What can we learn from the evolution of protein-ligand interactions to aid the design of new therapeutics?

Revealing Propolis Potential Activity on Inhibiting Estrogen Receptor and Heat Shock Protein 90 Overexpressed in Breast Cancer by Bioinformatics Approaches

Breast cancer is the most commonly diagnosed cancer globally, with the highest incidence of breast cancer occurring in Asian countries including Indonesia. Among the types of breast cancer, the estrogen receptor (ER)-positive subtype which is prominent with estrogen receptor alpha (ERα) and heat shock protein 90 (HSP90) overexpression genes becomes the most prevalent than the others, approximately 75% of all breast cancer cases. ERα and HSP90 play a role in breast cancer activities including breast tumor growth, invasion, and metastasis mechanism. Propolis, a natural bee product, has been explored for its anticancer activity. However, there is lack of studies that evaluated the potential inhibitor from propolis compounds to the ERα and HSP90 proteins. Therefore, this article focuses on examining the correlation between ERα and HSP90's role in breast cancer and investigating the potential of 93 unique propolis compositions in inhibiting these genes in breast cancer using in silico approaches. This study revealed the positive correlation between ERα and HSP90 genes in breast cancer disease development. Furthermore, we also found novel potential bioactive compounds of propolis against breast cancer through binding with ERα and HSP90; they were 3',4',7-trihydroxyisoflavone and baicalein-7-O-β-D glucopyranoside, respectively. Further research on these compounds is needed to elucidate deeper mechanisms and activity in the real biological system to develop new breast cancer drug treatments.

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

The development of small-molecule inhibitors targeting CD47

Immunotherapy has become an indispensable part of cancer treatment. A pivotal phagocytosis checkpoint, named cluster of differentiation 47 (CD47), which functions as 'don't eat me' signal to protect cells from phagocytosis upon interaction with signal regulatory protein alpha (SIRPα) on macrophages, has recently attracted much attention. Numerous antibodies targeting the CD47/SIRPα axis have shown encouraging efficacy in clinical trials. Meanwhile, studies on small-molecule inhibitors that interfere with CD47/SIRPα interaction or regulate CD47 expression are also in full swing. In this review, we summarize the small-molecule inhibitors interrupting the binding of CD47/SIRPα and regulating CD47 at the transcriptional, translational, and post-translational modification (PTM) levels. We provide perspectives and strategies for targeting the CD47/SIRPα phagocytosis checkpoint.

TIBLE: a web-based, freely accessible resource for small-molecule binding data for mycobacterial species

Database URL

http://www-cryst.bioc.cam.ac.uk/tible/.

A systematic analysis of atomic protein-ligand interactions in the PDB

As the protein databank (PDB) recently passed the cap of 123 456 structures, it stands more than ever as an important resource not only to analyze structural features of specific biological systems, but also to study the prevalence of structural patterns observed in a large body of unrelated structures, that may reflect rules governing protein folding or molecular recognition. Here, we compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins - 6444 of which have experimental binding affinity - representing 750 873 protein-ligand atomic interactions, and analyzed the frequency, geometry and impact of each interaction type. We find that hydrophobic interactions are generally enriched in high-efficiency ligands, but polar interactions are over-represented in fragment inhibitors. While most observations extracted from the PDB will be familiar to seasoned medicinal chemists, less expected findings, such as the high number of C-H···O hydrogen bonds or the relatively frequent amide-π stacking between the backbone amide of proteins and aromatic rings of ligands, uncover underused ligand design strategies.

Molecular inflation, attrition and the rule of five

Physicochemical properties underlie all aspects of drug action and are critical for solubility, permeability and successful formulation. Specific physicochemical properties shown to be relevant to oral drugs are size, lipophilicity, ionisation, hydrogen bonding, polarity, aromaticity and shape. The rule of 5 (Ro5) and subsequent studies have raised awareness of the importance of compound quality amongst bioactive molecules. Lipophilicity, probably the most important physical property of oral drugs, has on average changed little over time in oral drugs, until increases in drugs published after 1990. In contrast other molecular properties such as average size have increased significantly. Factors influencing property inflation include the targets pursued, where antivirals frequently violate the Ro5, risk/benefit considerations, and variable drug discovery practices. The compounds published in patents from the pharmaceutical industry are on average larger, more lipophilic and less complex than marketed oral drugs. The variation between individual companies' patented compounds is due to different practices and not to the targets pursued. Overall, there is demonstrable physical property attrition in moving from patents to candidate drugs to marketed drugs. The pharmaceutical industry's recent poor productivity has been due, in part, to progression of molecules that are unable to unambiguously test clinical efficacy, and attrition can therefore be improved by ensuring candidate drug quality is 'fit for purpose.' The combined ligand efficiency (LE) and lipophilic ligand efficiency (LLE) values of many marketed drugs are optimised relative to other molecules acting at the same target. Application of LLE in optimisation can help identify improved leads, even with challenging targets that seem to require lipophilic ligands. Because of their targets, some projects may need to pursue 'beyond Ro5' physicochemical space; such projects will require non-standard lead generation and optimisation and should not dominate in a well-balanced portfolio. Compound quality is controllable by lead selection and optimisation and should not be a cause of clinical failure.

