Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

Inhibition of FOXP3 by stapled alpha-helical peptides dampens regulatory T cell function

Therapies and preclinical probes designed to drug and better understand the specific functions of intracellular protein–protein interactions (PPIs) remain an area of unmet need. This study describes the development of prototype therapeutics against the FOXP3 homodimer, a PPI essential for regulatory T cell suppressive capacity. We demonstrate that hydrocarbon stapled peptides designed to block this interaction can dampen regulatory T cell (Treg cell) suppressive function and lead to genetic signatures of immune reactivation. This work provides strong scientific justification for continued development of FOXP3-specific peptide-based inhibitors and provides mechanistic insights into the design and delivery of specific inhibitors of the coiled-coil region of FOXP3. These studies ultimately could lead to new immunotherapeutic strategies to amplify immune responsiveness in a number of settings.

Despite continuing advances in the development of novel cellular-, antibody-, and chemotherapeutic-based strategies to enhance immune reactivity, the presence of regulatory T cells (Treg cells) remains a complicating factor for their clinical efficacy. To overcome dosing limitations and off-target effects from antibody-based Treg cell deletional strategies or small molecule drugging, we investigated the ability of hydrocarbon stapled alpha-helical (SAH) peptides to target FOXP3, the master transcription factor regulator of Treg cell development, maintenance, and suppressive function. Using the crystal structure of the FOXP3 homodimer as a guide, we developed SAHs in the likeness of a portion of the native FOXP3 antiparallel coiled-coil homodimerization domain (SAH-FOXP3) to block this key FOXP3 protein-protein interaction (PPI) through molecular mimicry. We describe the design, synthesis, and biochemical evaluation of single- and double-stapled SAHs covering the entire coiled-coil expanse. We show that lead SAH-FOXP3s bind FOXP3, are cell permeable and nontoxic to T cells, induce dose-dependent transcript and protein level alterations of FOXP3 target genes, impede Treg cell function, and lead to Treg cell gene expression changes in vivo consistent with FOXP3 dysfunction. These results demonstrate a proof of concept for rationally designed FOXP3-directed peptide therapeutics that could be used as approaches to amplify endogenous immune responsiveness.

Synergistic drug combinations from electronic health records and gene expression

Objective

Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.

Method

We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.

Results

From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.

Conclusions

This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

PDID: database of molecular-level putative protein-drug interactions in the structural human proteome

MOTIVATION

Many drugs interact with numerous proteins besides their intended therapeutic targets and a substantial portion of these interactions is yet to be elucidated. Protein-Drug Interaction Database (PDID) addresses incompleteness of these data by providing access to putative protein-drug interactions that cover the entire structural human proteome.

RESULTS

PDID covers 9652 structures from 3746 proteins and houses 16 800 putative interactions generated from close to 1.1 million accurate, all-atom structure-based predictions for several dozens of popular drugs. The predictions were generated with three modern methods: ILbind, SMAP and eFindSite. They are accompanied by propensity scores that quantify likelihood of interactions and coordinates of the putative location of the binding drugs in the corresponding protein structures. PDID complements the current databases that focus on the curated interactions and the BioDrugScreen database that relies on docking to find putative interactions. Moreover, we also include experimentally curated interactions which are linked to their sources: DrugBank, BindingDB and Protein Data Bank. Our database can be used to facilitate studies related to polypharmacology of drugs including repurposing and explaining side effects of drugs.

AVAILABILITY AND IMPLEMENTATION

PDID database is freely available at http://biomine.ece.ualberta.ca/PDID/.

Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space

One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.

2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

Trinucleotide repeats: a structural perspective

Trinucleotide repeat (TNR) expansions are present in a wide range of genes involved in several neurological disorders, being directly involved in the molecular mechanisms underlying pathogenesis through modulation of gene expression and/or the function of the RNA or protein it encodes. Structural and functional information on the role of TNR sequences in RNA and protein is crucial to understand the effect of TNR expansions in neurodegeneration. Therefore, this review intends to provide to the reader a structural and functional view of TNR and encoded homopeptide expansions, with a particular emphasis on polyQ expansions and its role at inducing the self-assembly, aggregation and functional alterations of the carrier protein, which culminates in neuronal toxicity and cell death. Detail will be given to the Machado-Joseph Disease-causative and polyQ-containing protein, ataxin-3, providing clues for the impact of polyQ expansion and its flanking regions in the modulation of ataxin-3 molecular interactions, function, and aggregation.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Virus-host interactomes--antiviral drug discovery

One of the key questions in virology is how viruses, encoding relatively few genes, gain temporary or constant control over their hosts. To understand pathogenicity of a virus it is important to gain knowledge on the function of the individual viral proteins in the host cell, on their interactions with viral and cellular proteins and on the consequences of these interactions on cellular signaling pathways. A combination of transcriptomics, proteomics, high-throughput technologies and the bioinformatical analysis of the respective data help to elucidate specific cellular antiviral drug target candidates. In addition, viral and human interactome analyses indicate that different viruses target common, central human proteins for entering cellular signaling pathways and machineries which might constitute powerful broad-spectrum antiviral targets.

