Search for improved host architectures: application of de novo structure-based design and high-throughput screening methods to identify optimal building blocks for multidentate ethers

This paper presents a computational approach to the deliberate design of improved host architectures. The approach, which involves the use of computer-aided design software, is illustrated by application to cation hosts containing multiple aliphatic ether oxygen binding sites. De novo molecule building software, HostDesigner, is interfaced with molecular mechanics software, GMMX, providing a tool for generating and screening millions of potential bidentate building block structures. Enhanced cation binding affinity can be achieved when highly organized building blocks are used to construct macrocyclic hosts.     

A highly potent non-nucleoside adenosine deaminase inhibitor: efficient drug discovery by intentional lead hybridization

We disclose herein the rapid discovery of the first highly potent (Ki = 7.7 nM) non-nucleoside adenosine deaminase (ADA) inhibitor based on the rational hybridization of two structurally distinct leads. Two micromolar inhibitors were discovered by a parallel rational design and random screening program, and individual crystal structures of bovine ADA in complexation with these inhibitors revealed several unknown binding sites and distinct binding modes. Using this information as the starting point, highly effective lead hybridization was achieved in only two structure-based drug design iterations. The conceptual approach illustrated by this example promises to be broadly useful in the search for new chemical entities and can contribute greatly to improve the overall efficiency and speed of drug discovery.     

Novel ligands for a purine riboswitch discovered by RNA-ligand docking

The increasing number of RNA crystal structures enables a structure-based approach to the discovery of new RNA-binding ligands. To develop the poorly explored area of RNA-ligand docking, we have conducted a virtual screening exercise for a purine riboswitch to probe the strengths and weaknesses of RNA-ligand docking. Using a standard protein-ligand docking program with only minor modifications, four new ligands with binding affinities in the micromolar range were identified, including two compounds based on molecular scaffolds not resembling known ligands. RNA-ligand docking performed comparably to protein-ligand docking indicating that this approach is a promising option to explore the wealth of RNA structures for structure-based ligand design.     

Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores

The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.     

Structure-based design technology contour and its application to the design of Renin inhibitors

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.     

De novo structure-based design of bisurea hosts for tetrahedral oxoanion guests

This paper presents a computational approach to the deliberate design of improved host architectures. De novo molecule building software, HostDesigner, is interfaced with molecular mechanics software, GMMX, providing a tool for generating and screening millions of potential structures. The efficacy of this computer-aided design methodology is illustrated with a search for bisurea podands that are structurally organized for complexation with tetrahedral oxoanions.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

Small molecule correctors of F508del-CFTR discovered by structure-based virtual screening

Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability. In silico structure-based screening was performed at the putative binding-sites and a total of 496 candidate compounds from all three sites were tested in functional assays. A total of 15 compounds, representing diverse chemotypes, were identified as F508del folding correctors. This corresponds to a 3% hit rate, ~tenfold higher than hit rates obtained in corresponding high-throughput screening campaigns. The same binding sites also yielded potentiators and, most notably, compounds with a dual corrector-potentiator activity (dual-acting). Compounds harboring both activity types may prove to be better leads for the development of CF therapeutics than either pure correctors or pure potentiators. To the best of our knowledge this is the first report of structure-based discovery of CFTR modulators.     

Addressing the metabolic activation potential of new leads in drug discovery: a case study using ion trap mass spectrometry and tritium labeling techniques

Metabolic activation of drug candidates to electrophilic reactive metabolites that can covalently modify cellular macromolecules may result in acute and/or idiosyncratic immune system-mediated toxicities in humans. This presents a significant potential liability for the future development of these compounds as safe therapeutic agents. We present here an example of an approach where sites of metabolic activation within a new drug candidate series were rapidly identified using online liquid chromatography/multi-stage mass spectrometry on an ion trap mass spectrometer. This was accomplished by trapping the reactive intermediates formed upon incubation of compounds with rat and human liver microsomes as their corresponding glutathione conjugates and mass spectral characterization of these thiol adducts. Based on the structures of the GSH adducts identified, potential sites and mechanisms of bioactivation within the chemical structure were proposed. These metabolism studies were interfaced with iterative structural modifications of the chemical series in order to block these bioactivation sites within the molecule. This strategy led to a significant reduction in the propensity of the compounds to undergo metabolic activation as evidenced by reductions in the irreversible binding of radioactivity to liver microsomal material upon incubation of tritium-labeled compounds with this in vitro system. With the efficiency and throughput achievable with such an approach, it appears feasible to identify and address the metabolic activation potential of new drug leads during routine metabolite identification studies in an early drug discovery setting.     

