spectrometry in vitro. This new method involves incubation of two cocktail doses and single cassette analysis. The two cocktail doses in vitro were developed to minimize solvent effects and mutual drug interactions among substrates: cocktail A was composed of phenacetin for CYP1A2, coumarin for CYP2A6, paclitaxel for CYP2C8, S-mephenytoin for CYP2C19, dextromethorphan for CYP2D6, and midazolam for CYP3A4; and cocktail B was composed of three substrates including bupropion for CYP2B6, tolbutamide for CYP2C9, and chlorzoxazone for CYP2E1. In the incubation study of these cocktails, the reaction mixtures were pooled and simultaneously analyzed using liquid chromatography/tandem mass spectrometry employing a fast gradient. The method was validated by comparing the inhibition data obtained from the incubation of each individual probe substrate alone with data from the new method. The IC50 value of each inhibitor in the cocktail agreed well with that of the individual probe drug as well as with values previously reported in the literature. As a HTS method for potential interactions of the inhibition of these nine P450 enzymes, this new method will be useful in the drug discovery process and for the mechanistic understanding of drug interactions.     

Electrostatics in protein binding and function

Protein electrostatic properties stem from the proportion and distribution of polar and charged residues. Polar and charged residues regulate the electrostatic properties by forming short-range interactions, like salt-bridges and hydrogen-bonds, and by defining the over-all electrostatic environment in the protein. Electrostatics play a major role in defining the mechanisms of protein-protein complex formation, molecular recognitions, thermal stabilities, conformational adaptabilities and protein movements. For example:- Functional hinges, or flexible regions of the protein, lack short-range electrostatic interactions; Thermophilic proteins have higher electrostatic interactions than their mesophilic counter parts; Increase in binding specificity and affinity involve optimization of electrostatics; High affinity antibodies have higher, and stronger, electrostatic interactions with their antigens; Rigid parts of proteins have higher and stronger electrostatic interactions. In this review we address the significance of electrostatics in protein folding, binding and function. We discuss that the electrostatic properties are evolutionally selected by a protein to perform an specific function. We also provide bona fide examples to illustrate this. Additionally, using continuum electrostatic and molecular dynamics approaches we show that the "hot-spot" inter-molecular interactions in a very specific antibody-antigen binding are mainly established through charged residues. These "hot-spot" molecular interactions stay intact even during high temperature molecular dynamics simulations, while the other inter-molecular interactions, of lesser functional significance, disappear. This further corroborates the significance of charge-charge interactions in defining binding mechanisms. High affinity binding frequently involves "electrostatic steering". The forces emerge from over-all electrostatic complementarities and by the formation of charged and polar interactions. We demonstrate that although the high affinity binding of barnase-barstar and anti-hen egg white lysozyme (HEL) antibody-HEL complexes involve different molecular mechanisms, it is electrostatically regulated in both the cases. These observations, and several other studies, suggest that a fine tuning of local and global electrostatic properties are essential for protein binding and function.     

Virtual screening of p53 mutants reveals Y220S as an additional rescue drug target for PhiKan083 with higher binding characteristics

Pharmacological intervention to reactivate p53 in human tumors holds great promise for cancer patients. A number of small molecules that reactivate p53 mutants that are either specific to certain mutation or more broadly on various mutants of p53 are discovered by rational design and screening methods. One of the most remarkable among small molecules for the rescue of specific mutant p53, Y220C is PhiKan083 (1-(9-ethyl-9H-carbazole-3-yl)-N-methylmethanamine) that have been demonstrated effective in advanced pre-clinical trials. Our attempt here is to identify additional targets of p53 mutants for rescue drugs and provide insight into the molecular level interactions of the drug with the mutant target. In this study PhiKan083 also known as PK083 is investigated, screened and validated on 28 different mutants of p53 using FlexX. Interaction of PhiKan08 with Y220C is found to be largely hydrophobic and a crucial single H-bond interaction with Asp228 in addition to few electrostatic interactions. Our study identified Y220S mutant as an additional target for PK083 as it shows a similar interaction pattern. Besides this, Docking and MD simulation studies, showed that Y220S binds to PK083 at higher efficiency as a result of improved steric and hydrophobic environment in the binding cavity in comparison with known mutant target, Y220C. Further, we point out that structure guided optimization of PhiKan08 can lead to an improved drug that can interact favourably with yet another mutant, Y220 N. In addition, this study revealed that Y220H and other mutants including native p53 does not provide any favourable interaction with PhiKan08 which is in accord with the experimental findings. These findings can facilitate the selection of patients for clinical studies and cancer survival analysis.     

