Augmented base pairing networks encode RNA-small molecule binding preferences

AbstractMotivationThe binding of small molecules to RNAs is an important mechanism which can stabilize 3D structures or activate key molecular functions. To date, computational and experimental efforts toward small molecule binding prediction have primarily focused on protein targets. Considering that a very large portion of the genome is transcribed into non-coding RNAs but only few regions are translated into proteins, successful annotations of RNA elements targeted by small-molecule would likely uncover a vast repertoire of biological pathways and possibly lead to new therapeutic avenues.ResultsOur work is a first attempt at bringing machine learning approaches to the problem of RNA drug discovery. RNAmigos takes advantage of the unique structural properties of RNA to predict small molecule ligands for unseen binding sites. A key feature of our model is an efficient representation of binding sites as augmented base pairing networks (ABPNs) aimed at encoding important structural patterns. We subject our ligand predictions to two virtual screen settings and show that we are able to rank the known ligand on average in the 73rd percentile, showing a significant improvement over several baselines. Furthermore, we observe that graphs which are augmented with non-Watson Crick (a.k.a non-canonical) base pairs are the only representation which is able to retrieve a significant signal, suggesting that non-canonical interactions are an necessary source of binding specificity in RNAs. We also find that an auxiliary graph representation task significantly boosts performance by providing efficient structural embeddings to the low data setting of ligand prediction. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights which can be applied to other structure-function learning tasks.AvailabilityCode and data is freely available at http://csb.cs.mcgill.ca/[email protected]

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Augmented base pairing networks encode RNA-small molecule binding preferences

Nucleic Acids Research ◽

10.1093/nar/gkaa583 ◽

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.

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Crystallographic Analysis of TPP Riboswitch Binding by Small-Molecule Ligands Discovered Through Fragment-Based Drug Discovery Approaches

Methods in Enzymology - Riboswitch Discovery, Structure and Function ◽

10.1016/b978-0-12-801122-5.00010-6 ◽

2014 ◽

pp. 221-233 ◽

Cited By ~ 1

Author(s):

Katherine Deigan Warner ◽

Adrian R. Ferré-D’Amaré

Keyword(s):

Drug Discovery ◽

Small Molecule ◽

Crystallographic Analysis ◽

Small Molecule Ligands ◽

Fragment Based Drug Discovery

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DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

10.26434/chemrxiv.13303187.v2 ◽

2021 ◽

Author(s):

Illimar Hugo Rekand ◽

Ruth Brenk

Keyword(s):

Drug Discovery ◽

Conformational Changes ◽

Binding Sites ◽

Rna Binding ◽

Data Set ◽

Protein Binding Sites ◽

Rna Targets ◽

Conventional Drug ◽

Protein Pockets ◽

Rna Binding Sites

RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. In addition, the majority of drug-like ligands contained in a data set of RNA pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets.

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Berichtigung: Discovery of Small Molecule Ligands for MALAT1 by Tuning an RNA‐Binding Scaffold

Angewandte Chemie ◽

10.1002/ange.201900875 ◽

2019 ◽

Vol 131 (17) ◽

pp. 5537-5537

Author(s):

Anita Donlic ◽

Brittany S. Morgan ◽

Jason L. Xu ◽

Anqi Liu ◽

Carlos Roble ◽

...

Keyword(s):

Small Molecule ◽

Rna Binding ◽

Small Molecule Ligands

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Faculty Opinions recommendation of Structure-Based Macrocycle Design in Small-Molecule Drug Discovery and Simple Metrics To Identify Opportunities for Macrocyclization of Small-Molecule Ligands.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.735307890.793558159 ◽

2019 ◽

Author(s):

John Lowe

Keyword(s):

Drug Discovery ◽

Small Molecule ◽

Small Molecule Drug ◽

Small Molecule Ligands

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Structure-Based DNA-Targeting Strategies with Small Molecule Ligands for Drug Discovery

Medicinal Research Reviews ◽

10.1002/med.21278 ◽

2013 ◽

Vol 33 (5) ◽

pp. 1119-1173 ◽

Cited By ~ 48

Author(s):

Jia Sheng ◽

Jianhua Gan ◽

Zhen Huang

Keyword(s):

Drug Discovery ◽

Small Molecule ◽

Dna Targeting ◽

Small Molecule Ligands

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DrugPred_RNA – Structure-Based Druggability Predictions for RNA Binding Sites

10.26434/chemrxiv.13303187.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Illimar Hugo Rekand ◽

Ruth Brenk

Keyword(s):

Drug Discovery ◽

Conformational Changes ◽

Binding Sites ◽

Rna Binding ◽

Data Set ◽

Protein Binding Sites ◽

Rna Targets ◽

Conventional Drug ◽

Protein Pockets ◽

Rna Binding Sites

RNA is an emerging target for drug discovery.<sup> </sup>However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predicator DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both, RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered sensible predictions for selected RNA binding sites. Further, the majority of drug-like ligands contained in a data set of RNA-containing pockets were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational changes in the binding site and can contribute to direct drug discovery efforts for RNA targets

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Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization

BMC Bioinformatics ◽

10.1186/s12859-021-04430-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shitao Zhao ◽

Michiaki Hamada

Keyword(s):

Neural Network ◽

Deep Learning ◽

Binding Sites ◽

Prediction Accuracy ◽

Large Scale ◽

Rna Binding ◽

Binding Prediction ◽

Binding Preference ◽

The Impact

Abstract Background Protein-RNA interactions play key roles in many processes regulating gene expression. To understand the underlying binding preference, ultraviolet cross-linking and immunoprecipitation (CLIP)-based methods have been used to identify the binding sites for hundreds of RNA-binding proteins (RBPs) in vivo. Using these large-scale experimental data to infer RNA binding preference and predict missing binding sites has become a great challenge. Some existing deep-learning models have demonstrated high prediction accuracy for individual RBPs. However, it remains difficult to avoid significant bias due to the experimental protocol. The DeepRiPe method was recently developed to solve this problem via introducing multi-task or multi-label learning into this field. However, this method has not reached an ideal level of prediction power due to the weak neural network architecture. Results Compared to the DeepRiPe approach, our Multi-resBind method demonstrated substantial improvements using the same large-scale PAR-CLIP dataset with respect to an increase in the area under the receiver operating characteristic curve and average precision. We conducted extensive experiments to evaluate the impact of various types of input data on the final prediction accuracy. The same approach was used to evaluate the effect of loss functions. Finally, a modified integrated gradient was employed to generate attribution maps. The patterns disentangled from relative contributions according to context offer biological insights into the underlying mechanism of protein-RNA interactions. Conclusions Here, we propose Multi-resBind as a new multi-label deep-learning approach to infer protein-RNA binding preferences and predict novel interactions. The results clearly demonstrate that Multi-resBind is a promising tool to predict unknown binding sites in vivo and gain biology insights into why the neural network makes a given prediction.

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