Avoiding drug resistance through extended drug target interfaces: a case for stapled peptides

Cancer drugs often fail due to the emergence of clinical resistance. This can manifest through mutations in target proteins that selectively exclude drug binding whilst retaining aberrant function. A priori knowledge of resistance-inducing mutations is therefore important for both drug design and clinical surveillance. Stapled peptides represent a novel class of antagonists capable of inhibiting therapeutically relevant protein-protein interactions. Here, we address the important question of potential resistance to stapled peptide inhibitors. HDM2 is the critical negative regulator of p53, and is often overexpressed in cancers that retain wild-type p53 function. Interrogation of a large collection of randomly mutated HDM2 proteins failed to identify point mutations that could selectively abrogate binding by a stapled peptide inhibitor (PM2). In contrast, the same interrogation methodology has previously uncovered point mutations that selectively inhibit binding by Nutlin, the prototypical small molecule inhibitor of HDM2. Our results demonstrate both the high level of structural p53 mimicry employed by PM2 to engage HDM2, and the potential resilience of stapled peptide antagonists to mutations in target proteins. This inherent feature could reduce clinical resistance should this class of drugs enter the clinic.

Identifying Interactions that Determine Fragment Binding at Protein Hotspots

Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods do little to suggest which interactions within a pocket are the most important for binding. Here we illustrate a method that samples atomic hotspots with simple molecular probes to produce fragment hotspot maps. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. The fragment hotspot map calculation is validated using experimental binding positions of 21 fragments and subsequent lead molecules. The ligands are found in high scoring areas of the fragment hotspot maps, with fragment atoms having a median percentage rank of 97%. Protein kinase B and pantothenate synthetase are examined in detail. In each case, the fragment hotspot maps are able to rationalize a Free-Wilson analysis of SAR data from a fragment-based drug design project.

Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces

Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.

Focusing on shared subpockets - new developments in fragment-based drug discovery

INTRODUCTION

Protein-protein interactions (PPIs) are important targets for understanding fundamental biology and for the development of therapeutic agents. Based on different physicochemical properties, numerous pieces of software (e.g., POCKETQUERY, ANCHORQUERY and FTMap) have been reported to find pockets on protein surfaces and have applications in facilitating the design and discovery of small-molecular-weight compounds that bind to these pockets.

AREAS COVERED

The authors discuss a pocket-centric method of analyzing PPI interfaces, which prioritize their pockets for small-molecule drug discovery and the importance of multicomponent reaction chemistry as starting points for undruggable targets. The authors also provide their perspectives on the field.

EXPERT OPINION

Only the tight interplay of efficient computational methods capable of screening a large chemical space and fast synthetic chemistry will lead to progress in the rational design of PPI antagonists in the future. Early drug discovery platforms will also benefit from efficient rapid feedback loops from early clinical research back to molecular design and the medicinal chemistry bench.

Molecular Property Design: Does Everyone Get It?

The principles of molecular property optimization in drug design have been understood for decades, yet much drug discovery activity today is conducted at the periphery of historical druglike property space. Lead optimization trajectories aimed at reducing physicochemical risk, assisted by ligand efficiency metrics, could help to reduce clinical attrition rates.

Flexibility and small pockets at protein-protein interfaces: New insights into druggability

The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein-protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein-protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of "orthosteric", competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein-protein interaction interfaces for which successful chemical modulators are being developed.

Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment

Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches. Several methods have been developed to characterize and compare protein-ligand interaction patterns. Usually implemented as fingerprints, these are mainly used for post processing docking scores and (off-)target prediction. In the latter application, interaction profiles detect similarities in the bound interactions of different ligands and thus identify essential interactions between a protein and its small molecule ligands. Interaction pattern similarity correlates with binding site similarity and is thus contributing to a higher precision in binding site similarity assessment of proteins with distinct global structure. This renders it valuable for existing drug repositioning approaches in structural bioinformatics. Current methods to characterize and compare structure-based interaction patterns - both for protein-small-molecule and protein-protein interactions - as well as their potential in target prediction will be reviewed in this article. The question of how the set of interaction types, flexibility or water-mediated interactions, influence the comparison of interaction patterns will be discussed. Due to the wealth of protein-ligand structures available today, predicted targets can be ranked by comparing their ligand interaction pattern to patterns of the known target. Such knowledge-based methods offer high precision in comparison to methods comparing whole binding sites based on shape and amino acid physicochemical similarity.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Inhibition of nutlin-resistant HDM2 mutants by stapled peptides

Pharmacological modulation of p53 activity is an attractive therapeutic strategy in cancers with wild-type p53. Presently in clinical trials, the small molecule Nutlin-3A competitively binds to HDM2, a key negative regulator of p53 and blocks its activity. We have described resistance mutations in HDM2 that selectively reduce affinity for Nutlin but not p53. In the present communication, we show that stapled peptides targeting the same region of HDM2 as Nutlin are refractory to these mutations, and display reduced discrimination between the wild-type and mutant HDM2s with regards to functional abrogation of interaction with p53. The larger interaction footprint afforded by stapled peptides suggests that this class of ligands may prove comparatively more resilient to acquired resistance in a clinical setting.