Alchemical absolute protein–ligand binding free energies for drug design

The recent advances in relative protein–ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets. We use a novel rigorous method to generate protein–ligand ensembles for the ligand in its decoupled state. Not only do the calculations deliver accurate protein–ligand binding affinity estimates, but they also provide detailed physical insight into the structural determinants of binding. We identify subtle rotamer rearrangements between apo and holo states of a protein that are crucial for binding. When compared to relative binding free energy calculations, obtaining absolute binding free energies is considerably more challenging in large part due to the need to explicitly account for the protein in its apo state. In this work we present several approaches to obtain apo state ensembles for accurate absolute ΔG calculations, thus outlining protocols for prospective application of the methods for drug discovery.

Molecular dynamics based absolute protein–ligand binding free energies can be calculated accurately and at large scale to facilitate drug discovery.     

Charge optimization of the interface between protein kinases and their ligands

Examining the potential for electrostatic complementarity between a ligand and a receptor is a useful technique for rational drug design, and can demonstrate how a system prioritizes interactions when allowed to optimize its charge distribution. In this computational study, we implemented the previously developed, continuum solvent-based charge optimization theory with a simple, quadratic programming algorithm and the UHBD Poisson-Boltzmann solver. This method allows one to compute the best set of point charges for a ligand or ligand region based on the ligand and receptor shape, and the receptor partial charges, by optimizing the binding free energy obtained from a continuum-solvent model. We applied charge optimization to a fragment of the heat-stable protein kinase inhibitor (PKI) of protein kinase A (PKA), to three flavopiridol inhibitors of CDK2, and to cyclin A which interacts with CDK2 to regulate the cell cycle. We found that a combination of global (involving every charge) and local (involving only charges in a local region) optimization can give useful hints for designing better inhibitors. Although some parts of an inhibitor may already contribute significantly to binding, we found that they could still be the most important targets for modifications to obtain stronger binders. In studying the binding of flavopiridol inhibitors to CDK2, comparable binding affinity could be obtained regardless of whether the net charges of the inhibitors were constrained to -2, -1, 0, 1, or 2 during the optimization. This provides flexibility in inhibitor design when a certain net charge of the inhibitor is desired in addition to strong binding affinity. For the study of the PKA-PKI and CDK2-cyclin A interfaces, we identified residues whose charge distributions are already close to optimal and those whose charge distributions could be refined to further improve binding.     

Privileged structures: applications in drug discovery

Over the past 15 years the privileged structure concept has emerged as a fruitful approach to the discovery of novel biologically active molecules. Privileged structures are molecular scaffolds with versatile binding properties, such that a single scaffold is able to provide potent and selective ligands for a range of different biological targets through modification of functional groups. In addition, privileged structures typically exhibit good drug-like properties, which in turn leads to more drug-like compound libraries and leads. The net result is the production of high quality leads that provide a solid foundation for further development. The identification of privileged structures will be discussed, emphasizing the importance of understanding the structure-target relationships that confer "privileged" status. This understanding allows privileged structure based libraries to be targeted at distinct target families (e.g. GPCRs, LGIC, enzymes/kinases). Privileged structures have been successfully exploited across and within different target families and promises to be an effective approach to the discovery and optimization of novel bioactive molecules. The application of the privileged structure approach, both in traditional medicinal chemistry and in the design of focused libraries, will be discussed with the aid of illustrative examples.     

Fragment-based drug design: computational & experimental state of the art

Fragment-based screening is an emerging technology which is used as an alternative to high-throughput screening (HTS), and often in parallel. Fragment screening focuses on very small compounds. Because of their small size and simplicity, fragments exhibit a low to medium binding affinity (mM to μM) and must therefore be screened at high concentration in order to detect binding events. Since some issues are associated with high-concentration screening in biochemical assays, biophysical methods are generally employed in fragment screening campaigns. Moreover, these techniques are very sensitive and some of them can give precise information about the binding mode of fragments, which facilitates the mandatory hit-to-lead optimization. One of the main advantages of fragment-based screening is that fragment hits generally exhibit a strong binding with respect to their size, and their subsequent optimization should lead to compounds with better pharmacokinetic properties compared to molecules evolved from HTS hits. In other words, fragments are interesting starting points for drug discovery projects. Besides, the chemical space of low-complexity compounds is very limited in comparison to that of drug-like molecules, and thus easier to explore with a screening library of limited size. Furthermore, the "combinatorial explosion" effect ensures that the resulting combinations of interlinked binding fragments may cover a significant part of "drug-like" chemical space. In parallel to experimental screening, virtual screening techniques, dedicated to fragments or wider compounds, are gaining momentum in order to further reduce the number of compounds to test. This article is a review of the latest news in both experimental and in silico virtual screening in the fragment-based discovery field. Given the specificity of this journal, special attention will be given to fragment library design.     

