Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions.

Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.Work in DRS’s laboratory is supported by the the European Union, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Medical Research Council and Wellcome Trust. Work in ARV’s laboratory is supported by the Medical Research Council and Wellcome Trust. Work in DJH's laboratory is supported by the Medical Research Council under grant ML/L007266/1. All calculations were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/) provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and were funded by the EPSRC under grants EP/F032773/1 and EP/J017639/1. GJM and ARV are affiliated with PhoreMost Ltd, Cambridge. We thank Alicia Higueruelo and John Skidmore for helpful discussions.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.chembiol.2015.04.01

Solution structure of the Hop TPR2A domain and investigation of target druggability by NMR, biochemical and in silico approaches

Heat shock protein 90 (Hsp90) is a molecular chaperone that plays an important role in tumour biology by promoting the stabilisation and activity of oncogenic ‘client’ proteins. Inhibition of Hsp90 by small-molecule drugs, acting via its ATP hydrolysis site, has shown promise as a molecularly targeted cancer therapy. Owing to the importance of Hop and other tetratricopeptide repeat (TPR)-containing cochaperones in regulating Hsp90 activity, the Hsp90-TPR domain interface is an alternative site for inhibitors, which could result in effects distinct from ATP site binders. The TPR binding site of Hsp90 cochaperones includes a shallow, positively charged groove that poses a significant challenge for druggability. Herein, we report the apo, solution-state structure of Hop TPR2A which enables this target for NMR-based screening approaches. We have designed prototype TPR ligands that mimic key native ‘carboxylate clamp’ interactions between Hsp90 and its TPR cochaperones and show that they block binding between Hop TPR2A and the Hsp90 C-terminal MEEVD peptide. We confirm direct TPR-binding of these ligands by mapping 1H–15N HSQC chemical shift perturbations to our new NMR structure. Our work provides a novel structure, a thorough assessment of druggability and robust screening approaches that may offer a potential route, albeit difficult, to address the chemically challenging nature of the Hop TPR2A target, with relevance to other TPR domain interactors

Prioritisation of potential drug targets against bartonella bacilliformis by an integrative in-silico approach

BACKGROUND Carrion’s disease (CD) is a neglected biphasic illness caused by Bartonella bacilliformis, a Gram-negative bacteria found in the Andean valleys. The spread of resistant strains underlines the need for novel antimicrobials against B. bacilliformis and related bacterial pathogens. OBJECTIVE The main aim of this study was to integrate genomic-scale data to shortlist a set of proteins that could serve as attractive targets for new antimicrobial discovery to combat B. bacilliformis. METHODS We performed a multidimensional genomic scale analysis of potential and relevant targets which includes structural druggability, metabolic analysis and essentiality criteria to select proteins with attractive features for drug discovery. FINDINGS We shortlisted seventeen relevant proteins to develop new drugs against the causative agent of Carrion’s disease. Particularly, the protein products of fabI, folA, aroA, trmFO, uppP and murE genes, meet an important number of desirable features that make them attractive targets for new drug development. This data compendium is freely available as a web server (http://target.sbg.qb.fcen.uba.ar/). MAIN CONCLUSION This work represents an effort to reduce the costs in the first phases of B. bacilliformis drug discovery.Fil: Farfán López, Mariella. Universidad Nacional Mayor de San Marcos; PerúFil: Espinoza Culupú, Abraham. Universidad Nacional Mayor de San Marcos; PerúFil: García De la guarda, Ruth. Universidad Nacional Mayor de San Marcos; PerúFil: Serral, Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; ArgentinaFil: Sosa, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Palomino, Maria Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Fernández Do Porto, Darío Augusto. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentin

Targeting protein–protein interactions in hematologic malignancies: still a challenge or a great opportunity for future therapies?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110054/1/imr12244.pd

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds

Binding-Site Assessment by Virtual Fragment Screening

The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors

Protein-protein interactions: network analysis and applications in drug discovery

Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets

In silico druggability assessment of the NUDIX hydrolase protein family as a workflow for target prioritization

Computational chemistry has now been widely accepted as a useful tool for shortening lead times in early drug discovery. When selecting new potential drug targets, it is important to assess the likelihood of finding suitable starting points for lead generation before pursuing costly high-throughput screening campaigns. By exploiting available high-resolution crystal structures, an in silico druggability assessment can facilitate the decision of whether, and in cases where several protein family members exist, which of these to pursue experimentally. Many of the algorithms and software suites commonly applied for in silico druggability assessment are complex, technically challenging and not always user-friendly. Here we applied the intuitive open access servers of DoGSite, FTMap and CryptoSite to comprehensively predict ligand binding pockets, druggability scores and conformationally active regions of the NUDIX protein family. In parallel we analyzed potential ligand binding sites, their druggability and pocket parameter using Schrödinger's SiteMap. Then an in silico docking cascade of a subset of the ZINC FragNow library using the Glide docking program was performed to assess identified pockets for large-scale small-molecule binding. Subsequently, this initial dual ranking of druggable sites within the NUDIX protein family was benchmarked against experimental hit rates obtained both in-house and by others from traditional biochemical and fragment screening campaigns. The observed correlation suggests that the presented user-friendly workflow of a dual parallel in silico druggability assessment is applicable as a standalone method for decision on target prioritization and exclusion in future screening campaigns

