Identification of indole-based natural compounds as inhibitors of PARP-1 against triple-negative breast cancer: a computational study

Triple-negative breast cancer (TNBC) is the most aggressive kind of breast cancer known to mankind. It is a heterogeneous disease that is formed due to the missing estrogen, progesterone and human epidermal growth factor 2 receptors. Poly(ADP-ribose) polymerase-1 (PARP-1) protein helps in the development of TNBC by repairing the cancer cells, which proliferate and spread metastatically. To determine the potential PARP-1 inhibitors (PARPi), 0.2 million natural products from Universal Natural Product Database were screened using molecular docking and six hit compounds were selected based on their binding affinity towards PARP-1. The bio-availability and drug-like properties of these natural products were evaluated using ADMET analysis. Molecular dynamics simulations were conducted for these complexes for 200 ns to examine their structural stability and dynamic behaviour and further compared with the complex of talazoparib (TALA), an FDA-approved PARPi. Using MM/PBSA calculations, we conclude that the complexes HIT-3 and HIT-5 (-25.64 and -23.14 kcal/mol, respectively) show stronger binding energies with PARP-1 than TALA with PARP-1 (-10.74 kcal/mol). Strong interactions were observed between the compounds and hotspot residues, Asp770, Ala880, Tyr889, Tyr896, Ala898, Asp899 and Tyr907, of PARP-1 due to the existence of various types of non-covalent interactions between the compounds and PARP-1. This research offers critical information about PARPi, which could potentially be incorporated into the treatment of TNBC. Moreover, these findings were validated by comparing them with an FDA-approved PARPi.

Automated benchmarking of combined protein structure and ligand conformation prediction

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

New prediction categories in CASP15

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Perspective: Structure determination of protein-ligand complexes at room temperature using X-ray diffraction approaches

The interaction between macromolecular proteins and small molecule ligands is an essential component of cellular function. Such ligands may include enzyme substrates, molecules involved in cellular signalling or pharmaceutical drugs. Together with biophysical techniques used to assess the thermodynamic and kinetic properties of ligand binding to proteins, methodology to determine high-resolution structures that enable atomic level interactions between protein and ligand(s) to be directly visualised is required. Whilst such structural approaches are well established with high throughput X-ray crystallography routinely used in the pharmaceutical sector, they provide only a static view of the complex. Recent advances in X-ray structural biology methods offer several new possibilities that can examine protein-ligand complexes at ambient temperature rather than under cryogenic conditions, enable transient binding sites and interactions to be characterised using time-resolved approaches and combine spectroscopic measurements from the same crystal that the structures themselves are determined. This Perspective reviews several recent developments in these areas and discusses new possibilities for applications of these advanced methodologies to transform our understanding of protein-ligand interactions.

Identification of Novel Inhibitors of a Plant Group A Protein Phosphatase Type 2C Using a Combined In Silico and Biochemical Approach

Type 2C protein phosphatases (PP2Cs) of group A play a significant role in the regulation of various processes in plants including growth, development, ion transport, and stress acclimation. In this study, we selected potential PP2C group A inhibitors using a structure-based virtual screening method followed by biochemical and in vitro validation. Over twenty million chemical compounds from the ZINC database were used for docking studies. The precision of the calculations was increased by an induced-fit docking protocol and the molecular mechanics/generalized Born surface area (MM/GBSA) method, which yielded approximate values for the binding energy of the protein-ligand complex. After clustering and ranking their activity, the top-ranking compounds were tested against PP2C group A members in vitro and their in vivo activity was also explored. Phosphatase activity assays identified two compounds with significant inhibitory activity against ABI1 protein ranging from around 57 to 91% at a concentration of 100 μM. Importantly, this in vitro activity correlated well with in vivo inhibition of seed germination, as expected for PP2C inhibitors. The results should promote the design of novel inhibitors with improved potency against ABI1-like and other PP2Cs that might be used in agriculture for the protection of crops against stress.

Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB)

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein-protein, protein-DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

Protein X-ray Crystallography and Drug Discovery

With the advent of structural biology in the drug discovery process, medicinal chemists gained the opportunity to use detailed structural information in order to progress screening hits into leads or drug candidates. X-ray crystallography has proven to be an invaluable tool in this respect, as it is able to provide exquisitely comprehensive structural information about the interaction of a ligand with a pharmacological target. As fragment-based drug discovery emerged in the recent years, X-ray crystallography has also become a powerful screening technology, able to provide structural information on complexes involving low-molecular weight compounds, despite weak binding affinities. Given the low numbers of compounds needed in a fragment library, compared to the hundreds of thousand usually present in drug-like compound libraries, it now becomes feasible to screen a whole fragment library using X-ray crystallography, providing a wealth of structural details that will fuel the fragment to drug process. Here, we review theoretical and practical aspects as well as the pros and cons of using X-ray crystallography in the drug discovery process.

Halogen bonding in halocarbon-protein complexes and computational tools for rational drug design

Introduction: Halogens have a prominent role in drug design. Often used as a mean to improve ADME properties, they are also becoming a tool in protein-ligand recognition given their ability to form a non-covalent interaction, termed halogen bond, where halogens act as electrophilic species interacting with electron-rich partners. Rational drug design of halogen-bonding lead molecules requires an accurate description of halocarbon-protein complexes by computational tools though not all methods are able to tackle this non-covalent interaction. Areas covered: The authors present a review of computational methodologies that can be used to properly describe halogen bonds in the context of protein-ligand complexes, providing also insights on how these methods can be used in the context of computer-aided drug design. Expert opinion: Although in the last few years many computational tools, ranging from fast screening methods to the more expensive QM calculations, have been developed to tackle the halogen bonding phenomenon, they are not yet standard in the literature. This will eventually change as official software distributions are including support for halogen bonding in their methods. Tackling desolvation of halogenated species seems to be a good strategy to improve the accuracy of computational methods, that will be more commonly used prior to laboratory work in the future.

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (K_a, K_d, K_i, IC₅₀) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions

The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.

Critical Evaluation of Native Electrospray Ionization Mass Spectrometry for Fragment-Based Screening

Fragment-based screening presents a promising alternative to high-throughput screening and has gained great attention in recent years. So far, only a few studies have discussed mass spectrometry as a screening technology for fragments. Herein, we report the application of native electrospray ionization mass spectrometry (MS) for screening defined sets of fragments against four different target proteins. Fragments were selected from a primary screening conducted with a thermal shift assay (TSA) and represented different binding categories. Our data indicated that, beside specific complex formation, many fragments show extensive multiple binding and also charge-state shifts. Both of these factors complicate automated data analysis and decrease the attractiveness of native MS as a primary screening tool for fragments. A comparison of the hits identified by native MS and TSA showed good agreement for two of the proteins. Furthermore, we discuss general challenges, including the determination of an optimal fragment concentration and the question of how to rank fragment hits according to their affinity. In conclusion, we consider native MS to be a highly valuable tool for the validation and deeper investigation of promising fragment hits rather than a method for primary screening.

Exploring the (Very Flat) Potential Energy Landscape of R-Br⋅⋅⋅π Interactions with Accurate CCSD(T) and SAPT Techniques

Halogen bonds involving an aromatic moiety as an acceptor, otherwise known as R-X⋅⋅⋅π interactions, have increasingly been recognized as being important in materials and in protein-ligand complexes. These types of interactions have been the subject of many recent investigations, but little is known about the ways in which the strengths of R-X⋅⋅⋅π interactions vary as a function of the relative geometries of the interacting pairs. Here we use the accurate CCSD(T) and SAPT2+3δMP2 methods to investigate the potential energy landscapes for systems of HBr, HCCBr, and NCBr complexed with benzene. It is found that only the separation between the complexed molecules have a strong effect on interaction strength while other geometric parameters, such as tilting and shifting R-Br⋅⋅⋅π donor relative to the benzene plane, affect these interactions only mildly. Importantly, it is found that the C_6v (T-shaped) configuration is not the global minimum for any of the dimers investigated.

Landscape of protein-small ligand binding modes

Elucidating the mechanisms of specific small-molecule (ligand) recognition by proteins is a long-standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein-ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all-against-all comparison of 20,040 protein-ligand complexes provided the landscape of the protein-ligand binding modes and its relationships with protein- and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R(2)  = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein-ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.

Cofactor-binding sites in proteins of deviating sequence: comparative analysis and clustering in torsion angle, cavity, and fold space

Small molecules are recognized in protein-binding pockets through surface-exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein-ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise to unexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein-cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein-binding pockets, and the local folding patterns next to the cofactor-binding site. State-of-the-art clustering techniques have been applied to group the different protein-cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process.