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Learning protein binding affinity using privileged information
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Abbasi, Wajid Arshad, Asif, Amina, Ben-Hur, Asa and Minhas, Fayyaz ul Amir Afsar (2018) Learning protein binding affinity using privileged information. BMC Bioinformatics, 19 (1). 425. doi:10.1186/s12859-018-2448-z ISSN 1471-2105.
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Official URL: http://dx.doi.org/10.1186/s12859-018-2448-z
Abstract
Background
Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.
Results
In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well.
Conclusions
The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history Q Science > QP Physiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Protein-protein interactions -- Research, Protein binding -- Research, Machine learning, Bioinformatics -- Research | ||||||
Journal or Publication Title: | BMC Bioinformatics | ||||||
Publisher: | BioMed Central Ltd. | ||||||
ISSN: | 1471-2105 | ||||||
Official Date: | 15 November 2018 | ||||||
Dates: |
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Volume: | 19 | ||||||
Number: | 1 | ||||||
Article Number: | 425 | ||||||
DOI: | 10.1186/s12859-018-2448-z | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 5 November 2019 | ||||||
Date of first compliant Open Access: | 5 November 2019 | ||||||
RIOXX Funder/Project Grant: |
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