The Potential of Intrinsically Disordered Proteins as Drug Targets

Tumor necrosis factor α-induced protein 3-interacting protein 1 (TNIP1) is a negative regulator of inflammatory signaling in several diseases. TNIP1 is also an intrinsically disordered protein (IDP), which makes it difficult for current drugs to affect it. More research on IDPs could lead to novel drugs targeting TNIP1, leading to improved therapies for patients with acute and chronic inflammatory diseases. The main difference between IDPs and the more common ordered proteins is that IDPs are flexible, a characteristic of TNIP1 which was demonstrated in this study via protease sensitivity. Ordered proteins are rigid, which means that they only have one well-defined three-dimensional structure. The flexibility of IDPs allows them to have multiple conformations that they can switch between quite easily. However, switching between conformations makes it much harder to solve for the structure of an IDP. Since developing drugs relies heavily on knowing a protein’s structure, IDPs have not yet been common therapeutic targets. Several screening approaches for new IDP-targeting drugs are considered here, including those driven by artificial intelligence. There have been some reports of successful small molecule screens, but finding a universal technique is still in high demand. Currently, it is thought that drugs binding to multiple conformations of IDPs may be beneficial over a drug only binding a single conformation. Since 20-30% of the proteins in our body are IDPs, continued characterization of IDPs could lead to better drug designing methods, more structural information about TNIP1, and a better multifaceted approach for treating psoriasis, cancer, Parkinson’s disease, ischemic vascular diseases, and beyond

Integrating protein annotations for the in silico prioritization of putative drug target proteins in malaria

Current anti-malarial methods have been effective in reducing the number of malarial cases. However, these methods do not completely block the transmission of the parasite. Research has shown that repeated use of the current anti-malarial drugs, which include artemisinin-based drug combinations, might be toxic to humans. There have also been reports of an emergence of artemisinin-resistant parasites. Finding anti-malarial drugs through the drug discovery process takes a long time and failure results in a great financial loss. The failure of drug discovery projects can be partly attributed to the improper selection of drug targets. There is thus a need for an eff ective way of identifying and validating new potential malaria drug targets for entry into the drug discovery process. The availability of the genome sequences for the Plasmodium parasite, human host and the Anopheles mosquito vector has facilitated post-genomic studies on malaria. Proper utilizationof this data, in combination with computational biology and bioinformatics techniques, could aid in the in silico prioritization of drug targets. This study was aimed at extensively annotating the protein sequences from the Plasmodium parasites, H. sapiens and A. gambiae with data from di fferent online databases in order to create a resource for the prioritization of drug targets in malaria. Essentiality, assay feasibility, resistance, toxicity, structural information and druggability were the main target selection criteria which were used to collect data for protein annotations. The data was used to populate the Discovery resource (http://malport. bi.up.ac.za/) for the in silico prioritization of potential drug targets. A new version of the Discovery system, Discovery 2.0 (http://discovery.bi.up.ac.za/), has been developed using Java. The system contains new and automatically updated data as well as improved functionalities. The new data in Discovery 2.0 includes UniProt accessions, gene ontology annotations from the UniProt-GOA project, pathways from Reactome and Malaria Parasite Metabolic Pathways databases, protein-protein interactions data from. IntAct as well as druggability data from the DrugEBIlity resource hosted by ChEMBL. Users can access the data by searching with a protein identi er, UniProt accession, protein name or through the advanced search which lets users filter protein sequences based on different protein properties. The results are organized in a tabbed environment, with each tab displaying different protein annotation data. A sample investigation using a previously proposed malarial target, S-adenosyl-Lhomocysteine hydrolase, was carried out to demonstrate the diff erent categories of data available in Discovery 2.0 as well as to test if the available data is su fficient for assessment and prioritization of drug targets. The study showed that using the annotation data in Discovery 2.0, a protein can be assessed, in a species comparative manner, on the potential of being a drug target based on the selection criteria mentioned here. However, supporting data from literature is also needed to further validate the findings.Dissertation (MSc)--University of Pretoria, 2012.Biochemistryunrestricte