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On the mechanisms of protein interactions : predicting their affinity from unbound tertiary structures
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Marín-López, Manuel Alejandro, Planas-Iglesias, Joan, Aguirre-Plans, Joaquim, Bonet, Jaume, Garcia-Garcia, Javier, Fernandez-Fuentes, Narcis and Oliva, Baldo (2018) On the mechanisms of protein interactions : predicting their affinity from unbound tertiary structures. Bioinformatics, 34 (4). pp. 592-598. doi:10.1093/bioinformatics/btx616 ISSN 1367-4803.
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WRAP- mechanisms-protein-interactions-predicting-affinity-unbound-tertiary-structures-Planas-Iglesias-2017.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (706Kb) | Preview |
Official URL: http://dx.doi.org/10.1093/bioinformatics/btx616
Abstract
Motivation:
The characterization of the protein–protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein–protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures.
Results:
We present a new approach that relies on the unbound protein structures and protein docking to predict protein–protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol.
Availability and implementation:
The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > QH Natural history > QH301 Biology Q Science > QP Physiology |
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| Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Protein binding, Cytology | |||||||||||||||
| Journal or Publication Title: | Bioinformatics | |||||||||||||||
| Publisher: | Oxford University Press | |||||||||||||||
| ISSN: | 1367-4803 | |||||||||||||||
| Official Date: | 15 February 2018 | |||||||||||||||
| Dates: |
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| Volume: | 34 | |||||||||||||||
| Number: | 4 | |||||||||||||||
| Page Range: | pp. 592-598 | |||||||||||||||
| DOI: | 10.1093/bioinformatics/btx616 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 24 April 2018 | |||||||||||||||
| Date of first compliant Open Access: | 24 April 2018 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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