A method for fast energy estimation and visualization of protein-ligand interaction

Summary A new computational and graphical method for facilitating ligand-protein docking studies is developed on a three-dimensional computer graphics display. Various physical and chemical properties inside the ligand binding pocket of a receptor protein, whose structure is elucidated by X-ray crystal analysis, are calculated on three-dimensional grid points and are stored in advance. By utilizing those tabulated data, it is possible to estimate the non-bonded and electrostatic interaction energy and the number of possible hydrogen bonds between protein and ligand molecules in real time during an interactive docking operation. The method also provides a comprehensive visualization of the local environment inside the binding pocket.With this method, it becomes easier to find a roughly stable geometry of ligand molecules, and one can therefore make a rapid survey of the binding capability of many drug candidates. The method will be useful for drug design as well as for the examination of protein-ligand interactions.     

Electrostatic properties of cyano-containing mesogens

We analyse the electrostatic properties of a set of cyano-containing mesogen molecules with different rigid cores and variable alkyl chain lengths, computing the molecular charge distributions. Using the simple prototype benzonitrile, we analyse the reliability of the quantum chemical methods used to estimate the electrostatic dipole moments of polar conjugated molecules. We show that the electronic properties of the long mesogenic molecules can be treated by combining HF geometry optimization procedures with single point MP2 calculations. We compare the results of these computations with the available experimental phase transition data of mesogens and discuss some examples of how the non-trivial mesomorphic behaviour, which is usually observed in these cyano compounds, can be qualitatively explained by the molecular electrostatic interaction potential.     

Electrostatic properties of cyano-containing mesogens

We analyse the electrostatic properties of a set of cyano-containing mesogen molecules with different rigid cores and variable alkyl chain lengths, computing the molecular charge distributions. Using the simple prototype benzonitrile, we analyse the reliability of the quantum chemical methods used to estimate the electrostatic dipole moments of polar conjugated molecules. We show that the electronic properties of the long mesogenic molecules can be treated by combining HF geometry optimization procedures with single point MP2 calculations. We compare the results of these computations with the available experimental phase transition data of mesogens and discuss some examples of how the non-trivial mesomorphic behaviour, which is usually observed in these cyano compounds, can be qualitatively explained by the molecular electrostatic interaction potential.     

Net charge changes in the calculation of relative ligand‐binding free energies via classical atomistic molecular dynamics simulation

The calculation of binding free energies of charged species to a target molecule is a frequently encountered problem in molecular dynamics studies of (bio‐)chemical thermodynamics. Many important endogenous receptor‐binding molecules, enzyme substrates, or drug molecules have a nonzero net charge. Absolute binding free energies, as well as binding free energies relative to another molecule with a different net charge will be affected by artifacts due to the used effective electrostatic interaction function and associated parameters (e.g., size of the computational box). In the present study, charging contributions to binding free energies of small oligoatomic ions to a series of model host cavities functionalized with different chemical groups are calculated with classical atomistic molecular dynamics simulation. Electrostatic interactions are treated using a lattice‐summation scheme or a cutoff‐truncation scheme with Barker–Watts reaction‐field correction, and the simulations are conducted in boxes of different edge lengths. It is illustrated that the charging free energies of the guest molecules in water and in the host strongly depend on the applied methodology and that neglect of correction terms for the artifacts introduced by the finite size of the simulated system and the use of an effective electrostatic interaction function considerably impairs the thermodynamic interpretation of guest‐host interactions. Application of correction terms for the various artifacts yields consistent results for the charging contribution to binding free energies and is thus a prerequisite for the valid interpretation or prediction of experimental data via molecular dynamics simulation. Analysis and correction of electrostatic artifacts according to the scheme proposed in the present study should therefore be considered an integral part of careful free‐energy calculation studies if changes in the net charge are involved. © 2013 The Authors Journal of Computational Chemistry Published by Wiley Periodicals, Inc.