%0 Journal Article %1 Tang2022-id %A Tang, Wai Shing %A da Silva, Gabriel Monteiro %A Kirveslahti, Henry %A Skeens, Erin %A Feng, Bibo %A Sudijono, Timothy %A Yang, Kevin K %A Mukherjee, Sayan %A Rubenstein, Brenda %A Crawford, Lorin %D 2022 %I Public Library of Science (PLoS) %J PLoS Comput. Biol. %K %N 5 %P e1010045 %T A topological data analytic approach for discovering biophysical signatures in protein dynamics %V 18 %X 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 a 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.

@article{Tang2022-id, 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 a 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.}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Tang, Wai Shing and da Silva, Gabriel Monteiro and Kirveslahti, Henry and Skeens, Erin and Feng, Bibo and Sudijono, Timothy and Yang, Kevin K and Mukherjee, Sayan and Rubenstein, Brenda and Crawford, Lorin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/21113da887956ca9affe0f8fa65d54234/scadsfct}, copyright = {http://creativecommons.org/licenses/by/4.0/}, interhash = {b1fa90a3850b59020ace42ab8621c834}, intrahash = {1113da887956ca9affe0f8fa65d54234}, journal = {PLoS Comput. Biol.}, keywords = {}, language = {en}, month = may, number = 5, pages = {e1010045}, publisher = {Public Library of Science (PLoS)}, timestamp = {2024-09-10T15:15:57.000+0200}, title = {A topological data analytic approach for discovering biophysical signatures in protein dynamics}, volume = 18, year = 2022 }