scholarly journals Systematic analysis of the interactions driving small molecule–RNA recognition

2020 ◽  
Vol 11 (7) ◽  
pp. 802-813 ◽  
Author(s):  
G. Padroni ◽  
N. N. Patwardhan ◽  
M. Schapira ◽  
A. E. Hargrove
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Rna Binding ◽  
Rna Recognition ◽  
Systematic Analysis

This study underscores privileged interactions for RNA binding small molecules, an emerging focus in drug discovery.

scholarly journals Discovery and Optimization of Selective Inhibitors of Meprin α (Part II)

2020 ◽  
Author(s):  
Chao Wang ◽  
Juan Diez ◽  
Hajeung Park ◽  
Christoph Becker-Pauly ◽  
Gregg B. Fields ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Hydroxamic Acid ◽  
Therapeutic Potential ◽  
Preceding Paper ◽  
Close Relative ◽  
Specific Inhibition ◽  
Selective Inhibitors

Meprin α is a zinc metalloproteinase (metzincin) that has been implicated in multiple diseases, including fibrosis and cancers. It has proven difficult to find small molecules that are capable of selectively inhibiting meprin α, or its close relative meprin β, over numerous other metzincins which, if inhibited, would elicit unwanted effects. We recently identified possible molecular starting points for meprin α-specific inhibition through an HTS effort (see part I, preceding paper). In part II we report the optimization of a potent and selective hydroxamic acid meprin α inhibitor probe which may help define the therapeutic potential for small molecule meprin α inhibition and spur further drug discovery efforts in the area of zinc metalloproteinase inhibition.

scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

In depth analysis of kinase cross screening data to identify chemical starting points for inhibition of the nek family of kinases

2017 ◽  
Author(s):  
Carrow I. Wells ◽  
Nirav R. Kapadia ◽  
Rafael M. Couñago ◽  
David H. Drewry
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Kinase Inhibitors ◽  
Biological Functions ◽  
Chemical Probes ◽  
High Quality ◽  
Depth Analysis ◽  
Number Of Citations ◽  
Shed Light

AbstractPotent, selective, and cell active small molecule kinase inhibitors are useful tools to help unravel the complexities of kinase signaling. As the biological functions of individual kinases become better understood, they can become targets of drug discovery efforts. The small molecules used to shed light on function can also then serve as chemical starting points in these drug discovery efforts. The Nek family of kinases has received very little attention, as judged by number of citations in PubMed, yet they appear to play many key roles and have been implicated in disease. Here we present our work to identify high quality chemical starting points that have emerged due to the increased incidence of broad kinome screening. We anticipate that this analysis will allow the community to progress towards the generation of chemical probes and eventually drugs that target members of the Nek family.

scholarly journals Augmented base pairing networks encode RNA-small molecule binding preferences

2020 ◽  
Vol 48 (14) ◽  
pp. 7690-7699
Author(s):  
Carlos Oliver ◽  
Vincent Mallet ◽  
Roman Sarrazin Gendron ◽  
Vladimir Reinharz ◽  
William L Hamilton ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Structure Function ◽  
Small Molecule ◽  
Rna Structure ◽  
Rna Binding ◽  
Binding Specificity ◽  
Representation Learning ◽  
Learning Task ◽  
Graph Representation ◽  
Base Pairing

Abstract RNA-small molecule binding is a key regulatory mechanism which can stabilize 3D structures and activate molecular functions. The discovery of RNA-targeting compounds is thus a current topic of interest for novel therapies. Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step towards understanding the interactions which drive binding specificity. Our tool, RNAmigos, builds and encodes a network representation of RNA structures to predict likely ligands for novel binding sites. We subject ligand predictions to virtual screening and show that we are able to place the true ligand in the 71st–73rd percentile in two decoy libraries, showing a significant improvement over several baselines, and a state of the art method. Furthermore, we observe that augmenting structural networks with non-canonical base pairing data is the only representation able to uncover a significant signal, suggesting that such interactions are a necessary source of binding specificity. We also find that pre-training with an auxiliary graph representation learning task significantly boosts performance of ligand prediction. This finding can serve as a general principle for RNA structure-function prediction when data is scarce. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights for possible applications to other structure-function learning tasks. The source code, data and a Web server are freely available at http://rnamigos.cs.mcgill.ca.

