Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

O’Meara, Matthew J.; Ballouz, Sara; Shoichet, Brian K.; Gillis, Jesse

doi:10.1371/journal.pone.0160098

Cited by 13 publications

(15 citation statements)

References 58 publications

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“…32,33 Besides the five muscarinic GPCR subtypes, acetylcholine also activates ligand-gated ion channels as primary targets. These nicotinic acetylcholine receptors (nAChRs) are widely expressed throughout the central and peripheral nervous system and at the neuromuscular junction.…”

Section: Resultsmentioning

confidence: 99%

Structure-Based Design and Discovery of New M₂ Receptor Agonists

et al. 2017

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Muscarinic receptor agonists are characterized by apparently strict restraints on their tertiary or quaternary amine and their distance to an ester or related center. On the basis of the active state crystal structure of the muscarinic M2 receptor in complex with iperoxo, we explored potential agonists that lacked the highly conserved functionalities of previously known ligands. Using structure-guided pharmacophore design followed by docking, we found two agonists (compounds 3 and 17), out of 19 docked and synthesized compounds, that fit the receptor well and were predicted to form a hydrogen-bond conserved among known agonists. Structural optimization led to compound 28, which was 4-fold more potent than its parent 3. Fortified by the discovery of this new scaffold, we sought a broader range of chemotypes by docking 2.2 million fragments, which revealed another three micromolar agonists unrelated either to 28 or known muscarinics. Even pockets as tightly defined and as deeply studied as that of the muscarinic reveal opportunities for the structure-based design and the discovery of new chemotypes.

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Section: Resultsmentioning

confidence: 99%

Structure-Based Design and Discovery of New M₂ Receptor Agonists

et al. 2017

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“…A large percentage of proteins remains untargeted, either due to bias in the drug discovery field for a select group of proteins or due to their undruggability, and this untapped pool of proteins has gained interest with protein deorphanizing efforts ( Edwards et al ., 2011 ; Fedorov et al ., 2010 ; O’Meara et al ., 2016 ). As future work, we will focus on building an effective representation for protein sequences.…”

Section: Discussionmentioning

confidence: 99%

DeepDTA: deep drug–target binding affinity prediction

2018

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MotivationThe identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).ResultsThe results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.Availability and implementation https://github.com/hkmztrk/DeepDTA Supplementary information Supplementary data are available at Bioinformatics online.

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“…Ligand-centric protein representation is only available for proteins with at least one known ligand interaction, while a sequence-based approach can miss key functional/mechanistic properties of the protein. The orthogonal information that can be obtained from the two approaches has been previously recognized ( O’meara et al , 2016 ). As future work, we will investigate combining both sequence and ligand information in protein representation.…”

Section: Discussionmentioning

confidence: 99%

A novel methodology on distributed representations of proteins using their interacting ligands

2018

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MotivationThe effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand-based approach can be utilized in protein representation. In this study, we propose SMILESVec, a Simplified molecular input line entry system (SMILES)-based method to represent ligands and a novel method to compute similarity of proteins by describing them based on their ligands. The proteins are defined utilizing the word-embeddings of the SMILES strings of their ligands. The performance of the proposed protein description method is evaluated in protein clustering task using TransClust and MCL algorithms. Two other protein representation methods that utilize protein sequence, Basic local alignment tool and ProtVec, and two compound fingerprint-based protein representation methods are compared.ResultsWe showed that ligand-based protein representation, which uses only SMILES strings of the ligands that proteins bind to, performs as well as protein sequence-based representation methods in protein clustering. The results suggest that ligand-based protein description can be an alternative to the traditional sequence or structure-based representation of proteins and this novel approach can be applied to different bioinformatics problems such as prediction of new protein–ligand interactions and protein function annotation.Availability and implementation https://github.com/hkmztrk/SMILESVecProteinRepresentation Supplementary information Supplementary data are available at Bioinformatics online.

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Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

Cited by 13 publications

References 58 publications

Structure-Based Design and Discovery of New M₂ Receptor Agonists

Structure-Based Design and Discovery of New M₂ Receptor Agonists

DeepDTA: deep drug–target binding affinity prediction

A novel methodology on distributed representations of proteins using their interacting ligands

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Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

Cited by 13 publications

References 58 publications

Structure-Based Design and Discovery of New M2 Receptor Agonists

Structure-Based Design and Discovery of New M2 Receptor Agonists

DeepDTA: deep drug–target binding affinity prediction

A novel methodology on distributed representations of proteins using their interacting ligands

Contact Info

Product

Resources

About

Structure-Based Design and Discovery of New M₂ Receptor Agonists

Structure-Based Design and Discovery of New M₂ Receptor Agonists