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Lmcrot: An Enhanced Protein Crotonylation Site Predictor By Leveraging An Interpretable Window-Level Embedding From A Transformer-Based Protein Language Model, Pawel Pratyush, Soufia Bahmani, Suresh Pokharel, Hamid D. Ismail, Dukka Bahadur

Michigan Tech Publications, Part 2

MOTIVATION: Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from Protein Language Models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted. RESULTS: Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed …