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Background Insight into the rigidity or flexibility of molecular structures is integral for a series of common research questions in molecular biology, including the identification of functional regions, simulated protein unfolding, or tracking and prediction of conformational changes in proteins over time. Determining rigidity in 3-dimensional space is a difficult problem in general. For a well-defined subclass of frameworks, however, this task can be solved in polynomial time with the help of constraint counting algorithms known as pebble games. Although this approach is well established, no easy-to-use implementation of the pebble game algorithm in the context of general graph analysis and molecular rigidity is currently available to researchers. Results To close this gap, we developed TRAMbio , a Python-based software tool for Topological Rigidity Analysis in Molecular Biology. We summarize and discuss the theoretical foundation of the pebble game and how it can be applied to molecular rigidity. Conclusions TRAMbio performs well even on large molecules and on discrete time series of protein movement. Results are accessible for both bioinformaticians and biologists, as rigid components can be rendered using standard molecular visualization platforms.

We present the Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we collected 9324 arguments from 6 diverse sources, covering religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms. Each argument was annotated by 3 crowdworkers for 54 values. The Touché23-ValueEval dataset extends the Webis-ArgValues-22. In comparison to the previous dataset, the effectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERT model increases. Therefore, though the classification difficulty increased as per the label distribution, the larger dataset allows for training better models.