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Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection
The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843838/ https://www.ncbi.nlm.nih.gov/pubmed/31615116 http://dx.doi.org/10.3390/biom9100607 |
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author | Akhter, Nasrin Chennupati, Gopinath Kabir, Kazi Lutful Djidjev, Hristo Shehu, Amarda |
author_facet | Akhter, Nasrin Chennupati, Gopinath Kabir, Kazi Lutful Djidjev, Hristo Shehu, Amarda |
author_sort | Akhter, Nasrin |
collection | PubMed |
description | The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function. |
format | Online Article Text |
id | pubmed-6843838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68438382019-11-25 Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection Akhter, Nasrin Chennupati, Gopinath Kabir, Kazi Lutful Djidjev, Hristo Shehu, Amarda Biomolecules Article The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function. MDPI 2019-10-14 /pmc/articles/PMC6843838/ /pubmed/31615116 http://dx.doi.org/10.3390/biom9100607 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Akhter, Nasrin Chennupati, Gopinath Kabir, Kazi Lutful Djidjev, Hristo Shehu, Amarda Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection |
title | Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection |
title_full | Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection |
title_fullStr | Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection |
title_full_unstemmed | Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection |
title_short | Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection |
title_sort | unsupervised and supervised learning over the energy landscape for protein decoy selection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843838/ https://www.ncbi.nlm.nih.gov/pubmed/31615116 http://dx.doi.org/10.3390/biom9100607 |
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