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Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

Journal Article · · Bioinformatics
 [1];  [1];  [2];  [3];  [3]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Biosciences Dept.
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computer Science & Informatics Department
  3. Sandia National Lab. (SNL-CA), Livermore, CA (United States). Biosystems Research
+ Show Author Affiliations
Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1426948
Report Number(s):
SAND--2007-1648J; 526857
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 2 Vol. 24; ISSN 1367-4803
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently journal January 2015
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework journal June 2010
Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers journal September 2012
Drug target prediction using adverse event report systems: a pharmacogenomic approach journal September 2012
Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets journal June 2013
DINIES: drug–target interaction network inference engine based on supervised analysis journal May 2014
Efficient multi-task chemogenomics for drug specificity prediction posted_content January 2018
A retrosynthetic biology approach to metabolic pathway design for therapeutic production journal January 2011
Inferring protein domains associated with drug side effects based on drug-target interaction network journal December 2013
Scalable prediction of compound-protein interactions using minwise hashing journal January 2013
Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets journal January 2014
In silico prediction of potential chemical reactions mediated by human enzymes journal June 2018
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network journal March 2020
Network-based characterization of drug-protein interaction signatures with a space-efficient approach journal April 2019
Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner journal December 2015
Side effect profile similarities shared between antidepressants and immune-modulators reveal potential novel targets for treating major depressive disorders journal October 2016
Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features journal March 2010
Prediction of Chemical-Protein Interactions Network with Weighted Network-Based Inference Method journal July 2012
Learning a peptide-protein binding affinity predictor with kernel ridge regression text January 2012
DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit journal January 2015
Engineering antibiotic production and overcoming bacterial resistance journal June 2011
Chemoinformatics as a Theoretical Chemistry Discipline journal January 2011
Inferring Chemogenomic Features from Drug-Target Interaction Networks journal December 2013
Scalable Prediction of Compound‐protein Interaction on Compressed Molecular Fingerprints journal December 2019
Drug recommendation with minimal side effects based on direct and temporal symptoms journal October 2018
The continuous molecular fields approach to building 3D-QSAR models journal May 2013
Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian Hierarchical Approach journal January 2016
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey journal January 2018
SensiPath: computer-aided design of sensing-enabling metabolic pathways journal April 2016
Assignment of EC Numbers to Enzymatic Reactions with Reaction Difference Fingerprints journal December 2012
Efficient multi-task chemogenomics for drug specificity prediction journal October 2018

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