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A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics

View ORCID ProfileWai Shing Tang, Gabriel Monteiro da Silva, View ORCID ProfileHenry Kirveslahti, View ORCID ProfileErin Skeens, Bibo Feng, View ORCID ProfileTimothy Sudijono, View ORCID ProfileKevin K. Yang, View ORCID ProfileSayan Mukherjee, View ORCID ProfileBrenda Rubenstein, View ORCID ProfileLorin Crawford
doi: https://doi.org/10.1101/2021.07.28.454240
Wai Shing Tang
1Department of Physics, Brown University, Providence, RI, USA
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Gabriel Monteiro da Silva
2Department of Molecular and Cell Biology, Brown University, Providence, RI, USA
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Henry Kirveslahti
3Department of Statistical Science, Duke University, Durham, NC, USA
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Erin Skeens
2Department of Molecular and Cell Biology, Brown University, Providence, RI, USA
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Bibo Feng
4Department of Chemistry, Brown University, Providence, RI, USA
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Timothy Sudijono
5Department of Statistics, Stanford University, Palo Alto, CA, USA
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Kevin K. Yang
6Microsoft Research New England, Cambridge, MA, USA
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Sayan Mukherjee
3Department of Statistical Science, Duke University, Durham, NC, USA
7Department of Computer Science, Duke University, Durham, NC, USA
8Department of Mathematics, Duke University, Durham, NC, USA
9Department of Bioinformatics & Biostatistics, Duke University, Durham, NC, USA
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  • ORCID record for Sayan Mukherjee
Brenda Rubenstein
4Department of Chemistry, Brown University, Providence, RI, USA
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Lorin Crawford
6Microsoft Research New England, Cambridge, MA, USA
10Department of Biostatistics, Brown University, Providence, RI, USA
11Center for Computational Molecular Biology, Brown University, Providence, RI, USA
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  • For correspondence: lcrawford@microsoft.com
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Abstract

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto an user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.

Significance Structural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • # Jointly Supervised This Work

  • https://github.com/lcrawlab/SINATRA-Pro

  • https://github.com/lcrawlab/SINATRA_Pro_Paper_Results

  • https://www.dropbox.com/sh/l4fj3paagyrpu2f/AAA65_NbNaX5IUllrazScZo9a?dl=0

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics
Wai Shing Tang, Gabriel Monteiro da Silva, Henry Kirveslahti, Erin Skeens, Bibo Feng, Timothy Sudijono, Kevin K. Yang, Sayan Mukherjee, Brenda Rubenstein, Lorin Crawford
bioRxiv 2021.07.28.454240; doi: https://doi.org/10.1101/2021.07.28.454240
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A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics
Wai Shing Tang, Gabriel Monteiro da Silva, Henry Kirveslahti, Erin Skeens, Bibo Feng, Timothy Sudijono, Kevin K. Yang, Sayan Mukherjee, Brenda Rubenstein, Lorin Crawford
bioRxiv 2021.07.28.454240; doi: https://doi.org/10.1101/2021.07.28.454240

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