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On use of tertiary structure characters in hidden Markov models for protein fold prediction

Malik, A. J.; Puente-Lelievre, C.; Matzke, N.; Ascher, D. B.

2024-04-11 bioinformatics
10.1101/2024.04.08.588419 bioRxiv
Show abstract

While advances in protein structure prediction have opened up insights into arcane proteins, weak sequence homology makes functional characterisation challenging. To overcome this challenge, we use structure-based hidden Markov models of groupings in SCOP, CATH and ECOD to predict folds in proteins and thereby infer function. Conservation of structure and ability of hidden Markov models to detect remote signals make this a powerful resource for complete characterisation of arcane proteins.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.