Molecular modelling prediction of ligand binding site flexibility

We have investigated the efficacy of generating multiple sidechain conformations using a rotamer library in order to find the experimentally observed ligand binding site conformation of a protein in the presence of a bound ligand. We made use of a recently published algorithm that performs an exhaustive conformational search using a rotamer library to enumerate all possible sidechain conformations in a binding site. This approach was applied to a dataset of proteins whose structures were determined by X-ray and NMR methods. All chosen proteins had two or more structures, generally involving different bound ligands. By taking one of these structures as a reference, we were able in most cases to successfully reproduce the experimentally determined conformations of the other structures, as well as to suggest alternative low-energy conformations of the binding site. In those few cases where this procedure failed, we observed that the bound ligand had induced a high-energy conformation of the binding site. These results suggest that for most proteins that exhibit limited backbone motion, ligands tend to bind to low energy conformations of their binding sites. Our results also reveal that it is possible in most cases to use a rotamer search-based approach to predict alternative low-energy protein binding site conformations that can be used by different ligands. This opens the possibility of incorporating alternative binding site conformations to improve the efficacy of docking and structure-based drug design algorithms.     

Structure-based discovery and in-parallel optimization of novel competitive inhibitors of thymidylate synthase.

BACKGROUND: The substrate sites of enzymes are attractive targets for structure-based inhibitor design. Two difficulties hinder efforts to discover and elaborate new (nonsubstrate-like) inhibitors for these sites. First, novel inhibitors often bind at nonsubstrate sites. Second, a novel scaffold introduces chemistry that is frequently unfamiliar, making synthetic elaboration challenging. RESULTS: In an effort to discover and elaborate a novel scaffold for a substrate site, we combined structure-based screening with in-parallel synthetic elaboration. These techniques were used to find new inhibitors that bound to the folate site of Lactobacillus casei thymidylate synthase (LcTS), an enzyme that is a potential target for proliferative diseases, and is highly studied. The available chemicals directory was screened, using a molecular-docking computer program, for molecules that complemented the three-dimensional structure of this site. Five high-ranking compounds were selected for testing. Activity and docking studies led to a derivative of one of these, dansyltyrosine (Ki 65 microM). Using solid-phase in-parallel techniques 33 derivatives of this lead were synthesized and tested. These analogs are dissimilar to the substrate but bind competitively with it. The most active analog had a Ki of 1.3 microM. The tighter binding inhibitors were also the most specific for LcTS versus related enzymes. CONCLUSIONS: TS can recognize inhibitors that are dissimilar to, but that bind competitively with, the folate substrate. Combining structure-based discovery with in-parallel synthetic techniques allowed the rapid elaboration of this series of compounds. More automated versions of this approach can be envisaged.     

Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH)

A structure-based drug discovery method is described that incorporates target flexibility through the use of an ensemble of protein conformations. The approach was applied to fatty acid amide hydrolase (FAAH), a key deactivating enzyme in the endocannabinoid system. The resultant dynamic pharmacophore models are rapidly able to identify known FAAH inhibitors over drug-like decoys. Different sources of FAAH conformational ensembles were explored, with both snapshots from molecular dynamics simulations and a group of X-ray structures performing well. Results were compared to those from docking and pharmacophore models generated from a single X-ray structure. Increasing conformational sampling consistently improved the pharmacophore models, emphasizing the importance of incorporating target flexibility in structure-based drug design.     

Rapid protein-ligand costructures using chemical shift perturbations

Structure-based drug discovery requires the iterative determination of protein-ligand costructures in order to improve the binding affinity and selectivity of potential drug candidates. In general, X-ray and NMR structure determination methods are time consuming and are typically the limiting factor in the drug discovery process. The application of molecular docking simulations to filter and evaluate drug candidates has become a common method to improve the throughput and efficiency of structure-based drug design. Unfortunately, molecular docking methods suffer from common problems that include ambiguous ligand conformers or failure to predict the correct docked structure. A rapid approach to determine accurate protein-ligand costructures is described based on NMR chemical shift perturbation (CSP) data routinely obtained using 2D 1H-15N HSQC spectra in high-throughput ligand affinity screens. The CSP data is used to both guide and filter AutoDock calculations using our AutoDockFilter program. This method is demonstrated for 19 distinct protein-ligand complexes where the docked conformers exhibited an average rmsd of 1.17 +/- 0.74 A relative to the original X-ray structures for the protein-ligand complexes.     

A searchable database for comparing protein-ligand binding sites for the analysis of structure-function relationships

The rapid expansion of structural information for protein-ligand binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross reactivity and toxicity. We have developed a large database of ligand binding sites extracted automatically from the Protein Data Bank. This has been combined with a method for calculating binding site similarity based on geometric hashing to create a relational database for the retrieval of site similarity and binding site superposition. It contains an all-against-all comparison of binding sites and holds known protein-ligand binding sites, which are made accessible to data mining. Here we demonstrate its utility in two structure-based applications: in determining site similarity and in aiding the derivation of a receptor-based pharmacophore model. The database is available from http://www.bioinformatics.leeds.ac.uk/sb/.     