Niche-Based Screening Reveals Leukemia Stem Cell Specific Therapeutics

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 760-760
Author(s):  
Kimberly A. Hartwell ◽  
Peter G. Miller ◽  
Alison L. Stewart ◽  
Alissa R. Kahn ◽  
David J. Logan ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Stem Cell ◽  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
High Throughput ◽  
Hematopoietic Cells ◽  
Leukemia Stem Cells

Abstract Abstract 760 Recent insights into the molecular and cellular processes that drive leukemia have called attention to the limitations intrinsic to traditional drug discovery approaches. To date, the majority of cell-based functional screens have relied on probing cell lines in vitro in isolation to identify compounds that decrease cellular viability. The development of novel therapeutics with greater efficacy and decreased toxicity will require the identification of small molecules that selectively target leukemia stem cells (LSCs) within the context of their microenvironment, while sparing normal cells. We hypothesized that it would be possible to systematically identify LSC susceptibilities by modeling key elements of bone marrow niche interactions in high throughput format. We tested this hypothesis by creating and optimizing an assay in which primary murine stem cell-enriched leukemia cells are plated on bone marrow stromal cells in 384-well format, and examined by a high content image-based readout of cobblestoning, an in vitro morphological surrogate of cell health and self-renewal. AML cells cultured in this way maintained their ability to reinitiate disease in mice with as few as 100 cells. 14,720 small molecule probes across diverse chemical space were screened at 5uM in our assay. Retest screening was performed in the presence of two different bone marrow stromal types in parallel, OP9s and primary mesenchymal stem cells (MSCs). Greater than 60% of primary screen hits positively retested (dose response with IC50 at or below 5 μM) on both types of stroma. Compounds that inhibited leukemic cobblestoning merely by killing the stroma were identified by CellTiter-Glo viability analysis and excluded. Compounds that killed normal primary hematopoietic stem and progenitor cell inputs, as assessed by a related co-culture screen, were also excluded. Selectivity for leukemia over normal hematopoietic cells was additionally examined in vitro by comingling these cells on stroma within the same wells. Primary human CD34+ AML leukemia and normal CD34+ cord blood cells were also tested, by way of the 5 week cobblestone area forming cell (CAFC) assay. Additionally, preliminary studies of human AML cells pulse-treated with small molecules ex vivo, followed by in vivo transplantation, provided further evidence of potent leukemia kill across genotypes. A biologically complex functional approach to drug discovery, such as the novel method described here, has previously been thought impossible, due to presumed incompatibility with high throughput scale. We show that it is possible, and that it bears fruit in a first pilot screen. By these means, we discover small molecule perturbants that act selectively in the context of the microenvironment to kill LSCs while sparing stroma and normal hematopoietic cells. Some hits act cell autonomously, and some do not, as evidenced by observed leukemia kill when only the stromal support cells are treated prior to the plating of leukemia. Some hits are known, such as parthenolide and celastrol, and some are previously underappreciated, such as HMG-CoA reductase inhibition. Others are entirely new, and would not have been revealed by conventional approaches to therapeutic discovery. We therefore present a powerful new approach, and identify drug candidates with the potential to selectively target leukemia stem cells in clinical patients. Disclosures: No relevant conflicts of interest to declare.

Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins

2009 ◽  
Vol 27 (7) ◽  
pp. 667-670 ◽  
Author(s):  
Debashish Ray ◽  
Hilal Kazan ◽  
Esther T Chan ◽  
Lourdes Peña Castillo ◽  
Sidharth Chaudhry ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Recognition ◽  
Systematic Analysis

scholarly journals Discovery of RNA Binding Small Molecules Using Small Molecule Microarrays

