Potentials of mean force for protein structure prediction vindicated, formalized and generalized.
Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances--so-called "potentials of mean force" (PMFs)--have been center stage in the prediction and design of protein struct...
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2010
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oai:doaj.org-article:45f90bfcdbad41b88281a3c31ce063c12021-11-18T07:36:58ZPotentials of mean force for protein structure prediction vindicated, formalized and generalized.1932-620310.1371/journal.pone.0013714https://doaj.org/article/45f90bfcdbad41b88281a3c31ce063c12010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21103041/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances--so-called "potentials of mean force" (PMFs)--have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state--a necessary component of these potentials--is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities "reference ratio distributions" deriving from the application of the "reference ratio method." This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.Thomas HamelryckMikael BorgMartin PaluszewskiJonas PaulsenJes FrellsenChristian AndreettaWouter BoomsmaSandro BottaroJesper Ferkinghoff-BorgPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 11, p e13714 (2010) |
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Medicine R Science Q |
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Medicine R Science Q Thomas Hamelryck Mikael Borg Martin Paluszewski Jonas Paulsen Jes Frellsen Christian Andreetta Wouter Boomsma Sandro Bottaro Jesper Ferkinghoff-Borg Potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| description |
Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances--so-called "potentials of mean force" (PMFs)--have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state--a necessary component of these potentials--is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities "reference ratio distributions" deriving from the application of the "reference ratio method." This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures. |
| format |
article |
| author |
Thomas Hamelryck Mikael Borg Martin Paluszewski Jonas Paulsen Jes Frellsen Christian Andreetta Wouter Boomsma Sandro Bottaro Jesper Ferkinghoff-Borg |
| author_facet |
Thomas Hamelryck Mikael Borg Martin Paluszewski Jonas Paulsen Jes Frellsen Christian Andreetta Wouter Boomsma Sandro Bottaro Jesper Ferkinghoff-Borg |
| author_sort |
Thomas Hamelryck |
| title |
Potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| title_short |
Potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| title_full |
Potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| title_fullStr |
Potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| title_full_unstemmed |
Potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| title_sort |
potentials of mean force for protein structure prediction vindicated, formalized and generalized. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2010 |
| url |
https://doaj.org/article/45f90bfcdbad41b88281a3c31ce063c1 |
| work_keys_str_mv |
AT thomashamelryck potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT mikaelborg potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT martinpaluszewski potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT jonaspaulsen potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT jesfrellsen potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT christianandreetta potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT wouterboomsma potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT sandrobottaro potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized AT jesperferkinghoffborg potentialsofmeanforceforproteinstructurepredictionvindicatedformalizedandgeneralized |
| _version_ |
1718423156207648768 |