High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning

Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential druggable target must satisfy several criteria, including disease association, cellular accessibility, binding pockets for drug-like molecules, and minimal cross-reactivity. While several methods exist for prediction of druggable proteins, they cannot be repurposed for RNAs due to fundamental differences in their binding modality. Taking all these constraints into account, a new structure-based model, Druggable RNA-Ligand binding Pocket Selector (DRLiPS), is developed here to predict binding site-level druggability of any given RNA target. A novel strategy for sampling negative binding sites in RNA structures using three parallel approaches is demonstrated here to improve model specificity: backbone motif search, exhaustive pocket prediction, and blind docking. An external blind test dataset has also been curated to showcase the model's generalizability to both experimental and modelled apo state RNA structures. DRLiPS has achieved an F1-score of 0.70, precision of 0.61, specificity of 0.89, and recall of 0.73 on this external test dataset, outperforming two existing methods, DrugPred_RNA and RNACavityMiner. Further analysis indicates that the features selected for model-building generalize well to both apo and holo states with a backbone RMSD tolerance of 3 Å. It can also predict the effect of binding site single point mutations on druggability, which can aid in optimizing synthetic RNA aptamers for small molecule recognition. The DRLiPS model is freely accessible at https://web.iitm.ac.in/bioinfo2/DRLiPS/.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database.

Hypothesis-generating proteome perturbation to identify NEU-4438 and acoziborole modes of action in the African Trypanosome

NEU-4438 is a lead for the development of drugs against Trypanosoma brucei, which causes human African trypanosomiasis. Optimized with phenotypic screening, targets of NEU-4438 are unknown. Herein, we present a cell perturbome workflow that compares NEU-4438's molecular modes of action to those of SCYX-7158 (acoziborole). Following a 6 h perturbation of trypanosomes, NEU-4438 and acoziborole reduced steady-state amounts of 68 and 92 unique proteins, respectively. After analysis of proteomes, hypotheses formulated for modes of action were tested: Acoziborole and NEU-4438 have different modes of action. Whereas NEU-4438 prevented DNA biosynthesis and basal body maturation, acoziborole destabilized CPSF3 and other proteins, inhibited polypeptide translation, and reduced endocytosis of haptoglobin-hemoglobin. These data point to CPSF3-independent modes of action for acoziborole. In case of polypharmacology, the cell-perturbome workflow elucidates modes of action because it is target-agnostic. Finally, the workflow can be used in any cell that is amenable to proteomic and molecular biology experiments.

Quinolinate Synthase: An Example of the Roles of the Second and Outer Coordination Spheres in Enzyme Catalysis

The activation energy barrier of biochemical reactions is normally lowered by an enzyme catalyst, which directly helps the weakening of the bond(s) to be broken. In many metalloenzymes, this is a first coordination sphere effect. Besides having a direct catalytic action, enzymes can fix their reactive groups and substrates so that they are optimally positioned and also modify the water activity in the system. They can either activate substrates prior to their reaction or bind preactivated substrates, thereby drastically reducing local entropic effects. The latter type is well represented by some bisubstrate reactions, where they have been defined as "entropic traps". These can be described as "second coordination sphere" processes, but enzymes can also control the reactivity beyond this point through local conformational changes belonging to an "outer coordinate sphere" that can be modulated by substrate binding. We have chosen the [4Fe-4S] cluster-dependent enzyme quinolinate synthase to illustrate each one of these processes. In addition, this very old metalloenzyme shows low in vitro substrate binding specificity, atypical reactivity that produces dead-end products, and a unique modulation of its active site volume.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.