Using a fragment-based approach to target protein-protein interactions

The ability to identify inhibitors of protein-protein interactions represents a major challenge in modern drug discovery and in the development of tools for chemical biology. In recent years, fragment-based approaches have emerged as a new methodology in drug discovery; however, few examples of small molecules that are active against chemotherapeutic targets have been published. Herein, we describe the fragment-based approach of targeting the interaction between the tumour suppressor BRCA2 and the recombination enzyme RAD51; it makes use of a screening pipeline of biophysical techniques that we expect to be more generally applicable to similar targets. Disruption of this interaction in vivo is hypothesised to give rise to cellular hypersensitivity to radiation and genotoxic drugs. We have used protein engineering to create a monomeric form of RAD51 by humanising a thermostable archaeal orthologue, RadA, and used this protein for fragment screening. The initial fragment hits were thoroughly validated biophysically by isothermal titration calorimetry (ITC) and NMR techniques and observed by X-ray crystallography to bind in a shallow surface pocket that is occupied in the native complex by the side chain of a phenylalanine from the conserved FxxA interaction motif found in BRCA2. This represents the first report of fragments or any small molecule binding at this protein-protein interaction site.

Dr. PIAS 2.0: an update of a database of predicted druggable protein-protein interactions

Druggable Protein–protein Interaction Assessment System (Dr. PIAS) is a database of druggable protein–protein interactions (PPIs) predicted by our support vector machine (SVM)-based method. Since the first publication of this database, Dr. PIAS has been updated to version 2.0. PPI data have been increased considerably, from 71 500 to 83 324 entries. As the new positive instances in our method, 4 PPIs and 10 tertiary structures have been added. This addition increases the prediction accuracy of our SVM classifier in comparison with the previous classifier, despite the number of added PPIs and structures is small. We have introduced the novel concept of ‘similar positives’ of druggable PPIs, which will help researchers discover small compounds that can inhibit predicted druggable PPIs. Dr. PIAS will aid the effective search for druggable PPIs from a mine of interactome data being rapidly accumulated. Dr. PIAS 2.0 is available at http://www.drpias.net.

Database URL: http://www.drpias.net.

Biophysical and computational fragment-based approaches to targeting protein-protein interactions: applications in structure-guided drug discovery

Drug discovery has classically targeted the active sites of enzymes or ligand-binding sites of receptors and ion channels. In an attempt to improve selectivity of drug candidates, modulation of protein-protein interfaces (PPIs) of multiprotein complexes that mediate conformation or colocation of components of cell-regulatory pathways has become a focus of interest. However, PPIs in multiprotein systems continue to pose significant challenges, as they are generally large, flat and poor in distinguishing features, making the design of small molecule antagonists a difficult task. Nevertheless, encouragement has come from the recognition that a few amino acids - so-called hotspots - may contribute the majority of interaction-free energy. The challenges posed by protein-protein interactions have led to a wellspring of creative approaches, including proteomimetics, stapled α-helical peptides and a plethora of antibody inspired molecular designs. Here, we review a more generic approach: fragment-based drug discovery. Fragments allow novel areas of chemical space to be explored more efficiently, but the initial hits have low affinity. This means that they will not normally disrupt PPIs, unless they are tethered, an approach that has been pioneered by Wells and co-workers. An alternative fragment-based approach is to stabilise the uncomplexed components of the multiprotein system in solution and employ conventional fragment-based screening. Here, we describe the current knowledge of the structures and properties of protein-protein interactions and the small molecules that can modulate them. We then describe the use of sensitive biophysical methods - nuclear magnetic resonance, X-ray crystallography, surface plasmon resonance, differential scanning fluorimetry or isothermal calorimetry - to screen and validate fragment binding. Fragment hits can subsequently be evolved into larger molecules with higher affinity and potency. These may provide new leads for drug candidates that target protein-protein interactions and have therapeutic value.

Structural biology and drug discovery for protein-protein interactions

Although targeting protein-protein interfaces of regulatory multiprotein complexes has become a significant focus in drug discovery, it continues to pose major challenges. Most interfaces would be classed as 'undruggable' by conventional analyses, as they tend to be large, flat and featureless. Over the past decade, encouragement has come from the discovery of hotspots that contribute much of the free energy of interaction, and this has led to the development of tethering methods that target small molecules to these sites, often inducing adaptive changes. Equally important has been the recognition that many protein-protein interactions involve a continuous epitope of one partner and a well-defined groove or series of specific small pockets. These observations have stimulated the development of stapled α-helical peptides and other proteomimetic approaches. They have also led to the realisation that fragments might gain low-affinity 'footholds' on some protein-protein interfaces, and that these fragments might be elaborated to useful modulators of the interactions.