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.     

Facile synthesis of nanoporous CuS nanospheres for high-performance supercapacitor electrodes

In recent years, development of high-performance supercapacitor electrode materials has stimulated a great deal of scientific research. The electrochemical performance of a supercapacitor strongly depends on its material structures. Herein, we report a simple strategy for high-performance supercapacitors by building pseudocapacitive CuS nanospheres with nanoporous structures, nanosized walls(10 nm) and relatively large specific surface area of 65 m~2/g. This electrode demonstrates excellent electrochemical performance including a maximum specific capacitance of 814 F/g at 1 A/g, significant rate capability of 42% capacitance retention at an ultrafast rate of 50 A/g, and outstanding long-term cycling stability at various current densities. The remarkable electrochemical performance of as-prepared nanoporous CuS nanospheres electrode has been attributed to its unique structures that plays a key role in providing short ion and electron diffusion pathways, facilitated ion transport and more active sites for electrochemical reactions. This work sheds a new light on the metal sulfides design philosophy, and demonstrates that nanoporous CuS nanospheres electrode is a promising candidate for application in high-performance supercapacitors.     

The process of structure-based drug design

The field of structure-based drug design is a rapidly growing area in which many successes have occurred in recent years. The explosion of genomic, proteomic, and structural information has provided hundreds of new targets and opportunities for future drug lead discovery. This review summarizes the process of structure-based drug design and includes, primarily, the choice of a target, the evaluation of a structure of that target, the pivotal questions to consider in choosing a method for drug lead discovery, and evaluation of the drug leads. Key principles in the field of structure-based drug design will be illustrated through a case study that explores drug design for AmpC beta-lactamase.     

Structural chemistry of a green fluorescent protein Zn biosensor

We designed a green fluorescent protein mutant (BFPms1) that preferentially binds Zn(II) (enhancing fluorescence intensity) and Cu(II) (quenching fluorescence) directly to a chromophore ligand that resembles a dipyrrole unit of a porphyrin. Crystallographic structure determination of apo, Zn(II)-bound, and Cu(II)-bound BFPms1 to better than 1.5 A resolution allowed us to refine metal centers without geometric restraints, to calculate experimental standard uncertainty errors for bond lengths and angles, and to model thermal displacement parameters anisotropically. The BFPms1 Zn(II) site (KD = 50 muM) displays distorted trigonal bipyrimidal geometry, with Zn(II) binding to Glu222, to a water molecule, and tridentate to the chromophore ligand. In contrast, the BFPms1 Cu(II) site (KD = 24 muM) exhibits square planar geometry similar to metalated porphyrins, with Cu(II) binding to the chromophore chelate and Glu222. The apo structure reveals a large electropositive region near the designed metal insertion channel, suggesting a basis for the measured metal cation binding kinetics. The preorganized tridentate ligand is accommodated in both coordination geometries by a 0.4 A difference between the Zn and Cu positions and by distinct rearrangements of Glu222. The highly accurate metal ligand bond lengths reveal different protonation states for the same oxygen bound to Zn vs Cu, with implications for the observed metal ion specificity. Crystallographic anisotropic thermal factor analysis validates metal ion rigidification of the chromophore in enhancement of fluorescence intensity upon Zn(II) binding. Thus, our high-resolution structures reveal how structure-based design has effectively linked selective metal binding to changes in fluorescent properties. Furthermore, this protein Zn(II) biosensor provides a prototype suitable for further optimization by directed evolution to generate metalloprotein variants with desirable physical or biochemical properties.     

Two-metal ion, Ni(II) and Cu(II), binding alpha-helical coiled coil peptide

Metalloproteins are an attractive target for de novo design. Usually, natural proteins incorporate two or more (hetero- or homo-) metal ions into their frameworks to perform their functions, but the design of multiple metal-binding sites is usually difficult to achieve. Here, we undertook the de novo engineering of heterometal-binding sites, Ni(II) and Cu(II), into a designed coiled coil structure based on an isoleucine zipper (IZ) peptide. Previously, we described two peptides, IZ-3adH and IZ-3aH. The former has two His residues and forms a triple-stranded coiled coil after binding Ni(II), Zn(II), or Cu(II). The latter has one His residue, which allowed binding with Cu(II) and Zn(II), but not with Ni(II). On the basis of these properties, we newly designed IZ(5)-2a3adH as a heterometal-binding peptide. This peptide can bind Cu(II) and Ni(II) simultaneously in the hydrophobic core of the triple-stranded coiled coil. The first metal ion binding induced the folding of the peptide into the triple-stranded coiled coil, thereby promoting the second metal ion binding. This is the first example of a peptide that can bind two different metal ions. This construction should provide valuable insights for the de novo design of metalloproteins.