An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these find...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-10
Main Authors: Nassif, Houssam, Al-Ali, Hassan, Khuri, Sawsan, Keirouz, Walid, Page, David
Format: Article
Language:English
Subjects:
Amino acids
Binding sites
Biochemistry
Classifiers
Data banks
Dependence
Grooves
Hexoses
Logic programming
Machine learning
Mathematical models
Proteins
Sugar
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.
ISSN:2331-8422
DOI:10.48550/arxiv.1810.04707