Protein structure tokenization converts 3D structures into discrete or vectorized representations, enabling the integration of structural and sequence data. Despite many recent works on structure tokenization, the properties of the underlying discrete representations are not well understood. In this work, we first demonstrate that the successful utilization of structural tokens in a language model for structure prediction depends on using rich, pre-trained sequence embeddings to bridge the semantic gap between the sequence and structural "language". The analysis of the structural vocabulary itself then reveals significant semantic redundancy, where multiple distinct tokens correspond to nearly identical local geometries, acting as "structural synonyms". This redundancy, rather than being a flaw, can be exploited with a simple "synonym swap" strategy to generate diverse conformational ensembles by perturbing a predicted structure with its structural synonyms. This computationally lightweight method accurately recapitulates protein flexibility, performing competitively with state-of-the-art models. Our study provides fundamental insights into the nature of discrete protein structure representations and introduces a powerful, near-instantaneous method for modeling protein dynamics. Source code is available in https://github.com/IDEA-XL/TokenMD.

Protecting the anonymity of authors has become a difficult task given the rise of automated authorship attributors. These attributors are capable of attributing the author of a text amongst a pool of authors with great accuracy. In order to counter the rise of these automated attributors, there has also been a rise of automated obfuscators. These obfuscators are capable of taking some text, perturbing the text in some manner, and, if successful, deceive an automated attributor in misattributing the wrong author. We devised three novel authorship obfuscation methods that utilized a Psycho-linguistic theory known as Uniform Information Density (UID) theory. This theory states that humans evenly distribute information amongst speech or text so as to maximize efficiency. Utilizing this theory in our three obfuscation methods, we attempted to see how successfully we could deceive two separate attributors. Obfuscating 50 human and 50 GPT-3 generated articles from the TuringBench dataset, we observed how well each method did on deceiving the attributors. While the quality of the obfuscation in terms of semantic preservation and sensical changes was high, we were not able to find any evidence to indicate UID was a viable guiding metric for obfuscation. However, due to restrictions in time we were unable to test a large enough sample of article or tune the parameters for our attributors to comment conclusively on UID in obfuscation.