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ISLAND: in-silico proteins binding affinity prediction using sequence information
BACKGROUND: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming ex...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688004/ https://www.ncbi.nlm.nih.gov/pubmed/33292419 http://dx.doi.org/10.1186/s13040-020-00231-w |
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author | Abbasi, Wajid Arshad Yaseen, Adiba Hassan, Fahad Ul Andleeb, Saiqa Minhas, Fayyaz Ul Amir Afsar |
author_facet | Abbasi, Wajid Arshad Yaseen, Adiba Hassan, Fahad Ul Andleeb, Saiqa Minhas, Fayyaz Ul Amir Afsar |
author_sort | Abbasi, Wajid Arshad |
collection | PubMed |
description | BACKGROUND: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. METHOD: We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. RESULTS: We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. CONCLUSION: This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem. |
format | Online Article Text |
id | pubmed-7688004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76880042020-11-30 ISLAND: in-silico proteins binding affinity prediction using sequence information Abbasi, Wajid Arshad Yaseen, Adiba Hassan, Fahad Ul Andleeb, Saiqa Minhas, Fayyaz Ul Amir Afsar BioData Min Research BACKGROUND: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. METHOD: We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. RESULTS: We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. CONCLUSION: This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem. BioMed Central 2020-11-25 /pmc/articles/PMC7688004/ /pubmed/33292419 http://dx.doi.org/10.1186/s13040-020-00231-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Abbasi, Wajid Arshad Yaseen, Adiba Hassan, Fahad Ul Andleeb, Saiqa Minhas, Fayyaz Ul Amir Afsar ISLAND: in-silico proteins binding affinity prediction using sequence information |
title | ISLAND: in-silico proteins binding affinity prediction using sequence information |
title_full | ISLAND: in-silico proteins binding affinity prediction using sequence information |
title_fullStr | ISLAND: in-silico proteins binding affinity prediction using sequence information |
title_full_unstemmed | ISLAND: in-silico proteins binding affinity prediction using sequence information |
title_short | ISLAND: in-silico proteins binding affinity prediction using sequence information |
title_sort | island: in-silico proteins binding affinity prediction using sequence information |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688004/ https://www.ncbi.nlm.nih.gov/pubmed/33292419 http://dx.doi.org/10.1186/s13040-020-00231-w |
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