2016 ◽  
pp. 157-175 ◽  
Author(s):  
Colleen M. Connelly ◽  
Fardokht A. Abulwerdi ◽  
John S. Schneekloth
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Rna Binding

scholarly journals Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery

2020 ◽  
Vol 21 (15) ◽  
pp. 5262 ◽  
Author(s):  
Qingxin Li ◽  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Structural Biology ◽  
Small Molecule ◽  
Molecular Interactions ◽  
Mechanisms Of Action ◽  
Rational Drug Design ◽  
Binding Modes ◽  
Small Molecule Drugs ◽  
Rational Drug

Small-molecule drugs are organic compounds affecting molecular pathways by targeting important proteins. These compounds have a low molecular weight, making them penetrate cells easily. Small-molecule drugs can be developed from leads derived from rational drug design or isolated from natural resources. A target-based drug discovery project usually includes target identification, target validation, hit identification, hit to lead and lead optimization. Understanding molecular interactions between small molecules and their targets is critical in drug discovery. Although many biophysical and biochemical methods are able to elucidate molecular interactions of small molecules with their targets, structural biology is the most powerful tool to determine the mechanisms of action for both targets and the developed compounds. Herein, we reviewed the application of structural biology to investigate binding modes of orthosteric and allosteric inhibitors. It is exemplified that structural biology provides a clear view of the binding modes of protease inhibitors and phosphatase inhibitors. We also demonstrate that structural biology provides insights into the function of a target and identifies a druggable site for rational drug design.

scholarly journals Optimized methods for IL-17A refolding and anti-IL17A Fab production for co-crystallization with small molecules

BioTechniques ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 70-76
Author(s):  
Xiaoyun Meng ◽  
Lanjun Zhang ◽  
Hong Wei ◽  
Furong Li ◽  
Lihua Hu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Yield Enhancement ◽  
High Affinity ◽  
Interleukin 17A ◽  
Affinity Peptide ◽  
First Time ◽  
Essential Prerequisite

Refolding of human interleukin 17A (IL-17A) has been reported; however, the key refolding protocol was not robust enough to deliver consistent results and to be easily scaled up for crystallization. Here we report an optimized refolding method for IL-17A. Although co-crystal structures of IL-17A with ligands have been obtained with a high-affinity peptide and an anti-IL-17A Fab as stabilizers, neither the production yield nor the characterization of the IL-17A/Fab complex was reported. To facilitate co-crystallization of IL-17A with small-molecule compounds derived from our DNA encoded library, we also describe the method for yield enhancement of anti-IL-17A Fab production and characterize the IL-17A/Fab complex for the first time, providing an essential prerequisite for structure-based drug discovery targeting IL-17A.

scholarly journals Target-Directed Approaches for Screening Small Molecules against RNA Targets

2020 ◽  
Vol 25 (8) ◽  
pp. 869-894 ◽  
Author(s):  
Hafeez S. Haniff ◽  
Laurent Knerr ◽  
Jonathan L. Chen ◽  
Matthew D. Disney ◽  
Helen L. Lightfoot
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Drug Targets ◽  
Industrial Development ◽  
Rna Binding ◽  
Cellular Functions ◽  
Rna Molecules ◽  
The Face ◽  
Rna Binders ◽  
The Right

RNA molecules have a variety of cellular functions that can drive disease pathologies. They are without a doubt one of the most intriguing yet controversial small-molecule drug targets. The ability to widely target RNA with small molecules could be revolutionary, once the right tools, assays, and targets are selected, thereby defining which biomolecules are targetable and what constitutes drug-like small molecules. Indeed, approaches developed over the past 5–10 years have changed the face of small molecule–RNA targeting by addressing historic concerns regarding affinity, selectivity, and structural dynamics. Presently, selective RNA–protein complex stabilizing drugs such as branaplam and risdiplam are in clinical trials for the modulation of SMN2 splicing, compounds identified from phenotypic screens with serendipitous outcomes. Fully developing RNA as a druggable target will require a target engagement-driven approach, and evolving chemical collections will be important for the industrial development of this class of target. In this review we discuss target-directed approaches that can be used to identify RNA-binding compounds and the chemical knowledge we have today of small-molecule RNA binders.

Sign in / Sign up

Export Citation Format

Share Document