BEES: Bayesian Ensemble Estimation from SAS

Samuel Bowerman; Joseph E. Curtis; Joseph Clayton; Emre H. Brookes; Jeff Wereszczynski

doi:10.1016/j.bpj.2019.06.024

BEES: Bayesian Ensemble Estimation from SAS

Samuel Bowerman, Joseph E. Curtis, Joseph Clayton, Emre H. Brookes, Jeff Wereszczynski

Chemistry and Biochemistry

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Many biomolecular complexes exist in a flexible ensemble of states in solution that is necessary to perform their biological function. Small-angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules because of their relative ease of use and their ability to simultaneously probe the full ensemble of states. However, SAS data is typically low dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries a significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the Bayesian Ensemble Estimation from SAS (BEES) program. Two forks of BEES are available, the primary one existing as a module for the SASSIE web server and a developmental version that is a stand-alone Python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES’ capabilities.

Original language	English
Pages (from-to)	399-407
Number of pages	9
Journal	Biophysical Journal
Volume	117
Issue number	3
DOIs	https://doi.org/10.1016/j.bpj.2019.06.024
State	Published - Aug 6 2019

Funding

The authors would like to thank Dr. Susan Krueger for valuable discussions in designing the plotting interface. E.H.B.’s work is supported by National Science Foundation grant number OAC-1740097 and NIH grant GM120600. S.B. J.C. and J.W. are supported by the National Institute of General Medical Sciences of the NIH under award number R35GM119647. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work benefited from CCP-SAS software developed through a joint Engineering and Physical Sciences Research Council (EP/K039121/1) and National Science Foundation (CHE-1265821) grant, as well as interactions and data collection at the Biophysics Collaborative Access Team, which is supported by National Institute of General Medical Sciences grant P41GM103622. This work used the Extreme Science and Engineering Discovery Environment, which is supported by National Science Foundation grant number ACI-1548562 (48). Certain commercial equipment, instruments, or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose. E.H.B.’s work is supported by National Science Foundation grant number OAC-1740097 and NIH grant GM120600 . S.B., J.C., and J.W. are supported by the National Institute of General Medical Sciences of the NIH under award number R35GM119647 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work benefited from CCP-SAS software developed through a joint Engineering and Physical Sciences Research Council ( EP/K039121/1 ) and National Science Foundation ( CHE-1265821 ) grant, as well as interactions and data collection at the Biophysics Collaborative Access Team, which is supported by National Institute of General Medical Sciences grant P41GM103622 . This work used the Extreme Science and Engineering Discovery Environment, which is supported by National Science Foundation grant number ACI-1548562 ( 48 ). Certain commercial equipment, instruments, or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

Funders	Funder number
OAC-1740097
GM120600
R35GM119647, P41GM103622
National Institute of Standards and Technology
Engineering and Physical Sciences Research Council	CHE-1265821, EP/K039121/1, ACI-1548562 ( 48

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10.1016/j.bpj.2019.06.024

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title = "BEES: Bayesian Ensemble Estimation from SAS",

abstract = "Many biomolecular complexes exist in a flexible ensemble of states in solution that is necessary to perform their biological function. Small-angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules because of their relative ease of use and their ability to simultaneously probe the full ensemble of states. However, SAS data is typically low dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries a significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the Bayesian Ensemble Estimation from SAS (BEES) program. Two forks of BEES are available, the primary one existing as a module for the SASSIE web server and a developmental version that is a stand-alone Python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES{\textquoteright} capabilities.",

author = "Samuel Bowerman and Curtis, \{Joseph E.\} and Joseph Clayton and Brookes, \{Emre H.\} and Jeff Wereszczynski",

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year = "2019",

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doi = "10.1016/j.bpj.2019.06.024",

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T2 - Bayesian Ensemble Estimation from SAS

AU - Bowerman, Samuel

AU - Curtis, Joseph E.

AU - Clayton, Joseph

AU - Brookes, Emre H.

AU - Wereszczynski, Jeff

PY - 2019/8/6

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N2 - Many biomolecular complexes exist in a flexible ensemble of states in solution that is necessary to perform their biological function. Small-angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules because of their relative ease of use and their ability to simultaneously probe the full ensemble of states. However, SAS data is typically low dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries a significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the Bayesian Ensemble Estimation from SAS (BEES) program. Two forks of BEES are available, the primary one existing as a module for the SASSIE web server and a developmental version that is a stand-alone Python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES’ capabilities.

AB - Many biomolecular complexes exist in a flexible ensemble of states in solution that is necessary to perform their biological function. Small-angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules because of their relative ease of use and their ability to simultaneously probe the full ensemble of states. However, SAS data is typically low dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries a significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the Bayesian Ensemble Estimation from SAS (BEES) program. Two forks of BEES are available, the primary one existing as a module for the SASSIE web server and a developmental version that is a stand-alone Python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES’ capabilities.

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BEES: Bayesian Ensemble Estimation from SAS

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